def main(): size = 12 net = 'native_'+str(size) with open('%s/pos_%s.txt' % (net, size), 'r') as f: pos = f.readlines() with open('%s/neg_%s.txt' % (net, size), 'r') as f: neg = f.readlines() with open('%s/part_%s.txt' % (net, size), 'r') as f: part = f.readlines() print('\n'+'pos') filename_cls = 'pnet_data_for_cls.tfrecords' print('Writing') examples = [] writer = tf.python_io.TFRecordWriter(filename_cls) cur_ = 0 sum_ = len(pos) for line in pos: view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0]+'.jpg' im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 12 or w != 12: im = cv2.resize(im, (12, 12)) im = im.astype('uint8') label = np.array([0, 1], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features(feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw)})) examples.append(example) print('\n'+'neg') cur_ = 0 neg_keep = npr.choice(len(neg), size=1000000, replace=False) sum_ = len(neg_keep) for i in neg_keep: line = neg[i] view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0]+'.jpg' im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 12 or w != 12: im = cv2.resize(im, (12, 12)) im = im.astype('uint8') label = np.array([1, 0], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features(feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw)})) examples.append(example) print(len(examples)) random.shuffle(examples) for example in examples: writer.write(example.SerializeToString()) writer.close() print('\n'+'pos') cur_ = 0 filename_roi = 'pnet_data_for_bbx.tfrecords' print('Writing') sum_ = len(pos) examples = [] writer = tf.python_io.TFRecordWriter(filename_roi) for line in pos: view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0]+'.jpg' im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 12 or w != 12: im = cv2.resize(im, (12, 12)) im = im.astype('uint8') label = np.array([float(words[2]), float(words[3]), float(words[4]), float(words[5])], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features(feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw)})) examples.append(example) print('\n'+'part') cur_ = 0 part_keep = npr.choice(len(part), size=300000, replace=False) sum_ = len(part_keep) for i in part_keep: view_bar(cur_, sum_) line = part[i] cur_ += 1 words = line.split() image_file_name = words[0]+'.jpg' im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 12 or w != 12: im = cv2.resize(im, (12, 12)) im = im.astype('uint8') label = np.array([float(words[2]), float(words[3]), float(words[4]), float(words[5])], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features(feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw)})) examples.append(example) print(len(examples)) random.shuffle(examples) for example in examples: writer.write(example.SerializeToString()) writer.close()
def main(): size = 12 net = 'native_' + str(size) with open('%s/pos_%s.txt' % (net, size), 'r') as f: pos = f.readlines() with open('%s/neg_%s.txt' % (net, size), 'r') as f: neg = f.readlines() with open('%s/part_%s.txt' % (net, size), 'r') as f: part = f.readlines() print('\n' + 'pos') filename_cls = 'pnet_data_for_cls.tfrecords' print('Writing') examples = [] writer = tf.python_io.TFRecordWriter(filename_cls) cur_ = 0 sum_ = len(pos) for line in pos: view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0] + '.jpg' im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 12 or w != 12: im = cv2.resize(im, (12, 12)) im = im.astype('uint8') label = np.array([0, 1], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print('\n' + 'neg') cur_ = 0 neg_keep = npr.choice(len(neg), size=10000, replace=False) sum_ = len(neg_keep) for i in neg_keep: line = neg[i] view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0] + '.jpg' im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 12 or w != 12: im = cv2.resize(im, (12, 12)) im = im.astype('uint8') label = np.array([1, 0], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) random.shuffle(examples) for example in examples: writer.write(example.SerializeToString()) writer.close() print('\n' + 'pos') cur_ = 0 filename_roi = 'pnet_data_for_bbx.tfrecords' print('Writing') sum_ = len(pos) examples = [] writer = tf.python_io.TFRecordWriter(filename_roi) for line in pos: view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0] + '.jpg' im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 12 or w != 12: im = cv2.resize(im, (12, 12)) im = im.astype('uint8') label = np.array([ float(words[2]), float(words[3]), float(words[4]), float(words[5]) ], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print('\n' + 'part') cur_ = 0 part_keep = npr.choice(len(part), size=300000, replace=False) sum_ = len(part_keep) for i in part_keep: view_bar(cur_, sum_) line = part[i] cur_ += 1 words = line.split() image_file_name = words[0] + '.jpg' im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 12 or w != 12: im = cv2.resize(im, (12, 12)) im = im.astype('uint8') label = np.array([ float(words[2]), float(words[3]), float(words[4]), float(words[5]) ], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) random.shuffle(examples) for example in examples: writer.write(example.SerializeToString()) writer.close()
