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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,46 @@ | ||
| from dface.prepare_data.widerface_annotation_gen.wider_loader import WIDER | ||
| import cv2 | ||
| import time | ||
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| #wider face original images path | ||
| path_to_image = '/idata/data/wider_face/WIDER_train/images' | ||
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| #matlab file path | ||
| file_to_label = './wider_face_train.mat' | ||
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| #target file path | ||
| target_file = './anno.txt' | ||
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| wider = WIDER(file_to_label, path_to_image) | ||
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| line_count = 0 | ||
| box_count = 0 | ||
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| print('start transforming....') | ||
| t = time.time() | ||
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| with open(target_file, 'w+') as f: | ||
| # press ctrl-C to stop the process | ||
| for data in wider.next(): | ||
| line = [] | ||
| line.append(str(data.image_name)) | ||
| line_count += 1 | ||
| for i,box in enumerate(data.bboxes): | ||
| box_count += 1 | ||
| for j,bvalue in enumerate(box): | ||
| line.append(str(bvalue)) | ||
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| line.append('\n') | ||
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| line_str = ' '.join(line) | ||
| f.write(line_str) | ||
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| st = time.time()-t | ||
| print('end transforming') | ||
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| print('spend time:%ld'%st) | ||
| print('total line(images):%d'%line_count) | ||
| print('total boxes(faces):%d'%box_count) | ||
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dface/prepare_data/widerface_annotation_gen/wider_loader.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,42 @@ | ||
| import h5py | ||
| import os | ||
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| class DATA(object): | ||
| def __init__(self, image_name, bboxes): | ||
| self.image_name = image_name | ||
| self.bboxes = bboxes | ||
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| class WIDER(object): | ||
| def __init__(self, file_to_label, path_to_image): | ||
| self.file_to_label = file_to_label | ||
| self.path_to_image = path_to_image | ||
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| self.f = h5py.File(file_to_label, 'r') | ||
| self.event_list = self.f.get('event_list') | ||
| self.file_list = self.f.get('file_list') | ||
| self.face_bbx_list = self.f.get('face_bbx_list') | ||
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| def next(self): | ||
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| for event_idx, event in enumerate(self.event_list.value[0]): | ||
| directory = self.f[event].value.tostring().decode('utf-16') | ||
| for im_idx, im in enumerate( | ||
| self.f[self.file_list.value[0][event_idx]].value[0]): | ||
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| im_name = self.f[im].value.tostring().decode('utf-16') | ||
| face_bbx = self.f[self.f[self.face_bbx_list.value | ||
| [0][event_idx]].value[0][im_idx]].value | ||
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| bboxes = [] | ||
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| for i in range(face_bbx.shape[1]): | ||
| xmin = int(face_bbx[0][i]) | ||
| ymin = int(face_bbx[1][i]) | ||
| xmax = int(face_bbx[0][i] + face_bbx[2][i]) | ||
| ymax = int(face_bbx[1][i] + face_bbx[3][i]) | ||
| bboxes.append((xmin, ymin, xmax, ymax)) | ||
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| yield DATA(os.path.join(self.path_to_image, directory, | ||
| im_name + '.jpg'), bboxes) |