From 79d9469d2b8bd94c557800ec4d1fbf8138636323 Mon Sep 17 00:00:00 2001 From: Artur Siepietowski Date: Tue, 26 Oct 2021 22:40:19 +0200 Subject: [PATCH] Nice results came (#11) * Nice results came * Working MlPreprocessing with Preporcessing param --- .dockerignore | 1 + Dockerfile | 3 + README.md | 40 +++- data/raw/1.JPG | Bin 0 -> 1132637 bytes data/raw/2.JPG | Bin 0 -> 1015473 bytes data/raw/3.JPG | Bin 0 -> 1002059 bytes detector/configs/plasma.py | 10 +- detector/configs/plasma_ml.py | 13 +- ..._4_unet_256.py => plasma_ml_4_unet_384.py} | 21 +- detector/detection/edge_detection.py | 54 +++-- detector/detection/preprocessing.py | 55 ++--- detector/detection/preprocessing_ml.py | 46 ++-- detector/detector.py | 8 +- detector/labelers/labelme.py | 222 +++++++++++++++++- detector_cli.py | 7 +- experiments/report.py | 76 ++++-- 16 files changed, 419 insertions(+), 137 deletions(-) create mode 100644 .dockerignore create mode 100755 data/raw/1.JPG create mode 100755 data/raw/2.JPG create mode 100755 data/raw/3.JPG rename detector/configs/{plasma_ml_4_unet_256.py => plasma_ml_4_unet_384.py} (78%) diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..4073c5d --- /dev/null +++ b/.dockerignore @@ -0,0 +1 @@ +detector/configs/models/* \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 6148288..744dc66 100644 --- a/Dockerfile +++ b/Dockerfile @@ -9,6 +9,9 @@ RUN apt-get update && \ COPY requirements.txt . RUN pip install -r requirements.txt +ENV TF_CPP_MIN_LOG_LEVEL 3 + +COPY .dockerignore . COPY detector_cli.py . COPY detector/ detector diff --git a/README.md b/README.md index e370148..659b896 100644 --- a/README.md +++ b/README.md @@ -1,29 +1,38 @@ [![License: CC BY-NC 4.0](https://licensebuttons.net/l/by-nc/4.0/80x15.png)](https://creativecommons.org/licenses/by-nc/4.0/) # PhotoVoltaicPanelDetection -This repository contains solution for automatic detection of solar modules. +This repository contains solution for automatic detection of solar modules. Main aim was +to prepare a tool that can be configured and based on that configuration produce detected +modules in to be defined formats, commonly used in machine learning but not only. ## Running in docker container +### Build Docker Image from source + +``` +docker build -t pvpd:0.0.1 -t pvpd . +``` + +Try running: +``` +docker run pvpd:0.1.0 -h +``` + ### Example run from docker ``` -docker run -v ${PWD}/data:/usr/data pvpd:0.0.1 -c PlasmaConfig -o /usr/data/ -f /usr/data/plasma/1.JPG -t raw -cm plasma +docker run -v ${PWD}/data:/usr/data pvpd:0.1.0 -c PlasmaConfig -o /usr/data --f /usr/data/raw/3.JPG -t raw -cm plasma -l LabelMeLabeler ``` - `-v ${PWD}/data:/usr/data` shares directory `data` from repository to `/usr/data` -- `pvpd:0.0.1` version of docker image +- `pvpd:0.1.0` version of docker image - `-c PlasmaConfig` which config will be used for image annotation - `-o /usr/data/` data will be saved in the shared directory `data` - `-f /usr/data/plasma/1.JPG` file `1.JPG` is going to be analyzing - `-t raw` file `1.JPG` is in a raw format. There is a need to extract thermal information - `-cm plasma` which color map will be used for thermal image values -### Build Docker Image from source -``` -docker build -t pvpd:0.0.1 -t pvpd . -``` ## Local installation @@ -52,3 +61,20 @@ pip install -r requirements.txt ``` ./detector_cli.py ``` + +### Configs +Configs are parts of this project that every user adjust for private images. +All config can be found those in [config directory](detector/configs). Using them requires some +knowledge about classic image processing methods. + +Whole detection is split into distinct steps that could be configured using each step +`Param`. It is highly suggested taking a look at those. + +For now available Configs can be found using `./detector_cli.py -h` + +### Labelers +Labeler is an entity that can be selected to generate a file containing data and metadata +from detection process. New labelers can be easily created to meet individual needs. 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hysteresis_min_thresh=35, hysteresis_max_thresh=45, - kernel_size=(3, 3), - kernel_shape=cv2.MORPH_RECT, + kernel_size=(7, 7), + kernel_shape=cv2.MORPH_CROSS, dilation_steps=4 ) segment_detector_params = SegmentDetectorParams( d_rho=1, d_theta=np.pi / 180, - min_num_votes=135, - min_line_length=max(floor(5 * (__edge_image_scaling)), 20), + min_num_votes=85, + min_line_length=max(floor(10 * (__edge_image_scaling-10)), 20), max_line_gap=20 * __edge_image_scaling, extension_pixels=35 * __edge_image_scaling ) @@ -43,7 +43,7 @@ class PlasmaConfig(Config): ) cluster_cleaning_params = ClusterCleaningParams( max_angle_variation_mean=np.pi / 180 * 20, - max_merging_angle=np.pi / 180 * 