def get_boxes(img_c):
# Resize
img_r, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
img_c,
square_size=square_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=mag_ratio)
# Save ratio index for height
ratio_h = ratio_w = 1 / target_ratio
# preprocessing of the image
x = imgproc.normalizeMeanVariance(img_r)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = x.unsqueeze(0) # [c, h, w] to [b, c, h, w]
# forward pass
y, _ = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# Post-processing
boxes, _ = craft_utils.getDetBoxes(score_text,
score_link,
text_threshold=text_threshold,
link_threshold=link_threshold,
low_text=low_text,
poly=False)
# Coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
return boxes
def test_net(net, image, text_threshold, link_threshold, low_text, cuda, poly):
t0 = time.time()
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
args.canvas_size,
interpolation=cv2.INTER_AREA,
mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
with torch.no_grad():
y, feature = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link,
text_threshold, link_threshold,
low_text, poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
return boxes, polys, ret_score_text
def train_data_transform_webtoon(self, idx):
''' Prepare the data for training '''
image, char_bboxes = self.load_image_and_gt(idx)
region_score_GT = self.gaussian_generator.region(image, char_bboxes)
affinity_score_GT = self.gaussian_generator.affinity(
image, char_bboxes)
region_score_GT, affinity_score_GT = self.train_data_resize(
region_score_GT, affinity_score_GT)
image = imgproc.normalizeMeanVariance(image)
image = cv2.resize(image, (self.image_size, self.image_size),
interpolation=cv2.INTER_LINEAR)
confidence = np.ones(
(region_score_GT.shape[0], region_score_GT.shape[1]), np.float32)
''' Augment the data for training '''
data_augmentation = Data_Augmentation(image, region_score_GT,
affinity_score_GT, confidence)
image, region_score_GT, affinity_score_GT, confidence = data_augmentation.select_augmentation_method(
)
''' Convert the data for Model prediction '''
image = torch.from_numpy(image).float().permute(2, 0, 1)
region_score_GT = torch.from_numpy(region_score_GT / 255).float()
affinity_score_GT = torch.from_numpy(affinity_score_GT / 255).float()
confidence = torch.from_numpy(confidence).float()
return image, region_score_GT, affinity_score_GT, confidence
def test_net(canvas_size, mag_ratio, net, image, text_threshold,
link_threshold, low_text, poly, device):
# resize
img_resized, target_ratio, size_heatmap = resize_aspect_ratio(image, canvas_size,\
interpolation=cv2.INTER_LINEAR, mag_ratio=mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
x = x.to(device)
# forward pass
with torch.no_grad():
y, feature = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# Post-processing
boxes, polys = getDetBoxes(score_text, score_link, text_threshold,
link_threshold, low_text, poly)
# coordinate adjustment
boxes = adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
return boxes, polys
def process(self, craft, seq, key, sub_img):
img_resized, target_ratio, size_heatmap = resize_aspect_ratio(
sub_img, 2560, interpolation=cv2.INTER_LINEAR, mag_ratio=1.)
ratio_h = ratio_w = 1 / target_ratio
x = normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = x.unsqueeze(0) # [c, h, w] to [b, c, h, w]
x = x.to(self.device)
y, feature = craft(x)
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
boxes, polys = getDetBoxes(score_text,
score_link,
text_threshold=0.7,
link_threshold=0.4,
low_text=0.4,
poly=False)
boxes = adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None:
polys[k] = boxes[k]
result = []
for i, box in enumerate(polys):
poly = np.array(box).astype(np.int32).reshape((-1))
result.append(poly)
horizontal_list, free_list = group_text_box(result,
slope_ths=0.8,
ycenter_ths=0.5,
height_ths=1,
width_ths=1,
add_margin=0.1)
# horizontal_list = [i for i in horizontal_list if i[0] > 0 and i[1] > 0]
min_size = 20
if min_size:
horizontal_list = [
i for i in horizontal_list
if max(i[1] - i[0], i[3] - i[2]) > 10
]
free_list = [
i for i in free_list
if max(diff([c[0] for c in i]), diff([c[1]
for c in i])) > min_size
]
seq[:] = [None] * len(horizontal_list)
model, vocab = build_model(self.config)
model.load_state_dict(
torch.load(self.weights, map_location=torch.device('cpu')))
for i, ele in enumerate(horizontal_list):
ele = [0 if i < 0 else i for i in ele]
img = sub_img[ele[2]:ele[3], ele[0]:ele[1], :]
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = Image.fromarray(img.astype(np.uint8))
p = threading.Thread(target=self.predict,
args=(model, vocab, seq, key, i, img))
p.start()
p.join()
def test_net(net, image, text_threshold, link_threshold, low_text, cuda,
image_path):
t0 = time.time()
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
args.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
y, _ = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
t0 = time.time() - t0
t1 = time.time()
if args.debug:
np.save(
os.path.join(
'./debug',
os.path.basename(image_path).split('.')[0] +
'_score_text.npy'), score_text)
np.save(
os.path.join(
