From ce7a797a2e0e4a12cf9853786609fc64cb982d00 Mon Sep 17 00:00:00 2001 From: ljchang Date: Mon, 9 Jan 2023 19:07:15 -0500 Subject: [PATCH 01/22] Fixed bug in video_detect due to face threshold --- feat/detector.py | 33 ++++++++++++++++++---- feat/utils/image_operations.py | 50 +++++++++++++++++++++++++++++++++- 2 files changed, 77 insertions(+), 6 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index 86e38cd7..6bfb21c5 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -646,7 +646,7 @@ def detect_image( pin_memory=False, frame_counter=0, skip_failed_detections=False, - threshold=0.5, + threshold=0.9, **kwargs, ): """ @@ -756,9 +756,11 @@ def detect_video( video_path, skip_frames=None, output_size=700, + threshold=0.9, batch_size=1, num_workers=0, pin_memory=False, + skip_failed_detections=False, **detector_kwargs, ): """Detects FEX from a video file. @@ -788,7 +790,9 @@ def detect_video( batch_output = [] for batch_data in tqdm(data_loader): - faces = self.detect_faces(batch_data["Image"], **detector_kwargs) + faces = self.detect_faces( + batch_data["Image"], threshold=threshold, **detector_kwargs + ) landmarks = self.detect_landmarks( batch_data["Image"], detected_faces=faces, **detector_kwargs ) @@ -799,9 +803,28 @@ def detect_video( ) frames = list(batch_data["Frame"].numpy()) landmarks = _inverse_landmark_transform(landmarks, batch_data) - output = self._create_fex( - faces, landmarks, poses, aus, emotions, batch_data["FileName"], frames - ) + + try: + self._check_detections( + faces, landmarks, poses, aus, emotions, batch_data + ) + output = self._create_fex( + faces, + landmarks, + poses, + aus, + emotions, + batch_data["FileName"], + frames, + ) + batch_output.append(output) + except ValueError as e: + if skip_failed_detections: + print(e) + continue + else: + raise e + batch_output.append(output) batch_output = pd.concat(batch_output) diff --git a/feat/utils/image_operations.py b/feat/utils/image_operations.py index b2ce5c5e..4fbf327a 100644 --- a/feat/utils/image_operations.py +++ b/feat/utils/image_operations.py @@ -22,6 +22,7 @@ from copy import deepcopy from skimage import draw import logging +from matplotlib.patches import Rectangle __all__ = [ "neutral", @@ -468,7 +469,29 @@ def __init__( def __repr__(self): return f"'height': {self.height}, 'width': {self.width}" - + + def __mul__(self, bbox2): + '''Create a new BBox based on the intersection between two BBox instances (AND operation)''' + + if isinstance(bbox2, (BBox)): + return BBox([np.max([self.left, bbox2.left]), + np.max([self.top, bbox2.top]), + np.min([self.right, bbox2.right]), + np.min([self.bottom, bbox2.bottom])]) + else: + raise NotImplementedError('Multiplication is currently only supported between two BBox instances') + + def __add__(self, bbox2): + '''Create a new BBox based on the intersection between two BBox instances (OR Operation)''' + + if isinstance(bbox2, (BBox)): + return BBox([np.min([self.left, bbox2.left]), + np.min([self.top, bbox2.top]), + np.max([self.right, bbox2.right]), + np.max([self.bottom, bbox2.bottom])]) + else: + raise NotImplementedError('Addition is currently only supported between two BBox instances') + def expand_by_factor(self, factor, symmetric=True): """Expand box by factor @@ -624,6 +647,31 @@ def inverse_transform_landmark(self, landmark): y = point[1] * self.height + self.top landmark_[i] = (x, y) return landmark_ + + def area(self): + """Compute the area of the bounding box""" + return self.height * self.width + + def overlap(self, bbox2): + """Compute the percent overlap between BBox with another BBox""" + return (self * bbox2).area()/self.area() + + def plot(self, ax=None, fill=False, linewidth=2, **kwargs): + """Plot bounding box + + Args: + ax: matplotlib axis + fill (bool): fill rectangle + """ + + if ax is None: + fig, ax = plt.subplots() + ax.plot() + + ax.add_patch(Rectangle((self.left, self.top), + self.width, self.height, + fill=fill, linewidth=linewidth, **kwargs)) + return ax def reverse_color_order(img): From 9aaa6452b59cd77b10aa1de1269b8678bcb004dc Mon Sep 17 00:00:00 2001 From: ljchang Date: Tue, 17 Jan 2023 21:58:52 -0500 Subject: [PATCH 02/22] fixed bug when no faces are detected --- feat/detector.py | 343 +++++++++++++++++++-------------- feat/utils/image_operations.py | 78 +++++--- 2 files changed, 247 insertions(+), 174 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index 6bfb21c5..f50b7bd3 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -38,6 +38,7 @@ import warnings from tqdm import tqdm import torchvision.transforms as transforms +from collections import ChainMap # Supress sklearn warning about pickled estimators and diff sklearn versions warnings.filterwarnings("ignore", category=UserWarning, module="sklearn") @@ -417,51 +418,54 @@ def detect_landmarks(self, frame, detected_faces, **landmark_model_kwargs): logging.info("detecting landmarks...") frame = convert_image_to_tensor(frame) - if self.info["landmark_model"]: - if self.info["landmark_model"].lower() == "mobilenet": - out_size = 224 - else: - out_size = 112 + if _is_list_of_lists_empty(detected_faces): + list_concat = detected_faces + else: + if self.info["landmark_model"]: + if self.info["landmark_model"].lower() == "mobilenet": + out_size = 224 + else: + out_size = 112 - extracted_faces, new_bbox = extract_face_from_bbox( - frame, detected_faces, face_size=out_size - ) + extracted_faces, new_bbox = extract_face_from_bbox( + frame, detected_faces, face_size=out_size + ) - extracted_faces = extracted_faces / 255.0 + extracted_faces = extracted_faces / 255.0 - if self.info["landmark_model"].lower() == "mobilenet": - extracted_faces = Compose( - [Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])] - )(extracted_faces) + if self.info["landmark_model"].lower() == "mobilenet": + extracted_faces = Compose( + [Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])] + )(extracted_faces) + + # Run Landmark Model + if self.info["landmark_model"].lower() == "mobilefacenet": + landmark = ( + self.landmark_detector(extracted_faces, **landmark_model_kwargs)[0] + .cpu() + .data.numpy() + ) + else: + landmark = ( + self.landmark_detector(extracted_faces, **landmark_model_kwargs) + .cpu() + .data.numpy() + ) - # Run Landmark Model - if self.info["landmark_model"].lower() == "mobilefacenet": - landmark = ( - self.landmark_detector(extracted_faces, **landmark_model_kwargs)[0] - .cpu() - .data.numpy() - ) - else: - landmark = ( - self.landmark_detector(extracted_faces, **landmark_model_kwargs) - .cpu() - .data.numpy() - ) + landmark = landmark.reshape(landmark.shape[0], -1, 2) - landmark = landmark.reshape(landmark.shape[0], -1, 2) + landmark_results = [] + for ik in range(landmark.shape[0]): - landmark_results = [] - for ik in range(landmark.shape[0]): + landmark_results.append( + new_bbox[ik].inverse_transform_landmark(landmark[ik, :, :]) + ) - landmark_results.append( - new_bbox[ik].inverse_transform_landmark(landmark[ik, :, :]) - ) - - length_index = [len(x) for x in detected_faces] - new_lens = np.insert(np.cumsum(length_index), 0, 0) - list_concat = [] - for ij in range(len(length_index)): - list_concat.append(landmark_results[new_lens[ij] : new_lens[ij + 1]]) + length_index = [len(x) for x in detected_faces] + new_lens = np.insert(np.cumsum(length_index), 0, 0) + list_concat = [] + for ij in range(len(length_index)): + list_concat.append(landmark_results[new_lens[ij] : new_lens[ij + 1]]) return list_concat @@ -515,21 +519,23 @@ def detect_aus(self, frame, landmarks, **au_model_kwargs): logging.info("detecting aus...") frame = convert_image_to_tensor(frame, img_type="float32") - # frame = transforms.ToTensor()(frame) - - if self["au_model"].lower() in ["svm", "xgb"]: - # transform = Grayscale(3) - # frame = transform(frame) - hog_arr, new_lands = self._batch_hog(frames=frame, landmarks=landmarks) - au_predictions = self.au_model.detect_au( - frame=hog_arr, landmarks=new_lands, **au_model_kwargs - ) + + if _is_list_of_lists_empty(landmarks): + return landmarks else: - au_predictions = self.au_model.detect_au( - frame, landmarks=landmarks, **au_model_kwargs - ) + if self["au_model"].lower() in ["svm", "xgb"]: + # transform = Grayscale(3) + # frame = transform(frame) + hog_arr, new_lands = self._batch_hog(frames=frame, landmarks=landmarks) + au_predictions = self.au_model.detect_au( + frame=hog_arr, landmarks=new_lands, **au_model_kwargs + ) + else: + au_predictions = self.au_model.detect_au( + frame, landmarks=landmarks, **au_model_kwargs + ) - return self._convert_detector_output(landmarks, au_predictions) + return self._convert_detector_output(landmarks, au_predictions) def _batch_hog(self, frames, landmarks): """ @@ -600,25 +606,30 @@ def detect_emotions(self, frame, facebox, landmarks, **emotion_model_kwargs): logging.info("detecting emotions...") frame = convert_image_to_tensor(frame, img_type="float32") - if self.info["emotion_model"].lower() == "resmasknet": - return self._convert_detector_output( - facebox, - self.emotion_model.detect_emo(frame, facebox, **emotion_model_kwargs), - ) + if _is_list_of_lists_empty(facebox): + return facebox + else: + if self.info["emotion_model"].lower() == "resmasknet": + return self._convert_detector_output( + facebox, + self.emotion_model.detect_emo( + frame, facebox, **emotion_model_kwargs + ), + ) - elif self.info["emotion_model"].lower() == "svm": - hog_arr, new_lands = self._batch_hog(frames=frame, landmarks=landmarks) - return self._convert_detector_output( - landmarks, - self.emotion_model.detect_emo( - frame=hog_arr, landmarks=new_lands, **emotion_model_kwargs - ), - ) + elif self.info["emotion_model"].lower() == "svm": + hog_arr, new_lands = self._batch_hog(frames=frame, landmarks=landmarks) + return self._convert_detector_output( + landmarks, + self.emotion_model.detect_emo( + frame=hog_arr, landmarks=new_lands, **emotion_model_kwargs + ), + ) - else: - raise ValueError( - "Cannot recognize input emo model! Please try to re-type emotion model" - ) + else: + raise ValueError( + "Cannot recognize input emo model! Please try to re-type emotion model" + ) def _check_detections(self, faces, landmarks, poses, aus, emotions, batch_data): """ @@ -627,6 +638,9 @@ def _check_detections(self, faces, landmarks, poses, aus, emotions, batch_data): # Each input arg is a nested list with length == number of faces in the batch + if _is_list_of_lists_empty(faces) & _is_list_of_lists_empty(poses): + return + # Check 1) img2pose sometimes gives fewer detections that other models, we can't # properly assemble Fex when that's the case. Returning more or the same # detections is ok for now @@ -871,91 +885,122 @@ def _create_fex( """ logging.info("creating fex output...") - files = [[f] * n for f, n in zip(file_names, [len(x) for x in faces])] - - # Convert to Pandas Format - out = [] - for i, frame in enumerate(faces): - for j, face_in_frame in enumerate(frame): - facebox_df = pd.DataFrame( - [ + + if _is_list_of_lists_empty(faces): + return Fex( + pd.DataFrame( + pd.Series( + ChainMap( + {x: np.nan for x in self.info["face_detection_columns"]}, + {x: np.nan for x in self.info["facepose_model_columns"]}, + {x: np.nan for x in self.info["face_landmark_columns"]}, + {x: np.nan for x in self.info["au_presence_columns"]}, + {x: np.nan for x in self.info["emotion_model_columns"]}, + { + FEAT_TIME_COLUMNS[0]: frame_counter, + "input": file_names[0], + }, + ) + ) + ).T, + au_columns=self.info["au_presence_columns"], + emotion_columns=FEAT_EMOTION_COLUMNS, + facebox_columns=FEAT_FACEBOX_COLUMNS, + landmark_columns=openface_2d_landmark_columns, + facepose_columns=self.info["facepose_model_columns"], + detector="Feat", + face_model=self.info["face_model"], + landmark_model=self.info["landmark_model"], + au_model=self.info["au_model"], + emotion_model=self.info["emotion_model"], + facepose_model=self.info["facepose_model"], + ) + else: + files = [[f] * n for f, n in zip(file_names, [len(x) for x in faces])] + + # Convert to Pandas Format + out = [] + for i, frame in enumerate(faces): + for j, face_in_frame in enumerate(frame): + facebox_df = pd.DataFrame( [ - face_in_frame[0], - face_in_frame[1], - face_in_frame[2] - face_in_frame[0], - face_in_frame[3] - face_in_frame[1], - face_in_frame[4], - ] - ], - columns=self.info["face_detection_columns"], - index=[j], - ) + [ + face_in_frame[0], + face_in_frame[1], + face_in_frame[2] - face_in_frame[0], + face_in_frame[3] - face_in_frame[1], + face_in_frame[4], + ] + ], + columns=self.info["face_detection_columns"], + index=[j], + ) - facepose_df = pd.DataFrame( - [poses[i][j].flatten(order="F")], - columns=self.info["facepose_model_columns"], - index=[j], - ) + facepose_df = pd.DataFrame( + [poses[i][j].flatten(order="F")], + columns=self.info["facepose_model_columns"], + index=[j], + ) - landmarks_df = pd.DataFrame( - [landmarks[i][j].flatten(order="F")], - columns=self.info["face_landmark_columns"], - index=[j], - ) + landmarks_df = pd.DataFrame( + [landmarks[i][j].flatten(order="F")], + columns=self.info["face_landmark_columns"], + index=[j], + ) - aus_df = pd.DataFrame( - aus[i][j, :].reshape(1, len(self["au_presence_columns"])), - columns=self.info["au_presence_columns"], - index=[j], - ) + aus_df = pd.DataFrame( + aus[i][j, :].reshape(1, len(self["au_presence_columns"])), + columns=self.info["au_presence_columns"], + index=[j], + ) - emotions_df = pd.DataFrame( - emotions[i][j, :].reshape(1, len(FEAT_EMOTION_COLUMNS)), - columns=FEAT_EMOTION_COLUMNS, - index=[j], - ) + emotions_df = pd.DataFrame( + emotions[i][j, :].reshape(1, len(FEAT_EMOTION_COLUMNS)), + columns=FEAT_EMOTION_COLUMNS, + index=[j], + ) - input_df = pd.DataFrame( - files[i][j], - columns=["input"], - index=[j], - ) + input_df = pd.DataFrame( + files[i][j], + columns=["input"], + index=[j], + ) - tmp_df = pd.concat( - [ - facebox_df, - landmarks_df, - facepose_df, - aus_df, - emotions_df, - input_df, - ], - axis=1, - ) + tmp_df = pd.concat( + [ + facebox_df, + landmarks_df, + facepose_df, + aus_df, + emotions_df, + input_df, + ], + axis=1, + ) - if isinstance(frame_counter, (list)): - tmp_df[FEAT_TIME_COLUMNS] = frame_counter[i] - else: - tmp_df[FEAT_TIME_COLUMNS] = frame_counter + i - out.append(tmp_df) - out = pd.concat(out) - out.reset_index(drop=True, inplace=True) - - # TODO: Add in support for gaze_columns - return Fex( - out, - au_columns=self.info["au_presence_columns"], - emotion_columns=FEAT_EMOTION_COLUMNS, - facebox_columns=FEAT_FACEBOX_COLUMNS, - landmark_columns=openface_2d_landmark_columns, - facepose_columns=self.info["facepose_model_columns"], - detector="Feat", - face_model=self.info["face_model"], - landmark_model=self.info["landmark_model"], - au_model=self.info["au_model"], - emotion_model=self.info["emotion_model"], - facepose_model=self.info["facepose_model"], - ) + if isinstance(frame_counter, (list)): + tmp_df[FEAT_TIME_COLUMNS] = frame_counter[i] + else: + tmp_df[FEAT_TIME_COLUMNS] = frame_counter + i + out.append(tmp_df) + out = pd.concat(out) + out.reset_index(drop=True, inplace=True) + + # TODO: Add in support for gaze_columns + return Fex( + out, + au_columns=self.info["au_presence_columns"], + emotion_columns=FEAT_EMOTION_COLUMNS, + facebox_columns=FEAT_FACEBOX_COLUMNS, + landmark_columns=openface_2d_landmark_columns, + facepose_columns=self.info["facepose_model_columns"], + detector="Feat", + face_model=self.info["face_model"], + landmark_model=self.info["landmark_model"], + au_model=self.info["au_model"], + emotion_model=self.info["emotion_model"], + facepose_model=self.info["facepose_model"], + ) def _convert_detector_output(self, detected_faces, detector_results): """ @@ -979,3 +1024,11 @@ def _convert_detector_output(self, detected_faces, detector_results): for ij in range(len(length_index)): list_concat.append(detector_results[new_lens[ij] : new_lens[ij + 1], :]) return list_concat + + +def _is_list_of_lists_empty(list_of_lists): + """Helper function to check if list of lists is empty""" + if not any(list_of_lists): + return True + else: + return False diff --git a/feat/utils/image_operations.py b/feat/utils/image_operations.py index 4fbf327a..e660e52d 100644 --- a/feat/utils/image_operations.py +++ b/feat/utils/image_operations.py @@ -189,9 +189,10 @@ def extract_face_from_bbox(frame, detected_faces, face_size=112, expand_bbox=1.2 cropped_faces.append(transform(cropped)) bbox_list.append(bbox) - faces = torch.cat( - tuple([convert_image_to_tensor(x["Image"]) for x in cropped_faces]), 0 - ) + faces = torch.cat( + tuple([convert_image_to_tensor(x["Image"]) for x in cropped_faces]), 0 + ) + return (faces, bbox_list) @@ -469,29 +470,41 @@ def __init__( def __repr__(self): return f"'height': {self.height}, 'width': {self.width}" - + def __mul__(self, bbox2): - '''Create a new BBox based on the intersection between two BBox instances (AND operation)''' + """Create a new BBox based on the intersection between two BBox instances (AND operation)""" if isinstance(bbox2, (BBox)): - return BBox([np.max([self.left, bbox2.left]), - np.max([self.top, bbox2.top]), - np.min([self.right, bbox2.right]), - np.min([self.bottom, bbox2.bottom])]) + return BBox( + [ + np.max([self.left, bbox2.left]), + np.max([self.top, bbox2.top]), + np.min([self.right, bbox2.right]), + np.min([self.bottom, bbox2.bottom]), + ] + ) else: - raise NotImplementedError('Multiplication is currently only supported between two BBox instances') - + raise NotImplementedError( + "Multiplication is currently only supported between two BBox instances" + ) + def __add__(self, bbox2): - '''Create a new BBox based on the intersection between two BBox instances (OR Operation)''' - + """Create a new BBox based on the intersection between two BBox instances (OR Operation)""" + if isinstance(bbox2, (BBox)): - return BBox([np.min([self.left, bbox2.left]), - np.min([self.top, bbox2.top]), - np.max([self.right, bbox2.right]), - np.max([self.bottom, bbox2.bottom])]) + return BBox( + [ + np.min([self.left, bbox2.left]), + np.min([self.top, bbox2.top]), + np.max([self.right, bbox2.right]), + np.max([self.bottom, bbox2.bottom]), + ] + ) else: - raise NotImplementedError('Addition is currently only supported between two BBox instances') - + raise NotImplementedError( + "Addition is currently only supported between two BBox instances" + ) + def expand_by_factor(self, factor, symmetric=True): """Expand box by factor @@ -647,31 +660,38 @@ def inverse_transform_landmark(self, landmark): y = point[1] * self.height + self.top landmark_[i] = (x, y) return landmark_ - + def area(self): """Compute the area of the bounding box""" return self.height * self.width def overlap(self, bbox2): """Compute the percent overlap between BBox with another BBox""" - return (self * bbox2).area()/self.area() - + return (self * bbox2).area() / self.area() + def plot(self, ax=None, fill=False, linewidth=2, **kwargs): """Plot bounding box - + Args: ax: matplotlib axis fill (bool): fill rectangle """ - + if ax is None: fig, ax = plt.subplots() ax.plot() - - ax.add_patch(Rectangle((self.left, self.top), - self.width, self.height, - fill=fill, linewidth=linewidth, **kwargs)) - return ax + + ax.add_patch( + Rectangle( + (self.left, self.top), + self.width, + self.height, + fill=fill, + linewidth=linewidth, + **kwargs, + ) + ) + return ax def reverse_color_order(img): From 6514081373b19f9a2467718f31a3ba539addc4dd Mon Sep 17 00:00:00 2001 From: ljchang Date: Tue, 17 Jan 2023 23:50:12 -0500 Subject: [PATCH 03/22] added no_face tests --- feat/tests/conftest.py | 5 +++++ feat/tests/test_detector_core.py | 29 ++++++++++++++++++++++++++++- 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/feat/tests/conftest.py b/feat/tests/conftest.py index bda0050f..d0232444 100644 --- a/feat/tests/conftest.py +++ b/feat/tests/conftest.py @@ -84,6 +84,11 @@ def default_detector(): return Detector() +@fixture(scope="module") +def no_face_img(data_path): + return os.path.join(data_path, "free-mountain-vector-01.jpg") + + @fixture(scope="module") def single_face_img(data_path): return os.path.join(data_path, "single_face.jpg") diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py index 4dead6e5..cb3f36cb 100644 --- a/feat/tests/test_detector_core.py +++ b/feat/tests/test_detector_core.py @@ -108,6 +108,33 @@ def test_nofile(default_detector): _ = default_detector.detect_image(inputFname) +# No Face images +def test_detect_single_img_no_face(default_detector, no_face_img): + """Test detection of a single image with no face. Default detector returns 173 attributes""" + out = default_detector.detect_image(no_face_img) + assert type(out) == Fex + assert out.shape == (1, 173) + assert np.isnan(out.happiness.values[0]) + + +def test_detect_multi_img_no_face(default_detector, no_face_img): + """Test detection of a multiple images with no face. Default detector returns 173 attributes""" + out = default_detector.detect_image([no_face_img] * 3) + assert out.shape == (3, 173) + + +def test_detect_multi_img_no_face_batching(default_detector, no_face_img): + """Test detection of a multiple images with no face. Default detector returns 173 attributes""" + out = default_detector.detect_image([no_face_img] * 5, batch_size=2) + assert out.shape == (5, 173) + + +def test_detect_multi_img_mixed_no_face(default_detector, no_face_img, single_face_img): + """Test detection of a single image with no face. Default detector returns 173 attributes""" + out = default_detector.detect_image([no_face_img, single_face_img] * 3) + assert out.shape == (6, 173) + + # Single images def test_detect_single_img_single_face(default_detector, single_face_img): """Test detection of single face from single image. Default detector returns 173 attributes""" @@ -151,7 +178,7 @@ def test_detect_mismatch_image_sizes(default_detector, single_face_img, multi_fa assert out.shape == (6, 173) out = default_detector.detect_image( - [multi_face_img, single_face_img] * 5, batch_size=5, output_size=256 + [multi_face_img, single_face_img] * 5, batch_size=5, output_size=512 ) assert out.shape == (30, 173) From e026286ca884af6dafb5ef6479357a7b3a9bce24 Mon Sep 17 00:00:00 2001 From: ljchang Date: Mon, 23 Jan 2023 00:54:02 -0500 Subject: [PATCH 04/22] fixed bug in BBox --- feat/utils/image_operations.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/feat/utils/image_operations.py b/feat/utils/image_operations.py index e660e52d..b0685e33 100644 --- a/feat/utils/image_operations.py +++ b/feat/utils/image_operations.py @@ -23,6 +23,7 @@ from skimage import draw import logging from matplotlib.patches import Rectangle +import matplotlib.pyplot as plt __all__ = [ "neutral", @@ -667,7 +668,11 @@ def area(self): def overlap(self, bbox2): """Compute the percent overlap between BBox with another BBox""" - return (self * bbox2).area() / self.area() + overlap_bbox = self * bbox2 + if (overlap_bbox.height < 0) or (overlap_bbox.width < 0): + return 0 + else: + return (self * bbox2).area() / self.area() def plot(self, ax=None, fill=False, linewidth=2, **kwargs): """Plot bounding box From f5a1276962b0f2b67c1d21d65aa867df62da811b Mon Sep 17 00:00:00 2001 From: ljchang Date: Mon, 23 Jan 2023 00:54:19 -0500 Subject: [PATCH 05/22] added checks to match faces to poses --- feat/detector.py | 208 +++++++++++++++++++++---------- feat/tests/test_detector_core.py | 145 +++++++++++++++++++++ 2 files changed, 288 insertions(+), 65 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index f50b7bd3..d5246a69 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -423,6 +423,7 @@ def detect_landmarks(self, frame, detected_faces, **landmark_model_kwargs): else: if self.info["landmark_model"]: if self.info["landmark_model"].lower() == "mobilenet": + out_size = 224 else: out_size = 112 @@ -492,12 +493,17 @@ def detect_facepose(self, frame, landmarks=None, **facepose_model_kwargs): # Normalize Data frame = convert_image_to_tensor(frame, img_type="float32") / 255 + output = {} if "img2pose" in self.info["facepose_model"]: faces, poses = self.facepose_detector(frame, **facepose_model_kwargs) + output["faces"] = faces + output["poses"] = poses else: - poses = self.facepose_detector(frame, landmarks, **facepose_model_kwargs) + output["poses"] = self.facepose_detector( + frame, landmarks, **facepose_model_kwargs + ) - return poses + return output def detect_aus(self, frame, landmarks, **au_model_kwargs): """Detect Action Units from image or video frame @@ -631,26 +637,6 @@ def detect_emotions(self, frame, facebox, landmarks, **emotion_model_kwargs): "Cannot recognize input emo model! Please try to re-type emotion model" ) - def _check_detections(self, faces, landmarks, poses, aus, emotions, batch_data): - """ - Private method to ensure that all detectors return the same number of detections - """ - - # Each input arg is a nested list with length == number of faces in the batch - - if _is_list_of_lists_empty(faces) & _is_list_of_lists_empty(poses): - return - - # Check 1) img2pose sometimes gives fewer detections that other models, we