30, + max_merging_angle=np.pi / 180 * 40, max_endpoint_distance=10 ) intersection_detector_params = IntersectionDetectorParams( diff --git a/detector/configs/plasma_ml.py b/detector/configs/plasma_ml.py index 06bd60a..06b8fd2 100644 --- a/detector/configs/plasma_ml.py +++ b/detector/configs/plasma_ml.py @@ -7,17 +7,16 @@ from detector.detection import EdgeDetectorParams, SegmentDetectorParams, SegmentClustererParams, \ ClusterCleaningParams, IntersectionDetectorParams, RectangleDetectorParams from detector.detection.preprocessing_ml import PreprocessingMlParams -from trainer.utils.consts import UNET_6_LAYERS class PlasmaMlConfig(Config): __preprocessing_image_scaling = 1 __edge_image_scaling = 3 preprocessing_params = PreprocessingMlParams( - model_name=UNET_6_LAYERS, - weight_path='/Users/artursiepietwoski/Developer/Private/PhotoVoltaicPanelsDetection/neural/trainer/training_result/1_training_unet_6_layers_2021-09-28T00:23:27_gray/cp.ckpt', + model_name='unet_6_layers', + weight_path='detector/configs/models/content/training_result/1_training_unet_6_layers_2021-09-28T00:23:27_gray/cp.ckpt', gray=True, - model_image_size=(128, 128), + model_image_size=(192,192), start_neurons=16, gaussian_blur=3, model_output_threshold=64, @@ -27,14 +26,14 @@ class PlasmaMlConfig(Config): image_scaling=__edge_image_scaling, hysteresis_min_thresh=35, hysteresis_max_thresh=45, - kernel_size=(3, 3), - kernel_shape=cv2.MORPH_RECT, + kernel_size=(7, 7), + kernel_shape=cv2.MORPH_CROSS, dilation_steps=4 ) segment_detector_params = SegmentDetectorParams( d_rho=1, d_theta=np.pi / 180, - min_num_votes=110, + min_num_votes=75, min_line_length=max(floor(10 * (__edge_image_scaling - 2)), 20), max_line_gap=20 * __edge_image_scaling, extension_pixels=35 * __edge_image_scaling diff --git a/detector/configs/plasma_ml_4_unet_256.py b/detector/configs/plasma_ml_4_unet_384.py similarity index 78% rename from detector/configs/plasma_ml_4_unet_256.py rename to detector/configs/plasma_ml_4_unet_384.py index 7f4580a..041be8f 100644 --- a/detector/configs/plasma_ml_4_unet_256.py +++ b/detector/configs/plasma_ml_4_unet_384.py @@ -7,17 +7,16 @@ from detector.detection import EdgeDetectorParams, SegmentDetectorParams, SegmentClustererParams, \ ClusterCleaningParams, IntersectionDetectorParams, RectangleDetectorParams from detector.detection.preprocessing_ml import PreprocessingMlParams -from trainer.utils.consts import UNET_6_LAYERS, UNET_4_LAYERS -class PlasmaMl4Unet256Config(Config): +class PlasmaMl4Unet384Config(Config): __preprocessing_image_scaling = 1 __edge_image_scaling = 3 preprocessing_params = PreprocessingMlParams( - model_name=UNET_4_LAYERS, - weight_path='detector/configs/models/1_training_unet_4_layers_2021-10-17T23:28:47_gray/cp.ckpt', + model_name='unet_4_layers', + weight_path='detector/configs/models/content/training_result/1_training_unet_4_layers_2021-10-17T23:28:47_gray/cp.ckpt', gray=True, - model_image_size=(256, 256), + model_image_size=(384, 384), start_neurons=16, gaussian_blur=3, model_output_threshold=64, @@ -27,14 +26,14 @@ class PlasmaMl4Unet256Config(Config): image_scaling=__edge_image_scaling, hysteresis_min_thresh=35, hysteresis_max_thresh=45, - kernel_size=(3, 3), - kernel_shape=cv2.MORPH_RECT, + kernel_size=(7, 7), + kernel_shape=cv2.MORPH_CROSS, dilation_steps=4 ) segment_detector_params = SegmentDetectorParams( d_rho=1, d_theta=np.pi / 180, - min_num_votes=110, + min_num_votes=75, min_line_length=max(floor(10 * (__edge_image_scaling - 2)), 20), max_line_gap=20 * __edge_image_scaling, extension_pixels=35 * __edge_image_scaling @@ -49,8 +48,8 @@ class PlasmaMl4Unet256Config(Config): ) cluster_cleaning_params = ClusterCleaningParams( max_angle_variation_mean=np.pi / 180 * 20, - max_merging_angle=np.pi / 180 * 40, - max_endpoint_distance=15 + max_merging_angle=np.pi / 180 * 30, + max_endpoint_distance=10 ) intersection_detector_params = IntersectionDetectorParams( angle_threshold=np.pi / 180 * 25 @@ -58,5 +57,5 @@ class PlasmaMl4Unet256Config(Config): rectangle_detector_params = RectangleDetectorParams( aspect_ratio=1.5, aspect_ratio_relative_deviation=0.35, - min_area=20 * 40 + min_area=20 * 30 ) diff --git a/detector/detection/edge_detection.py b/detector/detection/edge_detection.py index d2d7e86..514e03f 100644 --- a/detector/detection/edge_detection.py +++ b/detector/detection/edge_detection.py @@ -1,10 +1,11 @@ import logging -from dataclasses import dataclass, field +from dataclasses import dataclass from typing import Tuple import cv2 import numpy as np +from detector.utils.display import display_image_in_actual_size from detector.utils.images import scale_image @@ -28,13 +29,8 @@ class EdgeDetectorParams: kernel_size: Tuple[int, int] = (3, 3) kernel_shape: int = cv2.MORPH_CROSS - kernel: None = field(init=False, default_factory=tuple) - dilation_steps: int = 4 - def __post_init__(self): - self.kernel = cv2.getStructuringElement(self.kernel_shape, self.kernel_size) - class EdgeDetector: """Class responsible for detecting edges in greyscale images. @@ -87,7 +83,7 @@ def strengthen_edges(self, input_image): # adaptive thresholding adaptive_threshold = cv2.adaptiveThreshold(blurred_image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2) - denoised_image = cv2.fastNlMeansDenoising(adaptive_threshold, 9, 21, 7) # 30, 7, 25 + denoised_image = cv2.fastNlMeansDenoising(adaptive_threshold, 11, 31, 9) # 30, 7, 25 # contrasting brightness = 0 contrast = 64 # contrast_const @@ -95,6 +91,27 @@ def strengthen_edges(self, input_image): contrasted_image = self.apply_brightness_contrast(denoised_image, brightness, contrast) return contrasted_image + @staticmethod + def skeletize(dilated, kernel_shape, kernel_size): + done = False + size = np.size(dilated) + skel = np.zeros(dilated.shape, np.uint8) + img = dilated + + kernel = cv2.getStructuringElement(kernel_shape, kernel_size) + while not done: + logging.debug("Eroding canny edges") + eroded = cv2.erode(img, kernel) + temp = cv2.dilate(eroded, kernel) + temp = cv2.subtract(img, temp) + skel = cv2.bitwise_or(skel, temp) + img = eroded.copy() + + zeros = size - cv2.countNonZero(img) + if zeros == size: + done = True + return skel + def detect(self) -> None: """Detects the edges in the image passed to the constructor using the parameters passed to the constructor. """ @@ -113,25 +130,12 @@ def detect(self) -> None: dilated = cv2.dilate(canny, (3, 3), iterations=self.params.dilation_steps) logging.debug("Dilate canny edges with {} steps".format(self.params.dilation_steps)) - size = np.size(dilated) - skel = np.zeros(dilated.shape, np.uint8) - img = dilated - done = False - - kernel_size = (7, 7) - kernel_shape = cv2.MORPH_CROSS - kernel = cv2.getStructuringElement(kernel_shape, kernel_size) - while not done: - logging.debug("Eroding canny edges") - eroded = cv2.erode(img, kernel) - temp = cv2.dilate(eroded, kernel) - temp = cv2.subtract(img, temp) - skel = cv2.bitwise_or(skel, temp) - img = eroded.copy() + kernel_shape = self.params.kernel_shape + kernel_size = self.params.kernel_size - zeros = size - cv2.countNonZero(img) - if zeros == size: - done = True + skel1 = self.skeletize(dilated, kernel_shape, (3,3)) + skel2 = self.skeletize(dilated, kernel_shape, (5,5)) + skel = cv2.bitwise_or(skel1, skel2) self.edge_image = skel diff --git a/detector/detection/preprocessing.py b/detector/detection/preprocessing.py index 279c3db..f98b911 100644 --- a/detector/detection/preprocessing.py +++ b/detector/detection/preprocessing.py @@ -11,7 +11,6 @@ class PreprocessingParams: """Parameters used by the :class:`.Preprocessor`. Initializes the preprocessing parameters to their default value. - Attributes: :param gaussian_blur: Radius of the gaussian blur to apply to the input image. :param image_scaling: Scaling factor to apply to the input image. @@ -34,7 +33,6 @@ class Preprocessor: def __init__(self, input_image: np.ndarray, params: PreprocessingParams = PreprocessingParams()): """Initializes the frame preprocessor with the input image and the preprocessor parameters. - :param input_image: RGB or greyscale input image to be preprocessed. :param params: Preprocessing parameters. """ @@ -42,8 +40,9 @@ def __init__(self, input_image: np.ndarray, params: PreprocessingParams = Prepro self.params = params self.preprocessed_image = None self.scaled_image_rgb = None - self.scaled_image = None + self.scaled_image_gray = None self.attention_image = None + self.centroids = None self.mask = None @property @@ -99,43 +98,35 @@ def remove_reflections(self, input_image: np.ndarray) -> np.ndarray: def preprocess(self) -> None: """Preprocesses the :attr:`self.input_image` following this steps: - 1. The image is scaled using the :attr:`self.params.image_scaling` parameter. 2. The image is rotated using the :attr:`self.params.image_rotation` parameter. 