From 5ec02e3996f212f8f1f98536cb2b1f59f1fe5d4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=AD=A6=E6=85=A7=E5=87=AF?= Date: Fri, 21 Feb 2020 20:05:26 +0800 Subject: [PATCH] video --- config.py | 12 - face_detect_and_track.py | 197 - face_detection.py | 59 + face_points_detection.py | 17 - face_swap.py | 39 + imgs/realtime0.gif | Bin 4795252 -> 0 bytes main.py | 88 +- main_video.py | 58 + models/haarcascade_frontalface_default.xml | 33314 ----------------- models/shape_predictor_81_face_landmarks.dat | Bin 19743860 -> 0 bytes requirements.txt | 9 - video_handler.py | 279 - 12 files changed, 162 insertions(+), 33910 deletions(-) delete mode 100644 config.py delete mode 100644 face_detect_and_track.py delete mode 100644 face_points_detection.py delete mode 100644 imgs/realtime0.gif create mode 100644 main_video.py delete mode 100644 models/haarcascade_frontalface_default.xml delete mode 100644 models/shape_predictor_81_face_landmarks.dat delete mode 100644 video_handler.py diff --git a/config.py b/config.py deleted file mode 100644 index c8a34b5..0000000 --- a/config.py +++ /dev/null @@ -1,12 +0,0 @@ -import cv2 -PREDICTOR_PATH = "models/shape_predictor_68_face_landmarks.dat" -CASCADE_PATH="models/haarcascade_frontalface_default.xml" -OPENCV_OBJECT_TRACKERS = { - "kcf": cv2.TrackerKCF_create, - "boosting": cv2.TrackerBoosting_create, - "mil": cv2.TrackerMIL_create, - "tld": cv2.TrackerTLD_create, - "medianflow": cv2.TrackerMedianFlow_create, - "mosse": cv2.TrackerMOSSE_create - -} \ No newline at end of file diff --git a/face_detect_and_track.py b/face_detect_and_track.py deleted file mode 100644 index fd83a92..0000000 --- a/face_detect_and_track.py +++ /dev/null @@ -1,197 +0,0 @@ -import numpy as np -import dlib -import cv2 -import logging -import time -import math -import argparse -from config import * -logging.basicConfig(level=logging.DEBUG, - format="%(levelname)s:%(lineno)d:%(message)s") - - -class Detector(object): - def __init__(self): - self.detector = cv2.CascadeClassifier(CASCADE_PATH) - - def face_detection(self, frame)->np.ndarray: - face_rects = self.__get_face_rects(frame) - if not isinstance(face_rects, np.ndarray): - logging.debug("No face") - return 0 - - if not len(face_rects) == 1: - logging.error("Too many face") - return 0 - - return face_rects[0] - - def __get_face_rects(self, img): - # 1.3 5 - face_rects = self.detector.detectMultiScale( - img, scaleFactor=1.1, minNeighbors=2, flags=cv2.CASCADE_SCALE_IMAGE, minSize=(120, 120)) - # face_rects = self.detector.detectMultiScale(img, scaleFactor=1.3, minNeighbors=5,flags=cv2.CASCADE_SCALE_IMAGE,minSize=(120,120)) - # face_rect:ndarray - if face_rects == (): - return None - return face_rects - - -class DlibDetector(object): - def __init__(self)->dlib.rectangle: - self.detector = dlib.get_frontal_face_detector() - - def __get_face_rects(self, img, upsample_num_times=0): - # For faster speed, I recommend the param upsample_num_times == 0 - face_rects = self.detector(img, upsample_num_times) - if not face_rects: - return None # face_rects=None - return face_rects - - def rect_to_bb(self, rect: dlib.rectangle)->tuple: - x = rect.left() - y = rect.top() - w = rect.right() - x - h = rect.bottom() - y - return (x, y, w, h) - - def dlib_method_test(self): - cap = cv2.VideoCapture(0) - while (cv2.waitKey(1) != 27): - start_t = cv2.getTickCount() - grabbed, frame = cap.read() - face_rects = self.__get_face_rects(frame) - if not face_rects: - logging.info("No face") - cv2.imshow("output", frame) - continue - if not len(face_rects) == 1: - logging.error("Too many faces") - break - (x, y, w, h) = self.rect_to_bb(face_rects[0]) - cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - cv2.imshow("output", frame) - end_t = cv2.getTickCount() - fps = cv2.getTickFrequency() / (end_t - start_t) - logging.info("fps: {0}".format(fps)) - - def face_detection(self, frame, upsample_times=0): - face_rects = self.