can't - # properly assemble Fex when that's the case. Returning more or the same - # detections is ok for now - if len(poses[0]) >= len(faces[0]): - return - - raise ValueError( - f"Mismatch across detectors when processing batch: {batch_data['FileNames']}\n\nAn error occurred trying to merge detections into a single Fex object, as each type of detector is detecting a different number of faces:\n\nface_detector: {len(faces[0])}\npose_detector: {len(poses[0])}\nlandmark_detector: {len(landmarks[0])}\nau_detector: {len(aus[0])}\nemotion_detector: {len(emotions[0])}\n\nThis can happen for a number of reasons. Here are a few solutions:\n\n1) the face_model is too liberal. You use the 'threshold' keyword argument to make the detector more conservative, e.g. threshold= some val > 0.5\n2) the pose_detector gives different predictions than other detectors. You can use the same model for both pose and face detection by setting face_model='img2pose' and pose_model='img2pose' (or 'img2pose-c')" - ) - def detect_image( self, input_file_list, @@ -659,7 +645,6 @@ def detect_image( num_workers=0, pin_memory=False, frame_counter=0, - skip_failed_detections=False, threshold=0.9, **kwargs, ): @@ -715,8 +700,8 @@ def detect_image( warnings.warn( "Currently using mobilenet for landmark detection with batch_size > 1 may lead to erroneous detections. We recommend either setting batch_size=1 or using mobilefacenet as the landmark detection model. You can follow this issue for more: https://github.com/cosanlab/py-feat/issues/151" ) - try: + try: batch_output = [] for batch_id, batch_data in enumerate(tqdm(data_loader)): frame_counter += frame_counter + batch_id * batch_size @@ -736,26 +721,23 @@ def detect_image( faces = _inverse_face_transform(faces, batch_data) landmarks = _inverse_landmark_transform(landmarks, batch_data) - try: - self._check_detections( - faces, landmarks, poses, aus, emotions, batch_data - ) - output = self._create_fex( - faces, - landmarks, - poses, - aus, - emotions, - batch_data["FileNames"], - frame_counter, - ) - batch_output.append(output) - except ValueError as e: - if skip_failed_detections: - print(e) - continue - else: - raise e + + # match faces to poses - sometimes face detector finds different faces than pose detector. + faces, poses = _match_faces_to_poses( + faces, poses["faces"], poses["poses"] + ) + + output = self._create_fex( + faces, + landmarks, + poses["poses"], + aus, + emotions, + batch_data["FileNames"], + frame_counter, + ) + batch_output.append(output) + batch_output = pd.concat(batch_output) batch_output.reset_index(drop=True, inplace=True) @@ -774,7 +756,6 @@ def detect_video( batch_size=1, num_workers=0, pin_memory=False, - skip_failed_detections=False, **detector_kwargs, ): """Detects FEX from a video file. @@ -818,26 +799,18 @@ def detect_video( frames = list(batch_data["Frame"].numpy()) landmarks = _inverse_landmark_transform(landmarks, batch_data) - try: - self._check_detections( - faces, landmarks, poses, aus, emotions, batch_data - ) - output = self._create_fex( - faces, - landmarks, - poses, - aus, - emotions, - batch_data["FileName"], - frames, - ) - batch_output.append(output) - except ValueError as e: - if skip_failed_detections: - print(e) - continue - else: - raise e + # match faces to poses - sometimes face detector finds different faces than pose detector. + faces, poses = _match_faces_to_poses(faces, poses["faces"], poses["poses"]) + + output = self._create_fex( + faces, + landmarks, + poses["poses"], + aus, + emotions, + batch_data["FileName"], + frames, + ) batch_output.append(output) @@ -1032,3 +1005,108 @@ def _is_list_of_lists_empty(list_of_lists): return True else: return False + + +def _match_faces_to_poses(faces, faces_pose, poses): + """Function to match list of lists of faces and poses based on overlap in bounding boxes. + + Sometimes the face detector finds different faces than the pose detector unless the user + is using the same detector (i.e., img2pose). + + This function will match the faces and poses and will return nans if more faces are detected then poses. + """ + + if len(faces) != len(faces_pose): + raise ValueError( + "Make sure the number of images in faces and poses is the same." + ) + + overlap_faces = [] + overlap_poses = [] + for frame_face, frame_face_pose, frame_pose in zip(faces, faces_pose, poses): + if isinstance(frame_face[0], list): + n_faces = len(frame_face) + else: + n_faces = 1 + + if isinstance(frame_face_pose[0], list): + n_poses = len(frame_face_pose) + else: + n_poses = 1 + + frame_overlap = np.zeros([n_faces, n_poses]) + + if (n_faces == 1) & (n_poses > 1): + b1 = BBox(frame_face[:-1]) + + for pose_idx in range(n_poses): + b2 = BBox(frame_face_pose[pose_idx][:-1]) + frame_overlap[0, pose_idx] = b1.overlap(b2) + matched_pose_index = np.where( + frame_overlap[0, :] == frame_overlap[0, :].max() + )[0][0] + overlap_faces.append(frame_face) + overlap_poses.append(frame_pose[matched_pose_index]) + + elif (n_faces > 1) & (n_poses == 1): + b2 = BBox(frame_face_pose[:-1]) + for face_idx in range(n_faces): + b1 = BBox(frame_face[face_idx][:-1]) + frame_overlap[face_idx, 0] = b1.overlap(b2) + matched_face_index = np.where( + frame_overlap[:, 0] == frame_overlap[:, 0].max() + )[0][0] + new_poses = [] + for f_idx in range(n_faces): + if f_idx == matched_face_index: + new_poses.append(frame_pose[0]) + else: + new_poses.append(np.ones(3) * np.nan) + overlap_faces.append(frame_face) + overlap_poses.append(new_poses) + + else: + for face_idx in range(n_faces): + b1 = BBox(frame_face[face_idx][:-1]) + for pose_idx in range(n_poses): + b2 = BBox(frame_face_pose[pose_idx][:-1]) + frame_overlap[face_idx, pose_idx] = b1.overlap(b2) + + overlap_faces_frame = [] + overlap_poses_frame = [] + if n_faces < n_poses: + for face_idx in range(n_faces): + pose_idx = np.where( + frame_overlap[face_idx, :] == frame_overlap[face_idx, :].max() + )[0][0] + overlap_faces_frame.append(frame_face[face_idx]) + overlap_poses_frame.append(frame_pose[pose_idx]) + elif n_faces > n_poses: + matched_pose_index = [] + for pose_idx in range(n_poses): + matched_pose_index.append( + np.where( + frame_overlap[:, pose_idx] + == frame_overlap[:, pose_idx].max() + )[0][0] + ) + for face_idx in range(n_faces): + overlap_faces_frame.append(frame_face[face_idx]) + if face_idx in matched_pose_index: + overlap_poses_frame.append( + frame_pose[ + np.where( + frame_overlap[face_idx, :] + == frame_overlap[face_idx, :].max() + )[0][0] + ] + ) + else: + overlap_poses_frame.append(np.ones(3) * np.nan) + pose_idx = np.where( + frame_overlap[face_idx, :] == frame_overlap[face_idx, :].max() + )[0][0] + overlap_faces.append(overlap_faces_frame) + overlap_poses.append(overlap_poses_frame) + + return overlap_faces, overlap_poses diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py index cb3f36cb..35a658ba 100644 --- a/feat/tests/test_detector_core.py +++ b/feat/tests/test_detector_core.py @@ -188,3 +188,148 @@ def test_detect_video(default_detector, single_face_mov): out = default_detector.detect_video(single_face_mov, skip_frames=24) assert len(out) == 3 assert out.happiness.values.max() > 0 + + +def test_detect_mismatch_face_pose(default_detector): + # Multiple Faces, 1 pose + faces = [ + [ + [ + 45.34465026855469, + 49.546714782714844, + 63.04056167602539, + 70.38599395751953, + 0.95337886, + ], + [ + 146.09866333007812, + 96.34442901611328, + 165.69561767578125, + 120.71611022949219, + 0.9069432, + ], + ] + ] + faces_pose = [[46.0, 46.0, 66.0, 71.0, 0.99272925]] + poses = [[np.array([-3.72766398, 10.9359162, -3.19862351])]] + + new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) + assert len(new_faces[0]) == len(new_poses[0]) + + # 1 face, multiple poses + faces = [ + [ + 45.34465026855469, + 49.546714782714844, + 63.04056167602539, + 70.38599395751953, + 0.95337886, + ] + ] + + faces_pose = [ + [ + [65.0, 83.0, 87.0, 110.0, 0.99630725], + [141.0, 94.0, 167.0, 123.0, 0.9952237], + [111.0, 97.0, 136.0, 126.0, 0.99487805], + [91.0, 78.0, 109.0, 100.0, 0.99454665], + [46.0, 46.0, 66.0, 71.0, 0.99272925], + ] + ] + + poses = [ + [ + np.array([[-5.90236694, -2.81686444, -5.38250827]]), + np.array([[18.3324545, 7.2330487, 2.70649852]]), + np.array([[12.04520545, 5.91369713, 6.13698383]]), + np.array([[1.10688262, 1.56339815, -0.91693287]]), + np.array([[-3.72766398, 10.9359162, -3.19862351]]), + ] + ] + + new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) + assert len(new_faces[0]) == 5 + assert len(new_poses[0]) == 1 + + # 2 Faces, 5 Poses + faces = [ + [ + [ + 45.34465026855469, + 49.546714782714844, + 63.04056167602539, + 70.38599395751953, + 0.95337886, + ], + [ + 146.09866333007812, + 96.34442901611328, + 165.69561767578125, + 120.71611022949219, + 0.9069432, + ], + ] + ] + + faces_pose = [ + [ + [65.0, 83.0, 87.0, 110.0, 0.99630725], + [141.0, 94.0, 167.0, 123.0, 0.9952237], + [111.0, 97.0, 136.0, 126.0, 0.99487805], + [91.0, 78.0, 109.0, 100.0, 0.99454665], + [46.0, 46.0, 66.0, 71.0, 0.99272925], + ] + ] + + poses = [ + [ + np.array([[-5.90236694, -2.81686444, -5.38250827]]), + np.array([[18.3324545, 7.2330487, 2.70649852]]), + np.array([[12.04520545, 5.91369713, 6.13698383]]), + np.array([[1.10688262, 1.56339815, -0.91693287]]), + np.array([[-3.72766398, 10.9359162, -3.19862351]]), + ] + ] + + new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) + assert len(new_faces[0]) == len(new_poses[0]) + + # 5 Faces, 2 Poses + faces = [ + [ + [65.0, 83.0, 87.0, 110.0, 0.99630725], + [141.0, 94.0, 167.0, 123.0, 0.9952237], + [111.0, 97.0, 136.0, 126.0, 0.99487805], + [91.0, 78.0, 109.0, 100.0, 0.99454665], + [46.0, 46.0, 66.0, 71.0, 0.99272925], + ] + ] + + faces_pose = [ + [ + [ + 45.34465026855469, + 49.546714782714844, + 63.04056167602539, + 70.38599395751953, + 0.95337886, + ], + [ + 146.09866333007812, + 96.34442901611328, + 165.69561767578125, + 120.71611022949219, + 0.9069432, + ], + ] + ] + + poses = [ + [ + np.array([[-5.90236694, -2.81686444, -5.38250827]]), + np.array([[18.3324545, 7.2330487, 2.70649852]]), + ] + ] + + new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) + assert len(new_faces[0]) == len(new_poses[0]) From 6d4908c16dbb1c3dd3599daa0e2e7d02361ab549 Mon Sep 17 00:00:00 2001 From: ljchang Date: Mon, 23 Jan 2023 22:29:06 -0500 Subject: [PATCH 06/22] cleaned detector outputs to be more consistent --- feat/data.py | 28 ++++++++++--------- .../FaceBoxes/FaceBoxes_test.py | 10 +++---- feat/face_detectors/MTCNN/MTCNN_test.py | 8 ++++-- .../Retinaface/Retinaface_test.py | 12 ++++---- .../img2pose/img2pose_test.py | 2 +- feat/utils/image_operations.py | 2 +- 6 files changed, 33 insertions(+), 29 deletions(-) diff --git a/feat/data.py b/feat/data.py index 3c01cbd2..6040ae56 100644 --- a/feat/data.py +++ b/feat/data.py @@ -1974,19 +1974,21 @@ def _inverse_face_transform(faces, batch_data): out_face = [] for face in frame: out_face.append( - np.append( - ( - np.array( - [ - face[0] - left, - face[1] - top, - face[2] - left, - face[3] - top, - ] - ) - / scale - ), - face[4], + list( + np.append( + ( + np.array( + [ + face[0] - left, + face[1] - top, + face[2] - left, + face[3] - top, + ] + ) + / scale + ), + face[4], + ) ) ) out_frame.append(out_face) diff --git a/feat/face_detectors/FaceBoxes/FaceBoxes_test.py b/feat/face_detectors/FaceBoxes/FaceBoxes_test.py index 7a96f982..1bcfede2 100644 --- a/feat/face_detectors/FaceBoxes/FaceBoxes_test.py +++ b/feat/face_detectors/FaceBoxes/FaceBoxes_test.py @@ -52,7 +52,7 @@ def __init__( top_k=5000, keep_top_k=750, nms_threshold=0.3, - vis_threshold=0.5, + detection_threshold=0.5, resize=1, device="auto", ): @@ -82,14 +82,14 @@ def __init__( self.top_k, self.keep_top_k, self.nms_threshold, - self.vis_threshold, + self.detection_threshold, self.resize, ) = ( confidence_threshold, top_k, keep_top_k, nms_threshold, - vis_threshold, + detection_threshold, resize, ) @@ -148,11 +148,11 @@ def _calculate_boxinfo(self, im_height, im_width, loc, conf, scale): # keep top-K faster NMS dets = dets[: self.keep_top_k, :] - # filter using vis_thres - rescale box size to be proportional to image size + # filter using detection_threshold - rescale box size to be proportional to image size scale_x, scale_y = (im_width / im_height, im_height / im_width) det_bboxes = [] for b in dets: - if b[4] > self.vis_threshold: + if b[4] > self.detection_threshold: xmin, ymin, xmax, ymax, score = b det_bboxes.append( [ diff --git a/feat/face_detectors/MTCNN/MTCNN_test.py b/feat/face_detectors/MTCNN/MTCNN_test.py index 113e88fb..f67be176 100644 --- a/feat/face_detectors/MTCNN/MTCNN_test.py +++ b/feat/face_detectors/MTCNN/MTCNN_test.py @@ -32,6 +32,7 @@ class MTCNN(nn.Module): (default: {0}) min_face_size {int} -- Minimum face size to search for. (default: {20}) thresholds {list} -- MTCNN face detection thresholds (default: {[0.6, 0.7, 0.7]}) + detection_threshold (float): threshold for detectiong faces (default=0.5). Will override the last stage of thresholds factor {float} -- Factor used to create a scaling pyramid of face sizes. (default: {0.709}) post_process {bool} -- Whether or not to post process images tensors before returning. (default: {True}) @@ -46,8 +47,7 @@ class MTCNN(nn.Module): "center_weighted_size": box size minus weighted squared offset from image center (default: {None}) keep_all {bool} -- If True, all detected faces are returned, in the order dictated by the - select_largest parameter. If a save_path is specified, the first face is saved to that - path and the remaining faces are saved to 1, 2 etc. + select_largest parameter. (default: {False}) device {torch.device} -- The device on which to run neural net passes. Image tensors and models are copied to this device before running forward passes. (default: 'auto') @@ -59,11 +59,12 @@ def __init__( margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], + detection_threshold=0.5, factor=0.709, post_process=True, select_largest=True, selection_method=None, - keep_all=False, + keep_all=True, device="auto", ): super().