3. Attention detection. - a. If the image is RGB, the :attr:`self.params.red_threshold` parameter is used to determine the attention areas of the image. b. Otherwise the entire image is kept as attention. - """ - - scaled_image = scale_image(self.input_image, self.params.image_scaling) - rotated_image = rotate_image(scaled_image, self.params.image_rotation) + removed_reflections_img = self.remove_reflections(self.input_image) + scaled_image = scale_image(removed_reflections_img, self.params.image_scaling) + rotated_frame = rotate_image(scaled_image, self.params.image_rotation) if self.params.gaussian_blur > 0: - rotated_image = cv2.blur(rotated_image, (self.params.gaussian_blur, self.params.gaussian_blur)) - - removed_reflections_img = self.remove_reflections(rotated_image) + rotated_frame = cv2.blur(rotated_frame, (self.params.gaussian_blur, self.params.gaussian_blur)) if self.channels == 1: - self.scaled_image = scaled_image - self.scaled_image_rgb = cv2.cvtColor(self.scaled_image, cv2.COLOR_GRAY2BGR) - self.preprocessed_image = removed_reflections_img - mask = np.ones_like(self.scaled_image).astype(np.uint8) * 255 + self.scaled_image_gray = rotated_frame + self.scaled_image_rgb = cv2.cvtColor(self.scaled_image_gray, cv2.COLOR_GRAY2BGR) + self.preprocessed_image = self.scaled_image_gray.astype(np.uint8) + mask = np.ones_like(self.scaled_image_gray).astype(np.uint8) * 255 else: if self.gray_scale: - self.scaled_image = scaled_image[:, :, 0] - self.scaled_image_rgb = None - self.preprocessed_image = removed_reflections_img - mask = np.ones_like(self.scaled_image).astype(np.uint8) * 255 + self.scaled_image_rgb = rotated_frame + self.scaled_image_gray = rotated_frame[:, :, 0] + self.preprocessed_image = self.scaled_image_gray.astype(np.uint8) + mask = np.ones_like(self.scaled_image_gray).astype(np.uint8) * 255 else: - self.scaled_image_rgb = scaled_image - self.scaled_image = cv2.cvtColor(self.scaled_image_rgb, cv2.COLOR_BGR2GRAY) - - removed_reflections_img_gray = cv2.cvtColor(removed_reflections_img, cv2.COLOR_BGR2GRAY) + self.scaled_image_rgb = rotated_frame + self.scaled_image_gray = cv2.cvtColor(self.scaled_image_rgb, cv2.COLOR_BGR2GRAY) - thresholded_image = cv2.adaptiveThreshold(removed_reflections_img_gray, 255, - cv2.ADAPTIVE_THRESH_GAUSSIAN_C, + thresholded_image = cv2.adaptiveThreshold(self.scaled_image_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 713, 1) # Perform dilation and erosion on the thresholded image to remove holes and small islands. kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7)) @@ -144,7 +135,7 @@ def preprocess(self) -> None: distance_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 3) _, sure_foreground_image = cv2.threshold(distance_transform, 0.01 * distance_transform.max(), 255, 0) sure_foreground_image = np.uint8(sure_foreground_image) - background_removed_image = cv2.bitwise_and(self.scaled_image, sure_foreground_image) + background_removed_image = cv2.bitwise_and(self.scaled_image_gray, sure_foreground_image) closing = cv2.morphologyEx(background_removed_image, cv2.MORPH_CLOSE, kernel) opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel) @@ -157,7 +148,7 @@ def preprocess(self) -> None: contours = hulls result_contours = [] for contour in contours: - mask = np.zeros_like(self.scaled_image) + mask = np.zeros_like(self.scaled_image_gray) cv2.drawContours(mask, [contour], 0, (255), cv2.FILLED) mask = mask.astype(np.float) / 255. result = self.scaled_image_rgb[:, :, 2] * mask @@ -168,7 +159,7 @@ def preprocess(self) -> None: if len(contours) > len(result_contours): logging.info("Reduced number of contours") - mask = np.zeros_like(self.scaled_image) + mask = np.zeros_like(self.scaled_image_gray) cv2.drawContours(mask, result_contours, -1, (255), cv2.FILLED) mask = cv2.dilate(mask, kernel, iterations=4) self.mask = mask @@ -177,7 +168,9 @@ def preprocess(self) -> None: self.preprocessed_image = (background_removed_image * mask).astype(np.uint8) mask = (mask * 255).astype(np.uint8) + self.scaled_image_rgb = scale_image(self.input_image, self.params.image_scaling) + self.scaled_image_gray = cv2.cvtColor(self.scaled_image_rgb, cv2.COLOR_BGR2GRAY) attention_mask = cv2.applyColorMap(mask, cv2.COLORMAP_WINTER) self.attention_image = np.empty_like(self.scaled_image_rgb) - cv2.addWeighted(cv2.cvtColor(self.scaled_image, cv2.COLOR_GRAY2BGR), 0.7, attention_mask, 0.3, 0, - self.attention_image) + cv2.addWeighted(cv2.cvtColor(self.scaled_image_gray, cv2.COLOR_GRAY2BGR), 0.7, attention_mask, 0.3, 0, + self.attention_image) \ No newline at end of file diff --git a/detector/detection/preprocessing_ml.py b/detector/detection/preprocessing_ml.py index 47fbe4a..f3190c4 100644 --- a/detector/detection/preprocessing_ml.py +++ b/detector/detection/preprocessing_ml.py @@ -1,28 +1,30 @@ +import logging from dataclasses import dataclass from functools import lru_cache +from pathlib import Path from typing import Tuple import cv2 import numpy as np -from detector.utils.display import display_image_in_actual_size -from detector.utils.utils import read_bgr_img -from models.models_builders import get_model_builder -from trainer.utils.consts import UNET_6_LAYERS - @dataclass class PreprocessingMlParams: """Parameters used by the :class:`.Preprocessor`. - Initializes the preprocessing parameters to their default value. + Initializes the ML preprocessing parameters to their default