enable flowing from directory #60
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bertsky
reviewed
Apr 29, 2024
sbb_binarize/sbb_binarize.py
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| def run(self, image=None, image_path=None, save=None, use_patches=False): | ||
| if (image is not None and image_path is not None) or \ | ||
| (image is None and image_path is None): | ||
| raise ValueError("Must pass either a opencv2 image or an image_path") | ||
| if image_path is not None: | ||
| image = cv2.imread(image_path) | ||
| img_last = 0 | ||
| for n, (model, model_file) in enumerate(zip(self.models, self.model_files)): | ||
| self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files))) | ||
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| res = self.predict(model, image, use_patches) | ||
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| img_fin = np.zeros((res.shape[0], res.shape[1], 3)) | ||
| res[:, :][res[:, :] == 0] = 2 | ||
| res = res - 1 | ||
| res = res * 255 | ||
| img_fin[:, :, 0] = res | ||
| img_fin[:, :, 1] = res | ||
| img_fin[:, :, 2] = res | ||
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| img_fin = img_fin.astype(np.uint8) | ||
| img_fin = (res[:, :] == 0) * 255 | ||
| img_last = img_last + img_fin | ||
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| kernel = np.ones((5, 5), np.uint8) | ||
| img_last[:, :][img_last[:, :] > 0] = 255 | ||
| img_last = (img_last[:, :] == 0) * 255 | ||
| if save: | ||
| cv2.imwrite(save, img_last) | ||
| return img_last | ||
| def run(self, image=None, image_path=None, save=None, use_patches=False, dir_in=None, dir_out=None): | ||
| print(dir_in,'dir_in') | ||
| if not dir_in: | ||
| if (image is not None and image_path is not None) or \ | ||
| (image is None and image_path is None): | ||
| raise ValueError("Must pass either a opencv2 image or an image_path") | ||
| if image_path is not None: | ||
| image = cv2.imread(image_path) | ||
| img_last = 0 | ||
| for n, (model, model_file) in enumerate(zip(self.models, self.model_files)): | ||
| self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files))) | ||
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| res = self.predict(model, image, use_patches) | ||
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| img_fin = np.zeros((res.shape[0], res.shape[1], 3)) | ||
| res[:, :][res[:, :] == 0] = 2 | ||
| res = res - 1 | ||
| res = res * 255 | ||
| img_fin[:, :, 0] = res | ||
| img_fin[:, :, 1] = res | ||
| img_fin[:, :, 2] = res | ||
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| img_fin = img_fin.astype(np.uint8) | ||
| img_fin = (res[:, :] == 0) * 255 | ||
| img_last = img_last + img_fin | ||
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| kernel = np.ones((5, 5), np.uint8) | ||
| img_last[:, :][img_last[:, :] > 0] = 255 | ||
| img_last = (img_last[:, :] == 0) * 255 | ||
| if save: | ||
| cv2.imwrite(save, img_last) | ||
| return img_last | ||
| else: | ||
| ls_imgs = os.listdir(dir_in) | ||
| for image_name in ls_imgs: | ||
| print(image_name,'image_name') | ||
| image = cv2.imread(os.path.join(dir_in,image_name) ) | ||
| img_last = 0 | ||
| for n, (model, model_file) in enumerate(zip(self.models, self.model_files)): | ||
| self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files))) | ||
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| res = self.predict(model, image, use_patches) | ||
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| img_fin = np.zeros((res.shape[0], res.shape[1], 3)) | ||
| res[:, :][res[:, :] == 0] = 2 | ||
| res = res - 1 | ||
| res = res * 255 | ||
| img_fin[:, :, 0] = res | ||
| img_fin[:, :, 1] = res | ||
| img_fin[:, :, 2] = res | ||
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| img_fin = img_fin.astype(np.uint8) | ||
| img_fin = (res[:, :] == 0) * 255 | ||
| img_last = img_last + img_fin | ||
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| kernel = np.ones((5, 5), np.uint8) | ||
| img_last[:, :][img_last[:, :] > 0] = 255 | ||
| img_last = (img_last[:, :] == 0) * 255 | ||
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| cv2.imwrite(os.path.join(dir_out,image_name), img_last) |
There was a problem hiding this comment.
Instead of duplicating the whole function as a loop, I recommend a little refactoring:
- rewrite the function to a loop (which can be a single image)
- allow the kwarg
image_pathto be ambiguous between a single file and a directory, check and convert to loop – no need for new kwargdir_in - allow the kwarg
saveto be a directory in the same way – no need for the new kwargdir_out - raise exception if
saveis not a directory butimage_pathis
|
Also, I wonder if this is even needed – #48 already covers prediction of a directory... |
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adds the option to use a directory as input for batch processing