__init__() @@ -72,6 +73,7 @@ def __init__( self.margin = margin self.min_face_size = min_face_size self.thresholds = thresholds + self.thresholds[-1] = detection_threshold self.factor = factor self.post_process = post_process self.select_largest = select_largest diff --git a/feat/face_detectors/Retinaface/Retinaface_test.py b/feat/face_detectors/Retinaface/Retinaface_test.py index f1a56dc8..ed5e71d2 100644 --- a/feat/face_detectors/Retinaface/Retinaface_test.py +++ b/feat/face_detectors/Retinaface/Retinaface_test.py @@ -21,7 +21,7 @@ def __init__( self, device="auto", resize=1, - vis_threshold=0.5, + detection_threshold=0.5, nms_threshold=0.4, keep_top_k=750, top_k=5000, @@ -34,7 +34,7 @@ def __init__( device: (str) timer_flag: (bool) resize: (int) - vis_threshold: (float) + detection_threshold: (float) nms_threshold: (float) keep_top_k: (float) top_k: (float) @@ -78,14 +78,14 @@ def __init__( # Set cutoff parameters ( self.resize, - self.vis_threshold, + self.detection_threshold, self.nms_threshold, self.keep_top_k, self.top_k, self.confidence_threshold, ) = ( resize, - vis_threshold, + detection_threshold, nms_threshold, keep_top_k, top_k, @@ -175,11 +175,11 @@ def _calculate_boxinfo(self, im_height, im_width, loc, conf, landms, scale, img) # keep top-K faster NMS dets = dets[: self.keep_top_k, :] - # filter using vis_thres - rescale box size to be proportional to image size + # filter using detection_threshold - rescale box size to be proportional to image size scale_x, scale_y = (im_width / im_height, im_height / im_width) det_bboxes = [] for b in dets: - if b[4] > self.vis_threshold: + if b[4] > self.detection_threshold: xmin, ymin, xmax, ymax, score = b det_bboxes.append( [ diff --git a/feat/facepose_detectors/img2pose/img2pose_test.py b/feat/facepose_detectors/img2pose/img2pose_test.py index d915c234..3671b117 100644 --- a/feat/facepose_detectors/img2pose/img2pose_test.py +++ b/feat/facepose_detectors/img2pose/img2pose_test.py @@ -231,7 +231,7 @@ def predict(self, img, border_size=0, scale=1.0, euler=True): dof_pose = pose_pred[:] # pitch, roll, yaw, x, y, z dof_pose = dof_pose.reshape(1, -1) - det_pose.append(dof_pose) + det_pose.append(list(dof_pose.flatten())) return {"boxes": det_bboxes, "poses": det_pose} diff --git a/feat/utils/image_operations.py b/feat/utils/image_operations.py index b0685e33..1a278b3c 100644 --- a/feat/utils/image_operations.py +++ b/feat/utils/image_operations.py @@ -797,7 +797,7 @@ def convert_to_euler(rotvec, is_rotvec=True): rotvec = Rotation.from_rotvec(rotvec).as_matrix() rot_mat_2 = np.transpose(rotvec) angle = Rotation.from_matrix(rot_mat_2).as_euler("xyz", degrees=True) - return np.array([angle[0], -angle[2], -angle[1]]) # pitch, roll, yaw + return [angle[0], -angle[2], -angle[1]] # pitch, roll, yaw def py_cpu_nms(dets, thresh): From 78da80eb40851bb62ba94d9f4727b871af43f3c2 Mon Sep 17 00:00:00 2001 From: ljchang Date: Mon, 23 Jan 2023 22:29:37 -0500 Subject: [PATCH 07/22] cleaned helper functions --- feat/detector.py | 312 +++++++++++++-------------- feat/tests/test_detector_core.py | 44 ++-- feat/tests/test_pretrained_models.py | 6 +- feat/utils/__init__.py | 8 + 4 files changed, 188 insertions(+), 182 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index d5246a69..ab807f69 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -16,12 +16,14 @@ FEAT_FACEPOSE_COLUMNS_6D, FEAT_TIME_COLUMNS, set_torch_device, + is_list_of_lists_empty, ) from feat.utils.io import get_resource_path from feat.utils.image_operations import ( extract_face_from_landmarks, extract_face_from_bbox, convert_image_to_tensor, + BBox, ) from feat.pretrained import get_pretrained_models, fetch_model, AU_LANDMARK_MAP from feat.data import ( @@ -359,6 +361,7 @@ def detect_faces(self, frame, threshold=0.5, **face_model_kwargs): Args: frame (np.ndarray): 3d (single) or 4d (multiple) image array + threshold (float): threshold for detectiong faces (default=0.5) Returns: list: list of lists with the same length as the number of frames. Each list @@ -383,7 +386,7 @@ def detect_faces(self, frame, threshold=0.5, **face_model_kwargs): else: faces = self.face_detector(frame, **face_model_kwargs) - if len(faces) == 0: + if is_list_of_lists_empty(faces): logging.warning("Warning: NO FACE is detected") thresholded_face = [] @@ -418,7 +421,7 @@ def detect_landmarks(self, frame, detected_faces, **landmark_model_kwargs): logging.info("detecting landmarks...") frame = convert_image_to_tensor(frame) - if _is_list_of_lists_empty(detected_faces): + if is_list_of_lists_empty(detected_faces): list_concat = detected_faces else: if self.info["landmark_model"]: @@ -526,7 +529,7 @@ def detect_aus(self, frame, landmarks, **au_model_kwargs): logging.info("detecting aus...") frame = convert_image_to_tensor(frame, img_type="float32") - if _is_list_of_lists_empty(landmarks): + if is_list_of_lists_empty(landmarks): return landmarks else: if self["au_model"].lower() in ["svm", "xgb"]: @@ -541,7 +544,7 @@ def detect_aus(self, frame, landmarks, **au_model_kwargs): frame, landmarks=landmarks, **au_model_kwargs ) - return self._convert_detector_output(landmarks, au_predictions) + return _convert_detector_output(landmarks, au_predictions) def _batch_hog(self, frames, landmarks): """ @@ -612,11 +615,11 @@ def detect_emotions(self, frame, facebox, landmarks, **emotion_model_kwargs): logging.info("detecting emotions...") frame = convert_image_to_tensor(frame, img_type="float32") - if _is_list_of_lists_empty(facebox): + if is_list_of_lists_empty(facebox): return facebox else: if self.info["emotion_model"].lower() == "resmasknet": - return self._convert_detector_output( + return _convert_detector_output( facebox, self.emotion_model.detect_emo( frame, facebox, **emotion_model_kwargs @@ -625,7 +628,7 @@ def detect_emotions(self, frame, facebox, landmarks, **emotion_model_kwargs): elif self.info["emotion_model"].lower() == "svm": hog_arr, new_lands = self._batch_hog(frames=frame, landmarks=landmarks) - return self._convert_detector_output( + return _convert_detector_output( landmarks, self.emotion_model.detect_emo( frame=hog_arr, landmarks=new_lands, **emotion_model_kwargs @@ -645,7 +648,7 @@ def detect_image( num_workers=0, pin_memory=False, frame_counter=0, - threshold=0.9, + face_detection_threshold=0.5, **kwargs, ): """ @@ -660,17 +663,17 @@ def detect_image( Args: input_file_list (list of str): Path to a list of paths to image files. output_size (int): image size to rescale all image preserving aspect ratio. - Will raise an error if not set and batch_size > 1 but images are not the same size + Will raise an error if not set and batch_size > 1 but images are not the same size batch_size (int): how many batches of images you want to run at one shot. - Larger gives faster speed but is more memory-consuming. Images must be the + Larger gives faster speed but is more memory-consuming. Images must be the same size to be run in batches! num_workers (int): how many subprocesses to use for data loading. ``0`` means that the data will be loaded in the main process. pin_memory (bool): If ``True``, the data loader will copy Tensors into CUDA pinned memory before returning them. If your data elements are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type frame_counter (int): starting value to count frames - threshold (float): value between 0-1 to report a detection based on the - confidence of the face detector; Default >= 0.5 + face_detection_threshold (float): value between 0-1 to report a detection based on the + confidence of the face detector; Default >= 0.5 **kwargs: you can pass each detector specific kwargs using a dictionary - like: `face_model_kwargs = {...}, au_model_kwargs={...}, ...` + like: `face_model_kwargs = {...}, au_model_kwargs={...}, ...` Returns: Fex: Prediction results dataframe @@ -704,14 +707,15 @@ def detect_image( try: batch_output = [] for batch_id, batch_data in enumerate(tqdm(data_loader)): - frame_counter += frame_counter + batch_id * batch_size faces = self.detect_faces( - batch_data["Image"], threshold=threshold, **face_model_kwargs + batch_data["Image"], + threshold=face_detection_threshold, + **face_model_kwargs, ) landmarks = self.detect_landmarks( batch_data["Image"], detected_faces=faces, **landmark_model_kwargs ) - poses = self.detect_facepose( + poses_dict = self.detect_facepose( batch_data["Image"], landmarks, **facepose_model_kwargs ) aus = self.detect_aus(batch_data["Image"], landmarks, **au_model_kwargs) @@ -724,19 +728,20 @@ def detect_image( # match faces to poses - sometimes face detector finds different faces than pose detector. faces, poses = _match_faces_to_poses( - faces, poses["faces"], poses["poses"] + faces, poses_dict["faces"], poses_dict["poses"] ) output = self._create_fex( faces, landmarks, - poses["poses"], + poses, aus, emotions, batch_data["FileNames"], frame_counter, ) batch_output.append(output) + frame_counter += 1 * batch_size batch_output = pd.concat(batch_output) batch_output.reset_index(drop=True, inplace=True) @@ -752,10 +757,10 @@ def detect_video( video_path, skip_frames=None, output_size=700, - threshold=0.9, batch_size=1, num_workers=0, pin_memory=False, + face_detection_threshold=0.5, **detector_kwargs, ): """Detects FEX from a video file. @@ -770,6 +775,8 @@ def detect_video( pin_memory (bool): If ``True``, the data loader will copy Tensors into CUDA pinned memory before returning them. If your data elements are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type + face_detection_threshold (float): value between 0-1 to report a detection based on the + confidence of the face detector; Default >= 0.5 Returns: Fex: Prediction results dataframe @@ -786,12 +793,14 @@ def detect_video( batch_output = [] for batch_data in tqdm(data_loader): faces = self.detect_faces( - batch_data["Image"], threshold=threshold, **detector_kwargs + batch_data["Image"], + threshold=face_detection_threshold, + **detector_kwargs, ) landmarks = self.detect_landmarks( batch_data["Image"], detected_faces=faces, **detector_kwargs ) - poses = self.detect_facepose(batch_data["Image"], **detector_kwargs) + poses_dict = self.detect_facepose(batch_data["Image"], **detector_kwargs) aus = self.detect_aus(batch_data["Image"], landmarks, **detector_kwargs) emotions = self.detect_emotions( batch_data["Image"], faces, landmarks, **detector_kwargs @@ -800,12 +809,14 @@ def detect_video( landmarks = _inverse_landmark_transform(landmarks, batch_data) # match faces to poses - sometimes face detector finds different faces than pose detector. - faces, poses = _match_faces_to_poses(faces, poses["faces"], poses["poses"]) + faces, poses = _match_faces_to_poses( + faces, poses_dict["faces"], poses_dict["poses"] + ) output = self._create_fex( faces, landmarks, - poses["poses"], + poses, aus, emotions, batch_data["FileName"], @@ -818,27 +829,6 @@ def detect_video( batch_output.reset_index(drop=True, inplace=True) return batch_output.set_index("frame", drop=False) - def _convert_detector_output(detected_faces, detector_results): - """Helper function to convert AU/Emotion detector output into frame by face list of lists. - - Args: - detected_faces (list): list of lists output from face/landmark detector - au_results (np.array):, results from au/emotion detectors - - Returns: - list_concat: (list of list). The list which contains the number of faces. for example - if you process 2 frames and each frame contains 4 faces, it will return: - [[xxx,xxx,xxx,xxx],[xxx,xxx,xxx,xxx]] - """ - - length_index = [len(x) for x in detected_faces] - - list_concat = [] - new_lens = np.insert(np.cumsum(length_index), 0, 0) - for ij in range(len(length_index)): - list_concat.append(detector_results[new_lens[ij] : new_lens[ij + 1], :]) - return list_concat - def _create_fex( self, faces, landmarks, poses, aus, emotions, file_names, frame_counter ): @@ -859,7 +849,7 @@ def _create_fex( logging.info("creating fex output...") - if _is_list_of_lists_empty(faces): + if is_list_of_lists_empty(faces): return Fex( pd.DataFrame( pd.Series( @@ -877,9 +867,9 @@ def _create_fex( ) ).T, au_columns=self.info["au_presence_columns"], - emotion_columns=FEAT_EMOTION_COLUMNS, - facebox_columns=FEAT_FACEBOX_COLUMNS, - landmark_columns=openface_2d_landmark_columns, + emotion_columns=self.info["emotion_model_columns"], + facebox_columns=self.info["face_detection_columns"], + landmark_columns=self.info["face_landmark_columns"], facepose_columns=self.info["facepose_model_columns"], detector="Feat", face_model=self.info["face_model"], @@ -910,7 +900,7 @@ def _create_fex( ) facepose_df = pd.DataFrame( - [poses[i][j].flatten(order="F")], + [poses[i][j]], columns=self.info["facepose_model_columns"], index=[j], ) @@ -928,8 +918,10 @@ def _create_fex( ) emotions_df = pd.DataFrame( - emotions[i][j, :].reshape(1, len(FEAT_EMOTION_COLUMNS)), - columns=FEAT_EMOTION_COLUMNS, + emotions[i][j, :].reshape( + 1, len(self.info["emotion_model_columns"]) + ), + columns=self.info["emotion_model_columns"], index=[j], ) @@ -975,36 +967,29 @@ def _create_fex( facepose_model=self.info["facepose_model"], ) - def _convert_detector_output(self, detected_faces, detector_results): - """ - Helper function to convert AU/Emotion detector output into frame by face list of lists. - Either face or landmark detector list of list outputs can be used. - - Args: - detected_faces (list): list of lists output from face/landmark detector - au_results (np.array):, results from au/emotion detectors - Returns: - list_concat: (list of list). The list which contains the number of faces. for example - if you process 2 frames and each frame contains 4 faces, it will return: - [[xxx,xxx,xxx,xxx],[xxx,xxx,xxx,xxx]] - """ +def _convert_detector_output(detected_faces, detector_results): + """ + Helper function to convert AU/Emotion detector output into frame by face list of lists. + Either face or landmark detector list of list outputs can be used. - length_index = [len(x) for x in detected_faces] + Args: + detected_faces (list): list of lists output from face/landmark detector + au_results (np.array):, results from au/emotion detectors - list_concat = [] - new_lens = np.insert(np.cumsum(length_index), 0, 0) - for ij in range(len(length_index)): - list_concat.append(detector_results[new_lens[ij] : new_lens[ij + 1], :]) - return list_concat + Returns: + list_concat: (list of list). The list which contains the number of faces. for example + if you process 2 frames and each frame contains 4 faces, it will return: + [[xxx,xxx,xxx,xxx],[xxx,xxx,xxx,xxx]] + """ + length_index = [len(x) for x in detected_faces] -def _is_list_of_lists_empty(list_of_lists): - """Helper function to check if list of lists is empty""" - if not any(list_of_lists): - return True - else: - return False + list_concat = [] + new_lens = np.insert(np.cumsum(length_index), 0, 0) + for ij in range(len(length_index)): + list_concat.append(detector_results[new_lens[ij] : new_lens[ij + 1], :]) + return list_concat def _match_faces_to_poses(faces, faces_pose, poses): @@ -1018,95 +1003,106 @@ def _match_faces_to_poses(faces, faces_pose, poses): if len(faces) != len(faces_pose): raise ValueError( - "Make sure the number of images in faces and poses is the same." + "Make sure the number of batches in faces and poses is the same." ) - overlap_faces = [] - overlap_poses = [] - for frame_face, frame_face_pose, frame_pose in zip(faces, faces_pose, poses): - if isinstance(frame_face[0], list): - n_faces = len(frame_face) - else: - n_faces = 1 + if is_list_of_lists_empty(faces) & is_list_of_lists_empty(poses): + return (faces, poses) - if isinstance(frame_face_pose[0], list): - n_poses = len(frame_face_pose) - else: - n_poses = 1 - - frame_overlap = np.zeros([n_faces, n_poses]) - - if (n_faces == 1) & (n_poses > 1): - b1 = BBox(frame_face[:-1]) - - for pose_idx in range(n_poses): - b2 = BBox(frame_face_pose[pose_idx][:-1]) - frame_overlap[0, pose_idx] = b1.overlap(b2) - matched_pose_index = np.where( - frame_overlap[0, :] == frame_overlap[0, :].max() - )[0][0] - overlap_faces.append(frame_face) - overlap_poses.append(frame_pose[matched_pose_index]) - - elif (n_faces > 1) & (n_poses == 1): - b2 = BBox(frame_face_pose[:-1]) - for face_idx in range(n_faces): - b1 = BBox(frame_face[face_idx][:-1]) - frame_overlap[face_idx, 0] = b1.overlap(b2) - matched_face_index = np.where( - frame_overlap[:, 0] == frame_overlap[:, 0].max() - )[0][0] - new_poses = [] - for f_idx in range(n_faces): - if f_idx == matched_face_index: - new_poses.append(frame_pose[0]) - else: - new_poses.append(np.ones(3) * np.nan) - overlap_faces.append(frame_face) - overlap_poses.append(new_poses) + else: + + overlap_faces = [] + overlap_poses = [] + for frame_face, frame_face_pose, frame_pose in zip(faces, faces_pose, poses): + if isinstance(frame_face[0], list): + n_faces = len(frame_face) + else: + n_faces = 1 + + if isinstance(frame_face_pose[0], list): + n_poses = len(frame_face_pose) + else: + n_poses = 1 + + frame_overlap = np.zeros([n_faces, n_poses]) + + if (n_faces == 1) & (n_poses > 1): + b1 = BBox(frame_face[0][:-1]) - else: - for face_idx in range(n_faces): - b1 = BBox(frame_face[face_idx][:-1]) for pose_idx in range(n_poses): b2 = BBox(frame_face_pose[pose_idx][:-1]) - frame_overlap[face_idx, pose_idx] = b1.overlap(b2) + frame_overlap[0, pose_idx] = b1.overlap(b2) + matched_pose_index = np.where( + frame_overlap[0, :] == frame_overlap[0, :].max() + )[0][0] + overlap_faces.append(frame_face) + overlap_poses.append(frame_pose[matched_pose_index]) + + elif (n_faces > 1) & (n_poses == 1): + b2 = BBox(frame_face_pose[0][:-1]) + for face_idx in range(n_faces): + b1 = BBox(frame_face[face_idx][:-1]) + frame_overlap[face_idx, 0] = b1.overlap(b2) + matched_face_index = np.where( + frame_overlap[:, 0] == frame_overlap[:, 0].max() + )[0][0] + new_poses = [] + for f_idx in range(n_faces): + if f_idx == matched_face_index: + new_poses.append(frame_pose[0]) + else: + new_poses.append(np.ones(3) * np.nan) + overlap_faces.append(frame_face) + overlap_poses.append(new_poses) - overlap_faces_frame = [] - overlap_poses_frame = [] - if n_faces < n_poses: + else: for face_idx in range(n_faces): - pose_idx = np.where( - frame_overlap[face_idx, :] == frame_overlap[face_idx, :].max() - )[0][0] - overlap_faces_frame.append(frame_face[face_idx]) - overlap_poses_frame.append(frame_pose[pose_idx]) - elif n_faces > n_poses: - matched_pose_index = [] - for pose_idx in range(n_poses): - matched_pose_index.append( - np.where( - frame_overlap[:, pose_idx] - == frame_overlap[:, pose_idx].max() + b1 = BBox(frame_face[face_idx][:-1]) + for pose_idx in range(n_poses): + b2 = BBox(frame_face_pose[pose_idx][:-1]) + frame_overlap[face_idx, pose_idx] = b1.overlap(b2) + + overlap_faces_frame = [] + overlap_poses_frame = [] + if n_faces < n_poses: + for face_idx in range(n_faces): + pose_idx = np.where( + frame_overlap[face_idx, :] + == frame_overlap[face_idx, :].max() )[0][0] - ) - for face_idx in range(n_faces): - overlap_faces_frame.append(frame_face[face_idx]) - if face_idx in matched_pose_index: - overlap_poses_frame.append( - frame_pose[ - np.where( - frame_overlap[face_idx, :] - == frame_overlap[face_idx, :].max() - )[0][0] - ] + overlap_faces_frame.append(frame_face[face_idx]) + overlap_poses_frame.append(frame_pose[pose_idx]) + elif n_faces > n_poses: + matched_pose_index = [] + for pose_idx in range(n_poses): + matched_pose_index.append( + np.where( + frame_overlap[:, pose_idx] + == frame_overlap[:, pose_idx].max() + )[0][0] ) - else: - overlap_poses_frame.append(np.ones(3) * np.nan) - pose_idx = np.where( - frame_overlap[face_idx, :] == frame_overlap[face_idx, :].max() - )[0][0] - overlap_faces.append(overlap_faces_frame) - overlap_poses.append(overlap_poses_frame) - - return overlap_faces, overlap_poses + for face_idx in range(n_faces): + overlap_faces_frame.append(frame_face[face_idx]) + if face_idx in matched_pose_index: + overlap_poses_frame.append( + frame_pose[ + np.where( + frame_overlap[face_idx, :] + == frame_overlap[face_idx, :].max() + )[0][0] + ] + ) + else: + overlap_poses_frame.append(np.ones(3) * np.nan) + pose_idx = np.where( + frame_overlap[face_idx, :] + == frame_overlap[face_idx, :].max() + )[0][0] + elif n_faces == n_poses: + overlap_faces_frame = frame_face + overlap_poses_frame = frame_pose + + overlap_faces.append(overlap_faces_frame) + overlap_poses.append(overlap_poses_frame) + + return (overlap_faces, overlap_poses) diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py index 35a658ba..3287526f 100644 --- a/feat/tests/test_detector_core.py +++ b/feat/tests/test_detector_core.py @@ -1,4 +1,4 @@ -from feat.detector import Detector +from feat.detector import Detector, _match_faces_to_poses from feat.data import Fex from feat.utils.io import get_test_data_path import os @@ -190,7 +190,7 @@ def test_detect_video(default_detector, single_face_mov): assert out.happiness.values.max() > 0 -def test_detect_mismatch_face_pose(default_detector): +def test_detect_mismatch_face_pose(): # Multiple Faces, 1 pose faces = [ [ @@ -210,8 +210,8 @@ def test_detect_mismatch_face_pose(default_detector): ], ] ] - faces_pose = [[46.0, 46.0, 66.0, 71.0, 0.99272925]] - poses = [[np.array([-3.72766398, 10.9359162, -3.19862351])]] + faces_pose = [[[46.0, 46.0, 66.0, 71.0, 0.99272925]]] + poses = [[[-3.72766398, 10.9359162, -3.19862351]]] new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) assert len(new_faces[0]) == len(new_poses[0]) @@ -219,11 +219,13 @@ def test_detect_mismatch_face_pose(default_detector): # 1 face, multiple poses faces = [ [ - 45.34465026855469, - 49.546714782714844, - 63.04056167602539, - 70.38599395751953, - 0.95337886, + [ + 45.34465026855469, + 49.546714782714844, + 63.04056167602539, + 70.38599395751953, + 0.95337886, + ] ] ] @@ -239,11 +241,11 @@ def test_detect_mismatch_face_pose(default_detector): poses = [ [ - np.array([[-5.90236694, -2.81686444, -5.38250827]]), - np.array([[18.3324545, 7.2330487, 2.70649852]]), - np.array([[12.04520545, 5.91369713, 6.13698383]]), - np.array([[1.10688262, 1.56339815, -0.91693287]]), - np.array([[-3.72766398, 10.9359162, -3.19862351]]), + [-5.90236694, -2.81686444, -5.38250827], + [18.3324545, 7.2330487, 2.70649852], + [12.04520545, 5.91369713, 6.13698383], + [1.10688262, 1.56339815, -0.91693287], + [-3.72766398, 10.9359162, -3.19862351], ] ] @@ -283,11 +285,11 @@ def test_detect_mismatch_face_pose(default_detector): poses = [ [ - np.array([[-5.90236694, -2.81686444, -5.38250827]]), - np.array([[18.3324545, 7.2330487, 2.70649852]]), - np.array([[12.04520545, 5.91369713, 6.13698383]]), - np.array([[1.10688262, 1.56339815, -0.91693287]]), - np.array([[-3.72766398, 10.9359162, -3.19862351]]), + [-5.90236694, -2.81686444, -5.38250827], + [18.3324545, 7.2330487, 2.70649852], + [12.04520545, 5.91369713, 6.13698383], + [1.10688262, 1.56339815, -0.91693287], + [-3.72766398, 10.9359162, -3.19862351], ] ] @@ -326,8 +328,8 @@ def test_detect_mismatch_face_pose(default_detector): poses = [ [ - np.array([[-5.90236694, -2.81686444, -5.38250827]]), - np.array([[18.3324545, 7.2330487, 2.70649852]]), + [-5.90236694, -2.81686444, -5.38250827], + [18.3324545, 7.2330487, 2.70649852], ] ] diff --git a/feat/tests/test_pretrained_models.py b/feat/tests/test_pretrained_models.py index 7777226b..e829779a 100644 --- a/feat/tests/test_pretrained_models.py +++ b/feat/tests/test_pretrained_models.py @@ -202,7 +202,7 @@ def test_img2pose_facepose( default_detector.change_model(facepose_model="img2pose") poses = default_detector.detect_facepose(single_face_img_data) - assert np.allclose(poses, [0.86, -3.80, 6.60], atol=0.1) + assert np.allclose(poses["poses"], [0.86, -3.80, 6.60], atol=0.1) # Test DOF kwarg facepose_model_kwargs = {"RETURN_DIM": 6} @@ -215,10 +215,10 @@ def test_img2pose_facepose( # Also run directly poses = new_detector.detect_facepose(single_face_img_data) - assert len(poses[0][0].squeeze()) == 6 + assert len(poses["poses"][0][0].squeeze()) == 6 def test_img2pose_c_facepose(self, default_detector, single_face_img_data): default_detector.change_model(facepose_model="img2pose-c") poses = default_detector.detect_facepose(single_face_img_data) - assert np.allclose(poses, [0.86, -3.80, 6.60], atol=0.1) + assert np.allclose(poses["poses"], [0.86, -3.80, 6.60], atol=0.1) diff --git a/feat/utils/__init__.py b/feat/utils/__init__.py index 60ea78ab..0d400097 100644 --- a/feat/utils/__init__.py +++ b/feat/utils/__init__.py @@ -151,3 +151,11 @@ def set_torch_device(device="cpu"): else: return device + + +def is_list_of_lists_empty(list_of_lists): + """Helper function to check if list of lists is empty""" + if not any(list_of_lists): + return True + else: + return False From 3489f394df5c0b5f5096d66f20863eff1cdf7f19 Mon Sep 17 00:00:00 2001 From: ljchang Date: Mon, 23 Jan 2023 22:47:17 -0500 Subject: [PATCH 08/22] fixed test for matching face and pose detection --- feat/detector.py | 4 +++- feat/tests/test_detector_core.py | 5 ++++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index ab807f69..6d842d73 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -1035,8 +1035,10 @@ def _match_faces_to_poses(faces, faces_pose, poses): matched_pose_index = np.where( frame_overlap[0, :] == frame_overlap[0, :].max() )[0][0] + print(frame_face) + print([frame_pose[matched_pose_index]]) overlap_faces.append(frame_face) - overlap_poses.append(frame_pose[matched_pose_index]) + overlap_poses.append([frame_pose[matched_pose_index]]) elif (n_faces > 1) & (n_poses == 1): b2 = BBox(frame_face_pose[0][:-1]) diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py index 3287526f..9734c014 100644 --- a/feat/tests/test_detector_core.py +++ b/feat/tests/test_detector_core.py @@ -215,6 +215,7 @@ def test_detect_mismatch_face_pose(): new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) assert len(new_faces[0]) == len(new_poses[0]) + assert len(new_faces[0]) == 2 # 1 face, multiple poses faces = [ @@ -250,7 +251,7 @@ def test_detect_mismatch_face_pose(): ] new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) - assert len(new_faces[0]) == 5 + assert len(new_faces[0]) == 1 assert len(new_poses[0]) == 1 # 2 Faces, 5 Poses @@ -295,6 +296,7 @@ def test_detect_mismatch_face_pose(): new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) assert len(new_faces[0]) == len(new_poses[0]) + assert len(new_faces[0]) == 2 # 5 Faces, 2 Poses faces = [ @@ -335,3 +337,4 @@ def test_detect_mismatch_face_pose(): new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) assert len(new_faces[0]) == len(new_poses[0]) + assert len(new_faces[0]) == 2 From 0342941eb487a4a7ac8e1d168713d0526936d69f Mon Sep 17 00:00:00 2001 From: ljchang Date: Mon, 23 Jan 2023 22:55:27 -0500 Subject: [PATCH 09/22] Fixed matching face to pose test --- feat/tests/test_detector_core.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py index 9734c014..6e36056a 100644 --- a/feat/tests/test_detector_core.py +++ b/feat/tests/test_detector_core.py @@ -337,4 +337,4 @@ def test_detect_mismatch_face_pose(): new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) assert len(new_faces[0]) == len(new_poses[0]) - assert len(new_faces[0]) == 2 + assert len(new_faces[0]) == 5 From 585361530af92e71f66bd4a40f1ea5b102e102bb Mon Sep 17 00:00:00 2001 From: ljchang Date: Wed, 25 Jan 2023 00:12:11 -0500 Subject: [PATCH 10/22] handle frames with no face using batching --- feat/detector.py | 329 +++++++++++++++++++++++++---------------------- 1 file changed, 176 insertions(+), 153 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index 6d842d73..576d47ec 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -40,7 +40,6 @@ import warnings from tqdm import tqdm import torchvision.transforms as transforms -from collections import ChainMap # Supress sklearn warning about pickled estimators and diff sklearn versions warnings.filterwarnings("ignore", category=UserWarning, module="sklearn") @@ -535,9 +534,11 @@ def detect_aus(self, frame, landmarks, **au_model_kwargs): if self["au_model"].lower() in ["svm", "xgb"]: # transform = Grayscale(3) # frame = transform(frame) - hog_arr, new_lands = self._batch_hog(frames=frame, landmarks=landmarks) + hog_features, new_landmarks = self._batch_hog( + frames=frame, landmarks=landmarks + ) au_predictions = self.au_model.detect_au( - frame=hog_arr, landmarks=new_lands, **au_model_kwargs + frame=hog_features, landmarks=new_landmarks, **au_model_kwargs ) else: au_predictions = self.au_model.detect_au( @@ -552,44 +553,40 @@ def _batch_hog(self, frames, landmarks): frames is a batch of frames """ - len_index = [len(aa) for aa in landmarks] - lenth_cumu = np.cumsum(len_index) - lenth_cumu2 = np.insert(lenth_cumu, 0, 0) - new_lands_list = [] - flat_land = [item for sublist in landmarks for item in sublist] - hogs_arr = None - - for i in range(len(flat_land)): - - frame_assignment = np.where(i < lenth_cumu)[0][0] + hog_features = [] + new_landmark_frames = [] + for i, frame_landmark in enumerate(landmarks): + if len(frame_landmark) != 0: + new_landmarks_faces = [] + for j in range(len(frame_landmark)): + convex_hull, new_landmark = extract_face_from_landmarks( + frame=frames[i], + landmarks=frame_landmark[j], + face_size=112, + ) - convex_hull, new_lands = extract_face_from_landmarks( - frame=frames[frame_assignment], - landmarks=flat_land[i], - face_size=112, - ) + hog_features.append( + hog( + transforms.ToPILImage()(convex_hull[0]), + orientations=8, + pixels_per_cell=(8, 8), + cells_per_block=(2, 2), + visualize=False, + channel_axis=-1, + ).reshape(1, -1) + ) - hogs = hog( - transforms.ToPILImage()(convex_hull[0]), - orientations=8, - pixels_per_cell=(8, 8), - cells_per_block=(2, 2), - visualize=False, - channel_axis=-1, - ).reshape(1, -1) - - if hogs_arr is None: - hogs_arr = hogs + new_landmarks_faces.append(new_landmark) + new_landmark_frames.append(new_landmarks_faces) else: - hogs_arr = np.concatenate([hogs_arr, hogs], 0) + hog_features.append( + np.zeros((1, 5408)) + ) # LC: Need to confirm this size is fixed. + new_landmark_frames.append([np.zeros((68, 2))]) - new_lands_list.append(new_lands) + hog_features = np.concatenate(hog_features) - new_lands = [] - for i in range(len(lenth_cumu)): - new_lands.append(new_lands_list[lenth_cumu2[i] : (lenth_cumu2[i + 1])]) - - return (hogs_arr, new_lands) + return (hog_features, new_landmark_frames) def detect_emotions(self, frame, facebox, landmarks, **emotion_model_kwargs): """Detect emotions from image or video frame @@ -627,11 +624,15 @@ def detect_emotions(self, frame, facebox, landmarks, **emotion_model_kwargs): ) elif self.info["emotion_model"].lower() == "svm": - hog_arr, new_lands = self._batch_hog(frames=frame, landmarks=landmarks) + hog_features, new_landmarks = self._batch_hog( + frames=frame, landmarks=landmarks + ) return _convert_detector_output( landmarks, self.emotion_model.detect_emo( - frame=hog_arr, landmarks=new_lands, **emotion_model_kwargs + frame=hog_features, + landmarks=new_landmarks, + **emotion_model_kwargs, ), ) @@ -712,8 +713,11 @@ def detect_image( threshold=face_detection_threshold, **face_model_kwargs, ) + landmarks = self.detect_landmarks( - batch_data["Image"], detected_faces=faces, **landmark_model_kwargs + batch_data["Image"], + detected_faces=faces, + **landmark_model_kwargs, ) poses_dict = self.detect_facepose( batch_data["Image"], landmarks, **facepose_model_kwargs @@ -849,123 +853,135 @@ def _create_fex( logging.info("creating fex output...") - if is_list_of_lists_empty(faces): - return Fex( - pd.DataFrame( - pd.Series( - ChainMap( - {x: np.nan for x in self.info["face_detection_columns"]}, - {x: np.nan for x in self.info["facepose_model_columns"]}, - {x: np.nan for x in self.info["face_landmark_columns"]}, - {x: np.nan for x in self.info["au_presence_columns"]}, - {x: np.nan for x in self.info["emotion_model_columns"]}, - { - FEAT_TIME_COLUMNS[0]: frame_counter, - "input": file_names[0], - }, - ) - ) - ).T, - au_columns=self.info["au_presence_columns"], - emotion_columns=self.info["emotion_model_columns"], - facebox_columns=self.info["face_detection_columns"], - landmark_columns=self.info["face_landmark_columns"], - facepose_columns=self.info["facepose_model_columns"], - detector="Feat", - face_model=self.info["face_model"], - landmark_model=self.info["landmark_model"], - au_model=self.info["au_model"], - emotion_model=self.info["emotion_model"], - facepose_model=self.info["facepose_model"], - ) - else: - files = [[f] * n for f, n in zip(file_names, [len(x) for x in faces])] + out = [] + for i, frame in enumerate(faces): + if not frame: + facebox_df = pd.DataFrame( + {x: np.nan for x in self.info["face_detection_columns"]}, + columns=self.info["face_detection_columns"], + index=[i], + ) + facepose_df = pd.DataFrame( + {x: np.nan for x in self.info["facepose_model_columns"]}, + columns=self.info["facepose_model_columns"], + index=[i], + ) + landmarks_df = pd.DataFrame( + {x: np.nan for x in self.info["face_landmark_columns"]}, + columns=self.info["face_landmark_columns"], + index=[i], + ) + aus_df = pd.DataFrame( + {x: np.nan for x in self.info["au_presence_columns"]}, + columns=self.info["au_presence_columns"], + index=[i], + ) + emotions_df = pd.DataFrame( + {x: np.nan for x in self.info["emotion_model_columns"]}, + columns=self.info["emotion_model_columns"], + index=[i], + ) + input_df = pd.DataFrame(file_names[i], columns=["input"], index=[i]) + tmp_df = pd.concat( + [ + facebox_df, + landmarks_df, + facepose_df, + aus_df, + emotions_df, + input_df, + ], + axis=1, + ) + if isinstance(frame_counter, (list)): + tmp_df[FEAT_TIME_COLUMNS] = frame_counter[i] + else: + tmp_df[FEAT_TIME_COLUMNS] = frame_counter + i + out.append(tmp_df) - # Convert to Pandas Format - out = [] - for i, frame in enumerate(faces): - for j, face_in_frame in enumerate(frame): - facebox_df = pd.DataFrame( + for j, face_in_frame in enumerate(frame): + facebox_df = pd.DataFrame( + [ [ - [ - face_in_frame[0], - face_in_frame[1], - face_in_frame[2] - face_in_frame[0], - face_in_frame[3] - face_in_frame[1], - face_in_frame[4], - ] - ], - columns=self.info["face_detection_columns"], - index=[j], - ) + face_in_frame[0], + face_in_frame[1], + face_in_frame[2] - face_in_frame[0], + face_in_frame[3] - face_in_frame[1], + face_in_frame[4], + ] + ], + columns=self.info["face_detection_columns"], + index=[j], + ) - facepose_df = pd.DataFrame( - [poses[i][j]], - columns=self.info["facepose_model_columns"], - index=[j], - ) + facepose_df = pd.DataFrame( + [poses[i][j]], + columns=self.info["facepose_model_columns"], + index=[j], + ) - landmarks_df = pd.DataFrame( - [landmarks[i][j].flatten(order="F")], - columns=self.info["face_landmark_columns"], - index=[j], - ) + landmarks_df = pd.DataFrame( + [landmarks[i][j].flatten(order="F")], + columns=self.info["face_landmark_columns"], + index=[j], + ) - aus_df = pd.DataFrame( - aus[i][j, :].reshape(1, len(self["au_presence_columns"])), - columns=self.info["au_presence_columns"], - index=[j], - ) + aus_df = pd.DataFrame( + aus[i][j, :].reshape(1, len(self["au_presence_columns"])), + columns=self.info["au_presence_columns"], + index=[j], + ) - emotions_df = pd.DataFrame( - emotions[i][j, :].reshape( - 1, len(self.info["emotion_model_columns"]) - ), - columns=self.info["emotion_model_columns"], - index=[j], - ) + emotions_df = pd.DataFrame( + emotions[i][j, :].reshape( + 1, len(self.info["emotion_model_columns"]) + ), + columns=self.info["emotion_model_columns"], + index=[j], + ) - input_df = pd.DataFrame( - files[i][j], - columns=["input"], - index=[j], - ) + input_df = pd.DataFrame( + file_names[i], + columns=["input"], + index=[j], + ) - tmp_df = pd.concat( - [ - facebox_df, - landmarks_df, - facepose_df, - aus_df, - emotions_df, - input_df, - ], - axis=1, - ) + tmp_df = pd.concat( + [ + facebox_df, + landmarks_df, + facepose_df, + aus_df, + emotions_df, + input_df, + ], + axis=1, + ) - if isinstance(frame_counter, (list)): - tmp_df[FEAT_TIME_COLUMNS] = frame_counter[i] - else: - tmp_df[FEAT_TIME_COLUMNS] = frame_counter + i - out.append(tmp_df) - out = pd.concat(out) - out.reset_index(drop=True, inplace=True) - - # TODO: Add in support for gaze_columns - return Fex( - out, - au_columns=self.info["au_presence_columns"], - emotion_columns=FEAT_EMOTION_COLUMNS, - facebox_columns=FEAT_FACEBOX_COLUMNS, - landmark_columns=openface_2d_landmark_columns, - facepose_columns=self.info["facepose_model_columns"], - detector="Feat", - face_model=self.info["face_model"], - landmark_model=self.info["landmark_model"], - au_model=self.info["au_model"], - emotion_model=self.info["emotion_model"], - facepose_model=self.info["facepose_model"], - ) + if isinstance(frame_counter, (list)): + tmp_df[FEAT_TIME_COLUMNS] = frame_counter[i] + else: + tmp_df[FEAT_TIME_COLUMNS] = frame_counter + i + out.append(tmp_df) + + out = pd.concat(out) + out.reset_index(drop=True, inplace=True) + + # TODO: Add in support for gaze_columns + return Fex( + out, + au_columns=self.info["au_presence_columns"], + emotion_columns=self.info["emotion_model_columns"], + facebox_columns=self.info["face_detection_columns"], + landmark_columns=self.info["face_landmark_columns"], + facepose_columns=self.info["facepose_model_columns"], + detector="Feat", + face_model=self.info["face_model"], + landmark_model=self.info["landmark_model"], + au_model=self.info["au_model"], + emotion_model=self.info["emotion_model"], + facepose_model=self.info["facepose_model"], + ) def _convert_detector_output(detected_faces, detector_results): @@ -1006,7 +1022,8 @@ def _match_faces_to_poses(faces, faces_pose, poses): "Make sure the number of batches in faces and poses is the same." ) - if is_list_of_lists_empty(faces) & is_list_of_lists_empty(poses): + if is_list_of_lists_empty(faces): + # Currently assuming no faces if no face is detected. Not running pose return (faces, poses) else: @@ -1014,19 +1031,27 @@ def _match_faces_to_poses(faces, faces_pose, poses): overlap_faces = [] overlap_poses = [] for frame_face, frame_face_pose, frame_pose in zip(faces, faces_pose, poses): - if isinstance(frame_face[0], list): + if not frame_face: + n_faces = 0 + elif isinstance(frame_face[0], list): n_faces = len(frame_face) else: n_faces = 1 - if isinstance(frame_face_pose[0], list): + if not frame_face_pose: + n_poses = 0 + elif isinstance(frame_face_pose[0], list): n_poses = len(frame_face_pose) else: n_poses = 1 frame_overlap = np.zeros([n_faces, n_poses]) - if (n_faces == 1) & (n_poses > 1): + if n_faces == 0: + overlap_faces.append([]) + overlap_poses.append([]) + + elif (n_faces == 1) & (n_poses > 1): b1 = BBox(frame_face[0][:-1]) for pose_idx in range(n_poses): @@ -1035,8 +1060,6 @@ def _match_faces_to_poses(faces, faces_pose, poses): matched_pose_index = np.where( frame_overlap[0, :] == frame_overlap[0, :].max() )[0][0] - print(frame_face) - print([frame_pose[matched_pose_index]]) overlap_faces.append(frame_face) overlap_poses.append([frame_pose[matched_pose_index]]) From 16b1c4abb694752b238f0a6389871f493acf78b1 Mon Sep 17 00:00:00 2001 From: ljchang Date: Wed, 25 Jan 2023 00:30:27 -0500 Subject: [PATCH 11/22] fixed img2pose test --- feat/tests/test_pretrained_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feat/tests/test_pretrained_models.py b/feat/tests/test_pretrained_models.py index e829779a..72bf4e6f 100644 --- a/feat/tests/test_pretrained_models.py +++ b/feat/tests/test_pretrained_models.py @@ -215,7 +215,7 @@ def test_img2pose_facepose( # Also run directly poses = new_detector.detect_facepose(single_face_img_data) - assert len(poses["poses"][0][0].squeeze()) == 6 + assert len(poses["poses"][0][0]) == 6 def test_img2pose_c_facepose(self, default_detector, single_face_img_data): From 1d6e4442977047a2ceaaeec38bac981d812c1774 Mon Sep 17 00:00:00 2001 From: ljchang Date: Wed, 25 Jan 2023 09:15:22 -0500 Subject: [PATCH 12/22] fixed minor bug with video detection facebox --- feat/detector.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/feat/detector.py b/feat/detector.py index 576d47ec..75252b26 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -809,8 +809,9 @@ def detect_video( emotions = self.detect_emotions( batch_data["Image"], faces, landmarks, **detector_kwargs ) - frames = list(batch_data["Frame"].numpy()) + faces = _inverse_face_transform(faces, batch_data) landmarks = _inverse_landmark_transform(landmarks, batch_data) + frames = list(batch_data["Frame"].numpy()) # match faces to poses - sometimes face detector finds different faces than pose detector. faces, poses = _match_faces_to_poses( From 14831100b9f1c22bb3a43d6ee824c8c4b3484f09 Mon Sep 17 00:00:00 2001 From: ljchang Date: Wed, 25 Jan 2023 09:15:32 -0500 Subject: [PATCH 13/22] added more tests with no faces --- feat/tests/test_detector_core.py | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py index 6e36056a..74999ae7 100644 --- a/feat/tests/test_detector_core.py +++ b/feat/tests/test_detector_core.py @@ -129,10 +129,24 @@ def test_detect_multi_img_no_face_batching(default_detector, no_face_img): assert out.shape == (5, 173) -def test_detect_multi_img_mixed_no_face(default_detector, no_face_img, single_face_img): +def test_detect_multi_img_mixed_no_face( + default_detector, no_face_img, single_face_img, multi_face_img +): """Test detection of a single image with no face. Default detector returns 173 attributes""" - out = default_detector.detect_image([no_face_img, single_face_img] * 3) - assert out.shape == (6, 173) + out = default_detector.detect_image( + [single_face_img, no_face_img, multi_face_img] * 2 + ) + assert out.shape == (12, 173) + + +def test_detect_multi_img_mixed_no_face_batching( + default_detector, no_face_img, single_face_img, multi_face_img +): + """Test detection of a single image with no face. Default detector returns 173 attributes""" + out = default_detector.detect_image( + [single_face_img, no_face_img, multi_face_img] * 2, batch_size=4 + ) + assert out.shape == (12, 173) # Single images From b58742aa389b0a59779898d4e41ee5f7b81f487b Mon Sep 17 00:00:00 2001 From: ljchang Date: Wed, 25 Jan 2023 09:24:04 -0500 Subject: [PATCH 14/22] updated test --- feat/tests/test_detector_core.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py index 74999ae7..8188ced3 100644 --- a/feat/tests/test_detector_core.py +++ b/feat/tests/test_detector_core.py @@ -136,7 +136,7 @@ def test_detect_multi_img_mixed_no_face( out = default_detector.detect_image( [single_face_img, no_face_img, multi_face_img] * 2 ) - assert out.shape == (12, 173) + assert out.shape == (14, 173) def test_detect_multi_img_mixed_no_face_batching( @@ -144,9 +144,11 @@ def test_detect_multi_img_mixed_no_face_batching( ): """Test detection of a single image with no face. Default detector returns 173 attributes""" out = default_detector.detect_image( - [single_face_img, no_face_img, multi_face_img] * 2, batch_size=4 + [single_face_img, no_face_img, multi_face_img] * 2, + batch_size=4, + output_size=300, ) - assert out.shape == (12, 173) + assert out.shape == (14, 173) # Single images From c953d293cb6c2a3bd39d3f2c1279bd6ea1bcc69c Mon Sep 17 00:00:00 2001 From: ljchang Date: Wed, 25 Jan 2023 09:30:47 -0500 Subject: [PATCH 15/22] updated docstrings --- feat/detector.py | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index 75252b26..e451d065 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -550,7 +550,14 @@ def detect_aus(self, frame, landmarks, **au_model_kwargs): def _batch_hog(self, frames, landmarks): """ Helper function used in batch processing hog features - frames is a batch of frames + + Args: + frames: a batch of frames + landmarks: a list of list of detected landmarks + + Returns: + hog_features: a numpy array of hog features for each detected landmark + landmarks: updated landmarks """ hog_features = [] @@ -1010,12 +1017,22 @@ def _convert_detector_output(detected_faces, detector_results): def _match_faces_to_poses(faces, faces_pose, poses): - """Function to match list of lists of faces and poses based on overlap in bounding boxes. + """Helper function to match list of lists of faces and poses based on overlap in bounding boxes. Sometimes the face detector finds different faces than the pose detector unless the user is using the same detector (i.e., img2pose). This function will match the faces and poses and will return nans if more faces are detected then poses. + Will only return poses that match faces even if more faces are detected by pose detector. + + Args: + faces (list): list of lists of face bounding boxes from face detector + faces_pose (list): list of lists of face bounding boxes from pose detector + poses (list): list of lists of poses from pose detector + + Returns: + faces (list): list of list of faces that have been matched to poses + poses (list): list of list of poses that have been matched to faces """ if len(faces) != len(faces_pose): From 6df6d978f9e413be9d51899eec429f12be15afa5 Mon Sep 17 00:00:00 2001 From: ljchang Date: Wed, 1 Feb 2023 18:09:48 -0500 Subject: [PATCH 16/22] small bug fix with with empty poses in matching --- feat/detector.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index e451d065..4c4e8024 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -1137,10 +1137,6 @@ def _match_faces_to_poses(faces, faces_pose, poses): ) else: overlap_poses_frame.append(np.ones(3) * np.nan) - pose_idx = np.where( - frame_overlap[face_idx, :] - == frame_overlap[face_idx, :].max() - )[0][0] elif n_faces == n_poses: overlap_faces_frame = frame_face overlap_poses_frame = frame_pose From db49c63b953860be0997079ffbdad87ec2910ef4 Mon Sep 17 00:00:00 2001 From: ejolly Date: Fri, 3 Feb 2023 15:55:03 -0500 Subject: [PATCH 17/22] minor refactors to move new functions to class methods --- feat/detector.py | 276 ++++++++++++++++--------------- feat/tests/test_detector_core.py | 20 ++- feat/utils/__init__.py | 7 +- 3 files changed, 157 insertions(+), 146 deletions(-) diff --git a/feat/detector.py b/feat/detector.py index 4c4e8024..2d6e01a7 100644 --- a/feat/detector.py +++ b/feat/detector.py @@ -545,7 +545,7 @@ def detect_aus(self, frame, landmarks, **au_model_kwargs): frame, landmarks=landmarks, **au_model_kwargs ) - return _convert_detector_output(landmarks, au_predictions) + return self._convert_detector_output(landmarks, au_predictions) def _batch_hog(self, frames, landmarks): """ @@ -623,7 +623,7 @@ def detect_emotions(self, frame, facebox, landmarks, **emotion_model_kwargs): return facebox else: if self.info["emotion_model"].lower() == "resmasknet": - return _convert_detector_output( + return self._convert_detector_output( facebox, self.emotion_model.detect_emo( frame, facebox, **emotion_model_kwargs @@ -634,7 +634,7 @@ def detect_emotions(self, frame, facebox, landmarks, **emotion_model_kwargs): hog_features, new_landmarks = self._batch_hog( frames=frame, landmarks=landmarks ) - return _convert_detector_output( + return self._convert_detector_output( landmarks, self.emotion_model.detect_emo( frame=hog_features, @@ -715,6 +715,7 @@ def detect_image( try: batch_output = [] for batch_id, batch_data in enumerate(tqdm(data_loader)): + # TODO: Refactor me to use waterfall faces = self.detect_faces( batch_data["Image"], threshold=face_detection_threshold, @@ -738,7 +739,7 @@ def detect_image( landmarks = _inverse_landmark_transform(landmarks, batch_data) # match faces to poses - sometimes face detector finds different faces than pose detector. - faces, poses = _match_faces_to_poses( + faces, poses = self._match_faces_to_poses( faces, poses_dict["faces"], poses_dict["poses"] ) @@ -802,6 +803,7 @@ def detect_video( ) batch_output = [] + # TODO: refactor into waterfall for batch_data in tqdm(data_loader): faces = self.detect_faces( batch_data["Image"], @@ -821,7 +823,7 @@ def detect_video( frames = list(batch_data["Frame"].numpy()) # match faces to poses - sometimes face detector finds different faces than pose detector. - faces, poses = _match_faces_to_poses( + faces, poses = self._match_faces_to_poses( faces, poses_dict["faces"], poses_dict["poses"] ) @@ -991,157 +993,159 @@ def _create_fex( facepose_model=self.info["facepose_model"], ) + @staticmethod + def _convert_detector_output(detected_faces, detector_results): + """ + Helper function to convert AU/Emotion detector output into frame by face list of lists. + Either face or landmark detector list of list outputs can be used. -def _convert_detector_output(detected_faces, detector_results): - """ - Helper function to convert AU/Emotion detector output into frame by