def test_rect_synth800k():
data_root = '/Users/luning/Dev/data/SynthText/SynthText/'
img_fn = 'desert_78_83'
gt_fn = data_root + 'gt.mat'
targets = {}
targets = sio.loadmat(gt_fn,
targets,
squeeze_me=True,
struct_as_record=False,
variable_names=['imnames', 'wordBB', 'txt'])
imageNames = targets['imnames']
wordBBoxes = targets['wordBB']
transcripts = targets['txt']
mask = [True if img_fn in i else False for i in imageNames]
index = np.where(mask)[0][0]
print(index)
img_fn = imageNames[index]
img_fn = data_root + img_fn
img = cv2.imread(img_fn)
boxes = wordBBoxes[index]
transcripts = transcripts[index]
transcripts = [word for line in transcripts for word in line.split()]
boxes = np.expand_dims(boxes, axis=2) if (boxes.ndim == 2) else boxes
_, _, numOfWords = boxes.shape
boxes = boxes.reshape([8, numOfWords], order='F').T # num_words * 8
for i in range(len(boxes)):
x1, y1, x2, y2, x3, y3, x4, y4 = boxes[i]
print(boxes[i])
rotated_rect = cv2.minAreaRect(
np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]))
print(rotated_rect)
box_w, box_h = rotated_rect[0][0], rotated_rect[0][1]
if box_w < box_h:
box_w, box_h = box_h, box_w
width = 8 * box_w / box_h
print(width)
show_box(img, boxes[i].reshape(4, 2), transcripts[i])
def test_rect_icdar():
img_fn = '/Users/luning/Dev/data/icdar/icdar2015/4.4/training/ch4_training_images/img_283.jpg'
img_gt = '/Users/luning/Dev/data/icdar/icdar2015/4.4/training/ch4_training_localization_transcription_gt/gt_img_283.txt'
img = cv2.imread(img_fn)
boxes = []
transcripts = []
with open(img_gt, mode='r', encoding='utf-8-sig') as f:
for line in f:
text = line.strip().split(',')
box = [int(x) for x in text[:8]]
transcripts.append(text[8])
boxes.append(box)
boxes = np.array(boxes).astype(np.float32)
for i in range(len(boxes)):
x1, y1, x2, y2, x3, y3, x4, y4 = boxes[i]
print(boxes[i])
rotated_rect = cv2.minAreaRect(
np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]))
print(rotated_rect)
show_box(img, boxes[i].reshape(4, 2), transcripts[i])