value. Attributes: - :param gaussian_blur: Radius of the gaussian blur to apply to the input image. - :param image_scaling: Scaling factor to apply to the input image. - :param image_rotation: Angle expressed in radiants used to rotate the input image. - :param red_threshold: Temperature threshold used to discard `cold` unimportant areas in the image. + :param model_name: Name of predefined segmentation model + :param weight_path: Path to pretrained models :param min_area: Minimal surface of retained `important` areas of the image. Warm regions whose surface is smaller than this threshold are discarded. + :param gray: Is model trained on grayscale or red + :param model_image_size: ML Model output size + :param start_neurons: Initial value of trained model + :param model_output_threshold: ML Model output is size :param model_image_size: and have float value between 0 + and 1. This value is normalized to value between 0 and 255. Every value lower then model_output_threshold is + set to 0. + :param gaussian_blur: Radius of the gaussian blur to apply to the input image. """ model_name: str weight_path: str @@ -65,11 +67,14 @@ def __init__(self, input_image: np.ndarray, params: PreprocessingMlParams): :param input_image: RGB or greyscale input image to be preprocessed. :param params: Preprocessing parameters. """ + if not Path(params.weight_path).parent.exists() or Path(params.weight_path).name != 'cp.ckpt': + logging.error("ML Preprocessing is not available. Licence is required. Try other configs") + exit(2) self.input_image = input_image self.params = params self.preprocessed_image = None self.scaled_image_rgb = input_image - self.scaled_image = None + self.scaled_image_gray = None self.attention_image = None self.centroids = None self.mask = None @@ -78,6 +83,7 @@ def __init__(self, input_image: np.ndarray, params: PreprocessingMlParams): @staticmethod @lru_cache def prepare_model(params: PreprocessingMlParams): + from models.models_builders import get_model_builder model = get_model_builder(params.model_name)(params.model_image_size[0], params.model_image_size[1], params.channels, params.start_neurons) model.load_weights(params.weight_path) @@ -157,15 +163,14 @@ def preprocess(self) -> None: blurred_removed_reflections_img = cv2.blur(removed_reflections_img, (self.params.gaussian_blur, self.params.gaussian_blur)) step_1_img = cv2.cvtColor(blurred_removed_reflections_img, cv2.COLOR_BGR2GRAY) - self.scaled_image = step_1_img + self.scaled_image_gray = step_1_img - classic_mask = self.classic_image_processing_mask(step_1_img) ml_mask = self.ml_image_processing_mask() - merged_masks = cv2.bitwise_or(classic_mask, ml_mask) + merged_masks = ml_mask contours, hierarchy = cv2.findContours(merged_masks, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - contours = [contour for contour in contours if cv2.contourArea(contour) > (120 ** 2)] + contours = [contour for contour in contours if cv2.contourArea(contour) > (40 ** 2)] corrected_merged_mask = np.zeros_like(step_1_img) cv2.drawContours(corrected_merged_mask, contours, -1, (255), cv2.FILLED) corrected_merged_mask = corrected_merged_mask.astype(np.uint8) @@ -179,13 +184,6 @@ def preprocess(self) -> None: self.preprocessed_image = (blurred_mask * background_removed_image).astype(np.uint8) attention_mask = cv2.applyColorMap(corrected_merged_mask, cv2.COLORMAP_WINTER) - self.attention_image = cv2.addWeighted(cv2.cvtColor(self.scaled_image, cv2.COLOR_GRAY2BGR), 0.7, attention_mask, + self.attention_image = cv2.addWeighted(cv2.cvtColor(self.scaled_image_gray, cv2.COLOR_GRAY2BGR), 0.7, + attention_mask, 0.3, 0) - - -if __name__ == '__main__': - thermal_image = read_bgr_img( - '/Users/artursiepietwoski/Developer/Private/PhotoVoltaicPanelsDetection/data/labelme/plasma-DJI_1_R (459).JPG') - preprocesor = PreprocessorMl(thermal_image, PreprocessingMlParams(model_name=UNET_6_LAYERS, - weight_path='/Users/artursiepietwoski/Developer/Private/PhotoVoltaicPanelsDetection/neural/trainer/training_result/1_training_unet_6_layers_2021-09-28T00:23:27_gray/cp.ckpt')) - preprocesor.preprocess() diff --git a/detector/detector.py b/detector/detector.py index 60ea8bb..495015f 100644 --- a/detector/detector.py +++ b/detector/detector.py @@ -32,16 +32,16 @@ def preprocess_image_functional( preprocessor = Preprocessor(input_image=image, params=params) preprocessor.preprocess() scaled_image_rgb = preprocessor.scaled_image_rgb - scaled_image = preprocessor.scaled_image + scaled_image = preprocessor.scaled_image_gray preprocessed_image = preprocessor.preprocessed_image attention_image = preprocessor.attention_image mask = preprocessor.mask if not silent: - pyplot.subplot(231), pyplot.imshow(cv2.cvtColor(scaled_image_rgb, cv2.COLOR_BGR2RGB)) + pyplot.subplot(221), pyplot.imshow(cv2.cvtColor(scaled_image_rgb, cv2.COLOR_BGR2RGB)) pyplot.title('scaled_image_rgb'), pyplot.xticks([]), pyplot.yticks([]) pyplot.subplot(222), pyplot.imshow(cv2.cvtColor(scaled_image, cv2.COLOR_BGR2RGB)) - pyplot.title('scaled_image'), pyplot.xticks([]), pyplot.yticks([]) + pyplot.title('scaled_image_gray'), pyplot.xticks([]), pyplot.yticks([]) pyplot.subplot(223), pyplot.imshow(cv2.cvtColor(preprocessed_image, cv2.COLOR_BGR2RGB)) pyplot.title('preprocessed_image'), pyplot.xticks([]), pyplot.yticks([]) @@ -67,7 +67,7 @@ def preprocess_ml_image_functional( preprocessor = PreprocessorMl(input_image=image, params=params) preprocessor.preprocess() scaled_image_rgb = preprocessor.scaled_image_rgb - scaled_image = preprocessor.scaled_image + scaled_image = preprocessor.scaled_image_gray preprocessed_image = preprocessor.preprocessed_image attention_image = preprocessor.attention_image mask = preprocessor.mask diff --git a/detector/labelers/labelme.py b/detector/labelers/labelme.py index 3e8a1de..5530323 100644 --- a/detector/labelers/labelme.py +++ b/detector/labelers/labelme.py @@ -1,13 +1,229 @@ import base64 +import io import json +import logging import os +import os.path as osp -from labelme import LabelFile, __version__ -from labelme.label_file import LabelFileError +import PIL +import numpy as np from detector.labelers.abstract import RectangleLabeler from detector.utils.utils import to_camel +logger = logging.getLogger() + + +class LabelFileError(Exception): + pass + + +def img_data_to_pil(img_data): + f = io.BytesIO() + f.write(img_data) + img_pil = PIL.Image.open(f) + return img_pil + + +def img_data_to_arr(img_data): + img_pil = img_data_to_pil(img_data) + img_arr = np.array(img_pil) + return img_arr + + +def img_b64_to_arr(img_b64): + img_data = base64.b64decode(img_b64) + img_arr = img_data_to_arr(img_data) + return img_arr + + +def img_pil_to_data(img_pil): + f = io.BytesIO() + img_pil.save(f, format="PNG") + img_data = f.getvalue() + return img_data + + +def img_arr_to_b64(img_arr): + img_pil = PIL.Image.fromarray(img_arr) + f = io.BytesIO() + img_pil.save(f, format="PNG") + img_bin = f.getvalue() + if hasattr(base64, "encodebytes"): + img_b64 = base64.encodebytes(img_bin) + else: + img_b64 = base64.encodestring(img_bin) + return img_b64 + + +def img_data_to_png_data(img_data): + with io.BytesIO() as f: + f.write(img_data) + img = PIL.Image.open(f) + + with io.BytesIO() as f: + img.save(f, "PNG") + f.seek(0) + return f.read() + + +def apply_exif_orientation(image): + try: + exif = image._getexif() + except AttributeError: + exif = None + + if exif is None: + return image + + exif = { + PIL.ExifTags.TAGS[k]: v + for k, v in exif.items() + if k in PIL.ExifTags.TAGS + } + + orientation = exif.get("Orientation", None) + + if orientation == 1: + # do nothing + return image + elif orientation == 2: + # left-to-right mirror + return PIL.ImageOps.mirror(image) + elif orientation == 3: + # rotate 180 + return image.transpose(PIL.Image.ROTATE_180) + elif orientation == 4: + # top-to-bottom mirror + return PIL.ImageOps.flip(image) + elif orientation == 5: + # top-to-left mirror + return PIL.ImageOps.mirror(image.transpose(PIL.Image.ROTATE_270)) + elif orientation == 6: + # rotate 270 + return image.transpose(PIL.Image.ROTATE_270) + elif orientation == 7: + # top-to-right mirror + return PIL.ImageOps.mirror(image.transpose(PIL.Image.ROTATE_90)) + elif orientation == 8: + # rotate 90 + return image.transpose(PIL.Image.ROTATE_90) + else: + return image + + +class LabelFile(object): + suffix = ".json" + + def __init__(self, filename=None): + self.shapes = [] + self.imagePath = None + self.imageData = None + if filename is not None: + self.load(filename) + self.filename = filename + + @staticmethod + def load_image_file(filename): + try: + image_pil = PIL.Image.open(filename) + except IOError: + logger.error("Failed opening image file: {}".format(filename)) + return + + # apply orientation to image according to exif + image_pil = apply_exif_orientation(image_pil) + + with io.BytesIO() as f: + ext = osp.splitext(filename)[1].lower() + if ext in [".jpg", ".jpeg"]: + format = "JPEG" + else: + format = "PNG" + image_pil.save(f, format=format) + f.seek(0) + return f.read() + + def load(self, filename): + keys = [ + "version", + "imageData", + "imagePath", + "shapes", # polygonal annotations + "flags", # image level flags + "imageHeight", + "imageWidth", + ] + shape_keys = [ + "label", + "points", + "group_id", + "shape_type", + "flags", + ] + try: + with open(filename, "r") as f: + data = json.load(f) + + if data["imageData"] is not None: + imageData = base64.b64decode(data["imageData"]) + else: + # relative path from label file to