'./debug',
os.path.basename(image_path).split('.')[0] +
'_score_link.npy'), score_link)
# Post-processing
boxes = craft_utils.getDetBoxes(score_text, score_link, text_threshold,
link_threshold, low_text)
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
t1 = time.time() - t1
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
if args.show_time:
print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, ret_score_text
def pull_item(self, index):
# if self.get_imagename(index) == 'img_59.jpg':
# pass
# else:
# return [], [], [], [], np.array([0])
image, character_bboxes, words, confidence_mask, confidences = self.load_image_gt_and_confidencemask(index)
if len(confidences) == 0:
confidences = 1.0
else:
confidences = np.array(confidences).mean()
region_scores = np.zeros((image.shape[0], image.shape[1]), dtype=np.float32)
affinity_scores = np.zeros((image.shape[0], image.shape[1]), dtype=np.float32)
affinity_bboxes = []
if len(character_bboxes) > 0:
region_scores = self.gaussianTransformer.generate_region(region_scores.shape, character_bboxes)
affinity_scores, affinity_bboxes = self.gaussianTransformer.generate_affinity(region_scores.shape,
character_bboxes,
words)
if self.viz:
self.saveImage(self.get_imagename(index), image.copy(), character_bboxes, affinity_bboxes, region_scores,
affinity_scores,
confidence_mask)
random_transforms = [image, region_scores, affinity_scores, confidence_mask]
random_transforms = random_crop(random_transforms, (self.target_size, self.target_size), character_bboxes)
random_transforms = random_horizontal_flip(random_transforms)
random_transforms = random_rotate(random_transforms)
cvimage, region_scores, affinity_scores, confidence_mask = random_transforms
region_scores = self.resizeGt(region_scores)
affinity_scores = self.resizeGt(affinity_scores)
confidence_mask = self.resizeGt(confidence_mask)
if self.viz:
self.saveInput(self.get_imagename(index), cvimage, region_scores, affinity_scores, confidence_mask)
image = Image.fromarray(cvimage)
image = image.convert('RGB')
#image = transforms.ColorJitter(brightness=32.0 / 255, saturation=0.5)(image)
#밝기, 채 변화시키기
image = imgproc.normalizeMeanVariance(np.array(image), mean=(0.485, 0.456, 0.406),
variance=(0.229, 0.224, 0.225))
image_tensor = tf.convert_to_tensor(image, np.float32)
#image_tensor = tf.transpose(image_tensor,[2,0,1])
region_scores_tensor = tf.convert_to_tensor(region_scores / 255, np.float32)
affinity_scores_tensor = tf.convert_to_tensor(affinity_scores/255, np.float32)
confidence_mask_tensor = tf.convert_to_tensor(confidence_mask / 255, np.float32)
#print(confidences)
#self.count += 1
return image_tensor, region_scores_tensor, affinity_scores_tensor, confidence_mask_tensor, confidences
def test_net(net, image, text_threshold, link_threshold, low_text, cuda, poly,
ocr_type):
t0 = time.time()
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
args.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = x.unsqueeze(0) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
y, _ = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().detach().numpy()
score_link = y[0, :, :, 1].cpu().detach().numpy()
t0 = time.time() - t0
t1 = time.time()
# Post-processing
boxes, polys = utils.getDetBoxes(score_text, score_link, text_threshold,
link_threshold, low_text, poly, ocr_type)
# coordinate adjustment
boxes = utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
if ocr_type == 'single_char':
boxes = utils.cluster_sort(image.shape, boxes)
for k in range(len(polys)):
if polys[k] is None:
polys[k] = boxes[k]
t1 = time.time() - t1
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
if args.show_time:
print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, polys, ret_score_text
def __getitem__(self, idx):
image = imgproc.loadImage(self.image_list[idx])
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
self.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=self.mag_ratio)
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
# x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
return x, 1
def test_net(self, image, text_threshold, link_threshold, low_text, cuda, poly, refine_net=None):
t0 = time.time()
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, canvas_size, interpolation=cv2.INTER_LINEAR, mag_ratio=mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
with torch.no_grad():
y, feature = self.net(x)
# make score and link map
score_text = y[0,:,:,0].cpu().data.numpy()
score_link = y[0,:,:,1].cpu().data.numpy()
# refine link
if refine_net is not None:
with torch.no_grad():
y_refiner = refine_net(y, feature)
score_link = y_refiner[0,:,:,0].cpu().data.numpy()
t0 = time.time() - t0
t1 = time.time()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link, text_threshold, link_threshold, low_text, poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
t1 = time.time() - t1
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
# if show_time : print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, polys, ret_score_text
def test_net(self, image_opencv):
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image_opencv,
self.canvas_size,