face list of lists. - Either face or landmark detector list of list outputs can be used. + Args: + detected_faces (list): list of lists output from face/landmark detector + au_results (np.array):, results from au/emotion detectors - Args: - detected_faces (list): list of lists output from face/landmark detector - au_results (np.array):, results from au/emotion detectors + Returns: + list_concat: (list of list). The list which contains the number of faces. for example + if you process 2 frames and each frame contains 4 faces, it will return: + [[xxx,xxx,xxx,xxx],[xxx,xxx,xxx,xxx]] + """ - Returns: - list_concat: (list of list). The list which contains the number of faces. for example - if you process 2 frames and each frame contains 4 faces, it will return: - [[xxx,xxx,xxx,xxx],[xxx,xxx,xxx,xxx]] - """ + length_index = [len(x) for x in detected_faces] - length_index = [len(x) for x in detected_faces] + list_concat = [] + new_lens = np.insert(np.cumsum(length_index), 0, 0) + for ij in range(len(length_index)): + list_concat.append(detector_results[new_lens[ij] : new_lens[ij + 1], :]) + return list_concat - list_concat = [] - new_lens = np.insert(np.cumsum(length_index), 0, 0) - for ij in range(len(length_index)): - list_concat.append(detector_results[new_lens[ij] : new_lens[ij + 1], :]) - return list_concat + @staticmethod + def _match_faces_to_poses(faces, faces_pose, poses): + """Helper function to match list of lists of faces and poses based on overlap in bounding boxes. + Sometimes the face detector finds different faces than the pose detector unless the user + is using the same detector (i.e., img2pose). -def _match_faces_to_poses(faces, faces_pose, poses): - """Helper function to match list of lists of faces and poses based on overlap in bounding boxes. + This function will match the faces and poses and will return nans if more faces are detected then poses. + Will only return poses that match faces even if more faces are detected by pose detector. - Sometimes the face detector finds different faces than the pose detector unless the user - is using the same detector (i.e., img2pose). + Args: + faces (list): list of lists of face bounding boxes from face detector + faces_pose (list): list of lists of face bounding boxes from pose detector + poses (list): list of lists of poses from pose detector - This function will match the faces and poses and will return nans if more faces are detected then poses. - Will only return poses that match faces even if more faces are detected by pose detector. + Returns: + faces (list): list of list of faces that have been matched to poses + poses (list): list of list of poses that have been matched to faces + """ - Args: - faces (list): list of lists of face bounding boxes from face detector - faces_pose (list): list of lists of face bounding boxes from pose detector - poses (list): list of lists of poses from pose detector + if len(faces) != len(faces_pose): + raise ValueError( + "Make sure the number of batches in faces and poses is the same." + ) - Returns: - faces (list): list of list of faces that have been matched to poses - poses (list): list of list of poses that have been matched to faces - """ + if is_list_of_lists_empty(faces): + # Currently assuming no faces if no face is detected. Not running pose + return (faces, poses) - if len(faces) != len(faces_pose): - raise ValueError( - "Make sure the number of batches in faces and poses is the same." - ) + else: - if is_list_of_lists_empty(faces): - # Currently assuming no faces if no face is detected. Not running pose - return (faces, poses) + overlap_faces = [] + overlap_poses = [] + for frame_face, frame_face_pose, frame_pose in zip( + faces, faces_pose, poses + ): + if not frame_face: + n_faces = 0 + elif isinstance(frame_face[0], list): + n_faces = len(frame_face) + else: + n_faces = 1 - else: + if not frame_face_pose: + n_poses = 0 + elif isinstance(frame_face_pose[0], list): + n_poses = len(frame_face_pose) + else: + n_poses = 1 - overlap_faces = [] - overlap_poses = [] - for frame_face, frame_face_pose, frame_pose in zip(faces, faces_pose, poses): - if not frame_face: - n_faces = 0 - elif isinstance(frame_face[0], list): - n_faces = len(frame_face) - else: - n_faces = 1 + frame_overlap = np.zeros([n_faces, n_poses]) - if not frame_face_pose: - n_poses = 0 - elif isinstance(frame_face_pose[0], list): - n_poses = len(frame_face_pose) - else: - n_poses = 1 - - frame_overlap = np.zeros([n_faces, n_poses]) - - if n_faces == 0: - overlap_faces.append([]) - overlap_poses.append([]) - - elif (n_faces == 1) & (n_poses > 1): - b1 = BBox(frame_face[0][:-1]) - - for pose_idx in range(n_poses): - b2 = BBox(frame_face_pose[pose_idx][:-1]) - frame_overlap[0, pose_idx] = b1.overlap(b2) - matched_pose_index = np.where( - frame_overlap[0, :] == frame_overlap[0, :].max() - )[0][0] - overlap_faces.append(frame_face) - overlap_poses.append([frame_pose[matched_pose_index]]) - - elif (n_faces > 1) & (n_poses == 1): - b2 = BBox(frame_face_pose[0][:-1]) - for face_idx in range(n_faces): - b1 = BBox(frame_face[face_idx][:-1]) - frame_overlap[face_idx, 0] = b1.overlap(b2) - matched_face_index = np.where( - frame_overlap[:, 0] == frame_overlap[:, 0].max() - )[0][0] - new_poses = [] - for f_idx in range(n_faces): - if f_idx == matched_face_index: - new_poses.append(frame_pose[0]) - else: - new_poses.append(np.ones(3) * np.nan) - overlap_faces.append(frame_face) - overlap_poses.append(new_poses) + if n_faces == 0: + overlap_faces.append([]) + overlap_poses.append([]) + + elif (n_faces == 1) & (n_poses > 1): + b1 = BBox(frame_face[0][:-1]) - else: - for face_idx in range(n_faces): - b1 = BBox(frame_face[face_idx][:-1]) for pose_idx in range(n_poses): b2 = BBox(frame_face_pose[pose_idx][:-1]) - frame_overlap[face_idx, pose_idx] = b1.overlap(b2) + frame_overlap[0, pose_idx] = b1.overlap(b2) + matched_pose_index = np.where( + frame_overlap[0, :] == frame_overlap[0, :].max() + )[0][0] + overlap_faces.append(frame_face) + overlap_poses.append([frame_pose[matched_pose_index]]) + + elif (n_faces > 1) & (n_poses == 1): + b2 = BBox(frame_face_pose[0][:-1]) + for face_idx in range(n_faces): + b1 = BBox(frame_face[face_idx][:-1]) + frame_overlap[face_idx, 0] = b1.overlap(b2) + matched_face_index = np.where( + frame_overlap[:, 0] == frame_overlap[:, 0].max() + )[0][0] + new_poses = [] + for f_idx in range(n_faces): + if f_idx == matched_face_index: + new_poses.append(frame_pose[0]) + else: + new_poses.append(np.ones(3) * np.nan) + overlap_faces.append(frame_face) + overlap_poses.append(new_poses) - overlap_faces_frame = [] - overlap_poses_frame = [] - if n_faces < n_poses: + else: for face_idx in range(n_faces): - pose_idx = np.where( - frame_overlap[face_idx, :] - == frame_overlap[face_idx, :].max() - )[0][0] - overlap_faces_frame.append(frame_face[face_idx]) - overlap_poses_frame.append(frame_pose[pose_idx]) - elif n_faces > n_poses: - matched_pose_index = [] - for pose_idx in range(n_poses): - matched_pose_index.append( - np.where( - frame_overlap[:, pose_idx] - == frame_overlap[:, pose_idx].max() + b1 = BBox(frame_face[face_idx][:-1]) + for pose_idx in range(n_poses): + b2 = BBox(frame_face_pose[pose_idx][:-1]) + frame_overlap[face_idx, pose_idx] = b1.overlap(b2) + + overlap_faces_frame = [] + overlap_poses_frame = [] + if n_faces < n_poses: + for face_idx in range(n_faces): + pose_idx = np.where( + frame_overlap[face_idx, :] + == frame_overlap[face_idx, :].max() )[0][0] - ) - for face_idx in range(n_faces): - overlap_faces_frame.append(frame_face[face_idx]) - if face_idx in matched_pose_index: - overlap_poses_frame.append( - frame_pose[ - np.where( - frame_overlap[face_idx, :] - == frame_overlap[face_idx, :].max() - )[0][0] - ] + overlap_faces_frame.append(frame_face[face_idx]) + overlap_poses_frame.append(frame_pose[pose_idx]) + elif n_faces > n_poses: + matched_pose_index = [] + for pose_idx in range(n_poses): + matched_pose_index.append( + np.where( + frame_overlap[:, pose_idx] + == frame_overlap[:, pose_idx].max() + )[0][0] ) - else: - overlap_poses_frame.append(np.ones(3) * np.nan) - elif n_faces == n_poses: - overlap_faces_frame = frame_face - overlap_poses_frame = frame_pose - - overlap_faces.append(overlap_faces_frame) - overlap_poses.append(overlap_poses_frame) - - return (overlap_faces, overlap_poses) + for face_idx in range(n_faces): + overlap_faces_frame.append(frame_face[face_idx]) + if face_idx in matched_pose_index: + overlap_poses_frame.append( + frame_pose[ + np.where( + frame_overlap[face_idx, :] + == frame_overlap[face_idx, :].max() + )[0][0] + ] + ) + else: + overlap_poses_frame.append(np.ones(3) * np.nan) + elif n_faces == n_poses: + overlap_faces_frame = frame_face + overlap_poses_frame = frame_pose + + overlap_faces.append(overlap_faces_frame) + overlap_poses.append(overlap_poses_frame) + + return (overlap_faces, overlap_poses) diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py index 8188ced3..6d403f44 100644 --- a/feat/tests/test_detector_core.py +++ b/feat/tests/test_detector_core.py @@ -1,4 +1,4 @@ -from feat.detector import Detector, _match_faces_to_poses +from feat.detector import Detector from feat.data import Fex from feat.utils.io import get_test_data_path import os @@ -206,7 +206,7 @@ def test_detect_video(default_detector, single_face_mov): assert out.happiness.values.max() > 0 -def test_detect_mismatch_face_pose(): +def test_detect_mismatch_face_pose(default_detector): # Multiple Faces, 1 pose faces = [ [ @@ -229,7 +229,9 @@ def test_detect_mismatch_face_pose(): faces_pose = [[[46.0, 46.0, 66.0, 71.0, 0.99272925]]] poses = [[[-3.72766398, 10.9359162, -3.19862351]]] - new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) + new_faces, new_poses = default_detector._match_faces_to_poses( + faces, faces_pose, poses + ) assert len(new_faces[0]) == len(new_poses[0]) assert len(new_faces[0]) == 2 @@ -266,7 +268,9 @@ def test_detect_mismatch_face_pose(): ] ] - new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) + new_faces, new_poses = default_detector._match_faces_to_poses( + faces, faces_pose, poses + ) assert len(new_faces[0]) == 1 assert len(new_poses[0]) == 1 @@ -310,7 +314,9 @@ def test_detect_mismatch_face_pose(): ] ] - new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) + new_faces, new_poses = default_detector._match_faces_to_poses( + faces, faces_pose, poses + ) assert len(new_faces[0]) == len(new_poses[0]) assert len(new_faces[0]) == 2 @@ -351,6 +357,8 @@ def test_detect_mismatch_face_pose(): ] ] - new_faces, new_poses = _match_faces_to_poses(faces, faces_pose, poses) + new_faces, new_poses = default_detector._match_faces_to_poses( + faces, faces_pose, poses + ) assert len(new_faces[0]) == len(new_poses[0]) assert len(new_faces[0]) == 5 diff --git a/feat/utils/__init__.py b/feat/utils/__init__.py index 0d400097..a03379af 100644 --- a/feat/utils/__init__.py +++ b/feat/utils/__init__.py @@ -153,9 +153,8 @@ def set_torch_device(device="cpu"): return device +# TODO: Refactor the output of each detector into a reliable dataclass with the same +# structure to avoid utility functions like this def is_list_of_lists_empty(list_of_lists): """Helper function to check if list of lists is empty""" - if not any(list_of_lists): - return True - else: - return False + return not any(list_of_lists) From 843364ebc666fd669be4a68cbee33814a74ef62a Mon Sep 17 00:00:00 2001 From: ejolly Date: Fri, 3 Feb 2023 17:12:31 -0500 Subject: [PATCH 18/22] add test for videos with and without faces --- feat/tests/conftest.py | 15 +++++++++++++++ feat/tests/data/face_noface.mov | Bin 0 -> 1324450 bytes feat/tests/data/no_face.mp4 | Bin 0 -> 181553 bytes feat/tests/data/noface_face.mov | Bin 0 -> 2675494 bytes feat/tests/test_detector_core.py | 28 +++++++++++++++++++++++++++- 5 files changed, 42 insertions(+), 1 deletion(-) create mode 100644 feat/tests/data/face_noface.mov create mode 100644 feat/tests/data/no_face.mp4 create mode 100644 feat/tests/data/noface_face.mov diff --git a/feat/tests/conftest.py b/feat/tests/conftest.py index d0232444..69271eba 100644 --- a/feat/tests/conftest.py +++ b/feat/tests/conftest.py @@ -105,6 +105,21 @@ def single_face_mov(data_path): return os.path.join(data_path, "single_face.mp4") +@fixture(scope="module") +def no_face_mov(data_path): + return os.path.join(data_path, "no_face.mp4") + + +@fixture(scope="module") +def face_noface_mov(data_path): + return os.path.join(data_path, 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