relative path from cwd + imagePath = osp.join(osp.dirname(filename), data["imagePath"]) + imageData = self.load_image_file(imagePath) + flags = data.get("flags") or {} + imagePath = data["imagePath"] + self._check_image_height_and_width( + base64.b64encode(imageData).decode("utf-8"), + data.get("imageHeight"), + data.get("imageWidth"), + ) + shapes = [ + dict( + label=s["label"], + points=s["points"], + shape_type=s.get("shape_type", "polygon"), + flags=s.get("flags", {}), + group_id=s.get("group_id"), + other_data={ + k: v for k, v in s.items() if k not in shape_keys + }, + ) + for s in data["shapes"] + ] + except Exception as e: + raise LabelFileError(e) + + otherData = {} + for key, value in data.items(): + if key not in keys: + otherData[key] = value + + # Only replace data after everything is loaded. + self.flags = flags + self.shapes = shapes + self.imagePath = imagePath + self.imageData = imageData + self.filename = filename + self.otherData = otherData + + @staticmethod + def _check_image_height_and_width(imageData, imageHeight, imageWidth): + img_arr = img_b64_to_arr(imageData) + if imageHeight is not None and img_arr.shape[0] != imageHeight: + logger.error( + "imageHeight does not match with imageData or imagePath, " + "so getting imageHeight from actual image." + ) + imageHeight = img_arr.shape[0] + if imageWidth is not None and img_arr.shape[1] != imageWidth: + logger.error( + "imageWidth does not match with imageData or imagePath, " + "so getting imageWidth from actual image." + ) + imageWidth = img_arr.shape[1] + return imageHeight, imageWidth + class LabelMeLabeler(RectangleLabeler): extension = 'json' @@ -21,7 +237,7 @@ def __save(self, filename, shapes, image_path, image_height, image_width, image_ if tags is None: tags = {} data = dict( - version=__version__, + version='4.5.13', flags=None, shapes=shapes, imagePath=image_path, diff --git a/detector_cli.py b/detector_cli.py index 553244d..bd96b6c 100755 --- a/detector_cli.py +++ b/detector_cli.py @@ -67,11 +67,11 @@ def process_multiple(file_paths, config, output_dir, labelers, silent): helps = { 'multithread': "Multithreaded version of detection. Speed up detection using all available processor resources", - 'config': "Config Class name. Based on config PV panels are detected from image. " + 'config': "Config Class name. Based on config PV panels are detected on image. " "User can create his own config and place in detector/configs directory. " "Class name must be unique", 'labelers': "Space separated list labeler class name. Labeler serialize detected PV panels to a predefined format. " - "Bear in mind that not all label formats are lossless" + "Bear in mind that not all label formats are lossless. " "User can create his own config and place in detector/labelers directory.", 'type': f"Type of input image. When set to '{RAW}' extraction of FLIR image is done using " f"color map from 'color-map' parameter", @@ -82,7 +82,8 @@ def process_multiple(file_paths, config, output_dir, labelers, silent): 'thermal-image-output': "Directory to which extracted images are saved", 'preprocessed-size': "During preprocessing size of image can change. " "If set, image will have preprocessed image size", - 'show-step-images': "If set step images of detection will be shown using matplotlib", + 'show-step-images': "If set step images of detection will be shown using matplotlib. " + "Do noting in docker and can slow down processing.", "files": "Space separated list of paths to file detection (and if needed extraction) is done.", "input-dir": "Every file from this directory will be processed. Only files with .JPG and .jpg are processed" } diff --git a/experiments/report.py b/experiments/report.py index cc55f1a..8c16cf0 100644 --- a/experiments/report.py +++ b/experiments/report.py @@ -6,6 +6,7 @@ import pickle from dataclasses import dataclass from pathlib import Path +from statistics import median from typing import Union, List import cv2 @@ -14,9 +15,10 @@ from shapely.geometry import Polygon from shapely.ops import unary_union -from detector.utils.display import display_image_in_actual_size -from detector.utils.utils import iou_rectangle_annotated_photos +class ExperimentsConfig: + experiment_dir = 'exp_22_10_2021' + iou_threshold = 0.60 @dataclass class ReportResult: @@ -88,16 +90,16 @@ def create_report(self) -> dict: pred_id_dedup_df = sorted_df.drop_duplicates(subset=[self.PREDICTED_ID], keep='first') ground_truth_id_dedup_df = sorted_df.drop_duplicates(subset=[self.GROUND_TRUTH_ID], keep='first') - found_1 = pred_id_dedup_df[pred_id_dedup_df[self.IOU] >= 0.6] - found_2 = ground_truth_id_dedup_df[ground_truth_id_dedup_df[self.IOU] >= 0.6] - if found_1.size != found_2.size: - raise Exception("Results