__get_face_rects(frame, upsample_times) - if not face_rects: - logging.info("No face") - return 0 - - if not len(face_rects) == 1: - logging.error("Too many faces") - return 0 - - return self.rect_to_bb(face_rects[0]) - - -class Tracker(object): - """docstring for Tracker""" - # I really recommend use mosse, besides, KCF may be a good choice, but it has slow speed in windows. I guess KCF could work in ubuntu - - def __init__(self, tracker_type="mosse"): - self.tracker = OPENCV_OBJECT_TRACKERS[tracker_type]() - - def start_track(self, frame, x, y, w, h): - self.tracker.init(frame, (x, y, w, h)) - - def update_track(self, frame): - return self.tracker.update(frame) - - def clear(self): - return self.tracker.clear() - - -def expand_bbox(x, y, w, h): - x = np.max([0, x - int(w * 0.10)]) - y = np.max([0, y - int(h * 0.10)]) - w = int(w * 1.2) - h = int(h * 1.3) - return (x, y, w, h) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Human face detect and Track') - parser.add_argument('--video_path', default=0, help='Path for video') - args = parser.parse_args() - video_path = args.parse_args() - d = Detector() - t = Tracker() - - cap = cv2.VideoCapture(video_path) - face_flag = 0 - lose_threshold = 10 - target_lose_cnt = 0 - while (cv2.waitKey(1) != 27): - start_tc = cv2.getTickCount() - grabbed, frame = cap.read() - if not grabbed: - break - - if not face_flag: - face_bbox = d.face_detection(frame) - # No face has been detected - if isinstance(face_bbox, int): - cv2.imshow("output", frame) - continue - # detect successfully - else: - logging.info("tracker init") - face_flag = 1 - face_bbox = expand_bbox(*face_bbox) - t.start_track(frame, face_bbox) - target_lose_cnt = 0 - (x, y, w, h) = face_bbox - cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - cv2.imshow("output", frame) - - else: - # track - if target_lose_cnt < lose_threshold: - (success, box_predict) = t.update_track(frame) - if not success: - logging.info("update failed") - target_lose_cnt += 1 - cv2.imshow("output", frame) - continue - else: - (old_x, old_y, old_w, old_h) = (int(v) - for v in box_predict) - - # logging.debug("box_predict shape ({},{})".format(box_predict[2],box_predict[3])) - - # maxY=np.min([old_y+old_h,frame.shape[0]]) - # maxX=np.min([old_x+old_w,frame.shape[1]]) - # bbox = d.cascade_method(frame[old_y:maxY,old_x:maxX]) - - # draw predict rect - cv2.rectangle(frame, (old_x, old_y), - (old_x + old_w, old_y + old_h), (0, 0, 255), 2) - face_bbox = d.face_detection( - frame[old_y:old_y + old_h, old_x:old_x + old_w]) - if isinstance(face_bbox, int): - target_lose_cnt += 1 - cv2.imshow("output", frame) - continue - target_lose_cnt = 0 - (x, y, w, h) = expand_bbox(*face_bbox) - x = int(old_x + x) - y = int(old_y + y) - cv2.rectangle(frame, (x, y), (x + w, y + h), - (0, 255, 0), 2) - cv2.imshow("output", frame) - # lose target - else: - logging.info("losing target") - # reset tracker - t = Tracker() - face_flag = 0 - continue - end_tc = cv2.getTickCount() - fps = cv2.getTickFrequency() / (end_tc - start_tc) - logging.info(fps) - cv2.destroyAllWindows() - cap.release() diff --git a/face_detection.py b/face_detection.py index fe0ae7a..3f9f649 100644 --- a/face_detection.py +++ b/face_detection.py @@ -1,4 +1,6 @@ +import cv2 import dlib +import numpy as np ## Face detection def face_detection(img,upsample_times=1): @@ -9,3 +11,60 @@ def face_detection(img,upsample_times=1): faces = detector(img, upsample_times) return faces + +PREDICTOR_PATH = 'models/shape_predictor_68_face_landmarks.dat' +predictor = dlib.shape_predictor(PREDICTOR_PATH) +## Face