def main(input_size, classifier_tfrecord_fp, localizer_tfrecord_fp, root_data_dir): net = os.path.join(root_data_dir, str(input_size)) with open('%s/pos_%s.txt' % (net, input_size), 'r') as f: pos_hard = f.readlines() with open('%s/neg_%s.txt' % (net, input_size), 'r') as f: neg = f.readlines() with open('%s/part_%s.txt' % (net, input_size), 'r') as f: part_hard = f.readlines() with open( os.path.join( root_data_dir, 'native_' + '%s/pos_%s.txt' % (input_size, input_size)), 'r') as f: pos = f.readlines() with open( os.path.join( root_data_dir, 'native_' + '%s/part_%s.txt' % (input_size, input_size)), 'r') as f: part = f.readlines() print('\n' + 'positive hard') cur_ = 0 sum_ = len(pos_hard) print('Writing') examples = [] writer = tf.python_io.TFRecordWriter(classifier_tfrecord_fp) for line in pos_hard: view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0] im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 24 or w != 24: im = cv2.resize(im, (24, 24)) im = im.astype('uint8') label = np.array([0, 1], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) print('\n' + 'positive random cropped') cur_ = 0 pos_keep = npr.choice(len(pos), size=min(len(pos), 100000), replace=False) sum_ = len(pos_keep) print('Writing') for i in pos_keep: view_bar(cur_, sum_) cur_ += 1 line = pos[i] words = line.split() image_file_name = words[0] im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 24 or w != 24: im = cv2.resize(im, (24, 24)) im = im.astype('uint8') label = np.array([0, 1], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) print('\n' + 'negative random cropped') cur_ = 0 neg_keep = npr.choice(len(neg), size=min(len(neg), 1300000), replace=False) sum_ = len(neg_keep) for i in neg_keep: line = neg[i] view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0] im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 24 or w != 24: im = cv2.resize(im, (24, 24)) im = im.astype('uint8') label = np.array([1, 0], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) random.shuffle(examples) for example in examples: writer.write(example.SerializeToString()) writer.close() examples = [] print('\n' + 'positive random cropped') cur_ = 0 print('Writing') writer = tf.python_io.TFRecordWriter(localizer_tfrecord_fp) pos_keep = npr.choice(len(pos), size=min(len(pos), 100000), replace=False) sum_ = len(pos_keep) print('Writing') for i in pos_keep: view_bar(cur_, sum_) cur_ += 1 line = pos[i] words = line.split() image_file_name = words[0] im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 24 or w != 24: im = cv2.resize(im, (24, 24)) im = im.astype('uint8') label = np.array([ float(words[2]), float(words[3]), float(words[4]), float(words[5]) ], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) print('\n' + 'positive hard') cur_ = 0 print('Writing') sum_ = len(pos_hard) for line in pos_hard: view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0] im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 24 or w != 24: im = cv2.resize(im, (24, 24)) im = im.astype('uint8') label = np.array([ float(words[2]), float(words[3]), float(words[4]), float(words[5]) ], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) print('\n' + 'part hard') cur_ = 0 sum_ = len(part_hard) for line in part_hard: view_bar(cur_, sum_) cur_ += 1 words = line.split() image_file_name = words[0] im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 24 or w != 24: im = cv2.resize(im, (24, 24)) im = im.astype('uint8') label = np.array([ float(words[2]), float(words[3]), float(words[4]), float(words[5]) ], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) print('\n' + 'part random cropped') cur_ = 0 part_keep = npr.choice(len(part), size=min(len(part), 100000), replace=False) sum_ = len(part_keep) for i in part_keep: view_bar(cur_, sum_) line = part[i] cur_ += 1 words = line.split() image_file_name = words[0] im = cv2.imread(image_file_name) h, w, ch = im.shape if h != 24 or w != 24: im = cv2.resize(im, (24, 24)) im = im.astype('uint8') label = np.array([ float(words[2]), float(words[3]), float(words[4]), float(words[5]) ], dtype='float32') label_raw = label.tostring() image_raw = im.tostring() example = tf.train.Example(features=tf.train.Features( feature={ 'label_raw': bytes_feature(label_raw), 'image_raw': bytes_feature(image_raw) })) examples.append(example) print(len(examples)) random.shuffle(examples) for example in examples: writer.write(example.SerializeToString()) writer.close()