do not have sense. Number of founded rectangles should be equal/") - badly_found = pred_id_dedup_df[pred_id_dedup_df[self.IOU] < 0.6] - not_found = ground_truth_id_dedup_df[ground_truth_id_dedup_df[self.IOU] < 0.6] + dedup_df = sorted_df.drop_duplicates(subset=[self.PREDICTED_ID], keep='first').drop_duplicates( + subset=[self.GROUND_TRUTH_ID], keep='first') + + true_positive = dedup_df[dedup_df[self.IOU] >= ExperimentsConfig().iou_threshold] + badly_found = pred_id_dedup_df[~pred_id_dedup_df.isin(true_positive)].dropna() + not_found = ground_truth_id_dedup_df[~ground_truth_id_dedup_df.isin(true_positive)].dropna() self.report = { 'identifier': self.identifier, - 'true_positive_df': found_1, + 'true_positive_df': true_positive, 'false_negative_df': not_found, 'false_positive_df': badly_found, 'ground_truth_rectangles_no': sorted_df[self.GROUND_TRUTH_ID].max() + 1 if sorted_df[ @@ -176,8 +178,8 @@ def generate(self): self.generated_reports = (self.generate_one_report(report_obj) for report_obj in self.reports_objs) return self.generated_reports - def generate_one_report(self, report: FromFileReport): - evaluation_result_path = f"data/checkpoint/{report.identifier}" + def generate_one_report(self, report: FromFileReport) -> Union[dict, None]: + evaluation_result_path = f"data/{ExperimentsConfig().experiment_dir}/checkpoint/{report.identifier}" if not report.evaluation_result_exist(evaluation_result_path): print(f"Creating report for {report.identifier}") try: @@ -192,16 +194,56 @@ def generate_one_report(self, report: FromFileReport): return report.create_report() +def f1score(tp, fp, fn): + return tp / (tp + (fp + fn) / 2) + + +def rectangles_f1score(tp_rects, fp_rects, fn_rects): + if (len(tp_rects) + 1 / 2 * (len(fp_rects) + len(fn_rects))) != 0: + return (len(tp_rects) / ( + len(tp_rects) + 1 / 2 * (len(fp_rects) + len(fn_rects)))) + else: + return 1 + + if __name__ == '__main__': - report_generator = ReportGenerator(glob.glob('data/thermal-modules/*.json'), glob.glob('data/result/*.pickle')) + report_generator = ReportGenerator(glob.glob('data/thermal-modules-final/*.json'), + glob.glob(f'data/{ExperimentsConfig().experiment_dir}/result/*.pickle')) generated_reports = report_generator.generate() + fscores = [] + all_true_positive_rects = 0 + all_false_negative_rects = 0 + all_false_positive_rects = 0 + ground_truth_images = 0 for generated_report in generated_reports: - path = f"data/thermal/{generated_report['identifier']}.JPG" + path = f"data/{ExperimentsConfig().experiment_dir}/thermal/{generated_report['identifier']}.JPG" base_image = cv2.imread(path) - true_positive_rects = list(generated_report['true_positive_df'][['ground_truth_rect', 'predicted_rect', 'IOU']].itertuples(index=False, name=None)) - false_negative_rects = list(generated_report['false_negative_df'][['ground_truth_rect', 'predicted_rect', 'IOU']].itertuples(index=False, name=None)) - false_positive_rects = list(generated_report['false_positive_df'][['ground_truth_rect', 'predicted_rect', 'IOU']].itertuples(index=False, name=None)) + true_positive_rects = list(generated_report['true_positive_df'] + [['ground_truth_rect', 'predicted_rect', 'IOU']].itertuples(index=False, name=None)) + false_negative_rects = list(generated_report['false_negative_df'] + [['ground_truth_rect', 'predicted_rect', 'IOU']].itertuples(index=False, name=None)) + false_positive_rects = list(generated_report['false_positive_df'] + [['ground_truth_rect', 'predicted_rect', 'IOU']].itertuples(index=False, name=None)) all_rects = true_positive_rects + false_positive_rects + false_negative_rects - display_image_in_actual_size(iou_rectangle_annotated_photos(all_rects, base_image)) + + # display_image_in_actual_size(iou_rectangle_annotated_photos(all_rects, base_image)) + all_true_positive_rects = all_true_positive_rects + len(true_positive_rects) + all_false_negative_rects = all_false_negative_rects + len(false_negative_rects) + all_false_positive_rects = all_false_positive_rects + len(false_positive_rects) + ground_truth_images = len(generated_report['ground_truth_rectangles']) + ground_truth_images + fscores.append(rectangles_f1score(true_positive_rects, false_positive_rects, false_negative_rects)) + + median(fscores) + + import matplotlib.pyplot as plt + + pd.Series(fscores).plot.hist(grid=True, bins=100) + plt.title('Commute Times for 1,000 Commuters') + plt.xlabel('Counts') + plt.ylabel('Commute Time') + plt.grid(axis='y', alpha=0.75) + plt.show() + print(f"Overall f1score: {f1score(all_true_positive_rects, all_false_positive_rects, all_false_negative_rects)}") + print(f"Median of f1score: {median(fscores)}") print(f"END with {report_generator.errors} errors ")
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