and points detection +def face_points_detection(img, bbox:dlib.rectangle): + # Get the landmarks/parts for the face in box d. + shape = predictor(img, bbox) + + # loop over the 68 facial landmarks and convert them + # to a 2-tuple of (x, y)-coordinates + coords = np.asarray(list([p.x, p.y] for p in shape.parts()), dtype=np.int) + + # return the array of (x, y)-coordinates + return coords + +def select_face(im, r=10, choose=True): + faces = face_detection(im) + + if len(faces) == 0: + return None, None, None + + if len(faces) == 1 or not choose: + idx = np.argmax([(face.right() - face.left()) * (face.bottom() - face.top()) for face in faces]) + bbox = faces[idx] + else: + bbox = [] + + def click_on_face(event, x, y, flags, params): + if event != cv2.EVENT_LBUTTONDOWN: + return + + for face in faces: + if face.left() < x < face.right() and face.top() < y < face.bottom(): + bbox.append(face) + break + + im_copy = im.copy() + for face in faces: + # draw the face bounding box + cv2.rectangle(im_copy, (face.left(), face.top()), (face.right(), face.bottom()), (0, 0, 255), 1) + cv2.imshow('Click the Face:', im_copy) + cv2.setMouseCallback('Click the Face:', click_on_face) + while len(bbox) == 0: + cv2.waitKey(1) + cv2.destroyAllWindows() + bbox = bbox[0] + + points = np.asarray(face_points_detection(im, bbox)) + + im_w, im_h = im.shape[:2] + left, top = np.min(points, 0) + right, bottom = np.max(points, 0) + + x, y = max(0, left - r), max(0, top - r) + w, h = min(right + r, im_h) - x, min(bottom + r, im_w) - y + + return points - np.asarray([[x, y]]), (x, y, w, h), im[y:y + h, x:x + w] diff --git a/face_points_detection.py b/face_points_detection.py deleted file mode 100644 index ecc72f4..0000000 --- a/face_points_detection.py +++ /dev/null @@ -1,17 +0,0 @@ -#! /usr/bin/env python -import dlib -import numpy as np - -PREDICTOR_PATH = 'models/shape_predictor_68_face_landmarks.dat' -predictor = dlib.shape_predictor(PREDICTOR_PATH) -## Face and points detection -def face_points_detection(img, bbox:dlib.rectangle): - # Get the landmarks/parts for the face in box d. - shape = predictor(img, bbox) - - # loop over the 68 facial landmarks and convert them - # to a 2-tuple of (x, y)-coordinates - coords = np.asarray(list([p.x, p.y] for p in shape.parts()), dtype=np.int) - - # return the array of (x, y)-coordinates - return coords diff --git a/face_swap.py b/face_swap.py index 0d008f5..6d92101 100644 --- a/face_swap.py +++ b/face_swap.py @@ -197,3 +197,42 @@ def check_points(img,points): else: return True return False + + +def face_swap(src_face, dst_face, src_points, dst_points, dst_shape, dst_img, args, end=48): + h, w = dst_face.shape[:2] + + ## 3d warp + warped_src_face = warp_image_3d(src_face, src_points[:end], dst_points[:end], (h, w)) + ## Mask for blending + mask = mask_from_points((h, w), dst_points) + mask_src = np.mean(warped_src_face, axis=2) > 0 + mask = np.asarray(mask * mask_src, dtype=np.uint8) + ## Correct color + if args.correct_color: + warped_src_face = apply_mask(warped_src_face, mask) + dst_face_masked = apply_mask(dst_face, mask) + warped_src_face = correct_colours(dst_face_masked, warped_src_face, dst_points) + ## 2d warp + if args.warp_2d: + unwarped_src_face = warp_image_3d(warped_src_face, dst_points[:end], src_points[:end], src_face.shape[:2]) + warped_src_face = warp_image_2d(unwarped_src_face, transformation_from_points(dst_points, src_points), + (h, w, 3)) + + mask = mask_from_points((h, w), dst_points) + mask_src = np.mean(warped_src_face, axis=2) > 0 + mask = np.asarray(mask * mask_src, dtype=np.uint8) + + ## Shrink the mask + kernel = np.ones((10, 10), np.uint8) + mask = cv2.erode(mask, kernel, iterations=1) + ##Poisson 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