interpolation=self.interpolation,
mag_ratio=self.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if self.cuda:
x = x.cuda()
# forward pass
y, feature = self.net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# refine link
t0 = time.time()
if self.refine_net is not None:
y_refiner = self.refine_net(y, feature)
score_link = y_refiner[0, :, :, 0].cpu().data.numpy()
t0 = time.time() - t0
t1 = time.time()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link,
self.text_threshold,
self.link_threshold,
self.low_text, self.poly)
#print(boxes)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
t1 = time.time() - t1
if self.show_time:
print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, polys
def get_prediction(net,
image,
text_threshold,
link_threshold,
low_text,
cuda,
poly,
refine_net=None):
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image, 1280, interpolation=cv2.INTER_LINEAR, mag_ratio=1.5)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
with torch.no_grad():
y, feature = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# refine link
if refine_net is not None:
with torch.no_grad():
y_refiner = refine_net(y, feature)
score_link = y_refiner[0, :, :, 0].cpu().data.numpy()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link,
text_threshold, link_threshold,
low_text, poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
return boxes, polys
def pull_item(self, index):
image, character_bboxes, words, confidence_mask, confidences = self.load_image_gt_and_confidence_mask(
index)
region_scores = np.zeros((image.shape[0], image.shape[1]),
dtype=np.float32)
affinity_scores = np.zeros((image.shape[0], image.shape[1]),
dtype=np.float32)
affinity_bboxes = []
if len(character_bboxes) > 0:
region_scores = self.gaussianTransformer.generate_region(
region_scores.shape, character_bboxes)
affinity_scores, affinity_bboxes = self.gaussianTransformer.generate_affinity(
region_scores.shape, character_bboxes, words)
if self.viz:
self.save_image(self.get_image_name(index), image.copy(),
character_bboxes, affinity_bboxes, region_scores,
affinity_scores, confidence_mask)
random_transforms = [
image, region_scores, affinity_scores, confidence_mask
]
random_transforms = random_crop(random_transforms,
(self.target_size, self.target_size),
character_bboxes)
random_transforms = random_horizontal_flip(random_transforms)
random_transforms = random_rotate(random_transforms)
cvimage, region_scores, affinity_scores, confidence_mask = random_transforms
region_scores = self.resizeGt(region_scores)
affinity_scores = self.resizeGt(affinity_scores)
confidence_mask = self.resizeGt(confidence_mask)
if self.viz:
self.save(self.get_image_name(index), cvimage, region_scores,
affinity_scores, confidence_mask)
image = Image.fromarray(cvimage)
image = image.convert('RGB')
image = transforms.ColorJitter(brightness=32.0 / 255,
saturation=0.5)(image)
image = imgproc.normalizeMeanVariance(np.array(image))
image = torch.from_numpy(image).float().permute(2, 0, 1)
region_scores_torch = torch.from_numpy(region_scores / 255).float()
affinity_scores_torch = torch.from_numpy(affinity_scores / 255).float()
confidence_mask_torch = torch.from_numpy(confidence_mask).float()
return image, region_scores_torch, affinity_scores_torch, confidence_mask_torch
def test_net(net, image, text_threshold, link_threshold, low_text, cuda):
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
opt.MAXIMUM_IMAGE_SIZE,
interpolation=cv2.INTER_LINEAR,
mag_ratio=opt.MAG_RATIO)
ratio_h = ratio_w = 1 / target_ratio
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1)
x = Variable(x.unsqueeze(0))
if cuda: x = x.cuda()
# predict
y, _ = net(x)
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# post-process : get shape of bounding box
boxes, polys, word_boxes, word_polys, line_boxes, line_polys = ltd_utils.getDetBoxes(
score_text, score_link, text_threshold, link_threshold, low_text)
boxes = ltd_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = ltd_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
word_boxes = ltd_utils.adjustResultCoordinates(word_boxes, ratio_w,
ratio_h)
word_polys = ltd_utils.adjustResultCoordinates(word_polys, ratio_w,
ratio_h)
line_boxes = ltd_utils.adjustResultCoordinates(line_boxes, ratio_w,
ratio_h)
line_polys = ltd_utils.adjustResultCoordinates(line_polys, ratio_w,
ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
for a in range(len(word_polys)):
if word_polys[a] is None: word_polys[a] = word_boxes[a]
for l in range(len(line_polys)):
if line_polys[l] is None: line_polys[l] = line_boxes[l]
return polys, word_polys, line_polys, score_text
def representative_data_gen():
for file in os.listdir(dataset_path)[:10]:
file_path = dataset_path + file
image = imgproc.loadImage(file_path)
image = cv2.resize(image,
dsize=(800, 1280),
interpolation=cv2.INTER_LINEAR)
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image, 1280, interpolation=cv2.INTER_LINEAR, mag_ratio=1.5)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
x = x.cpu().detach().numpy()
yield [x]
def test_net(net, image, text_threshold, link_threshold, low_text, cuda, poly,filename,result_folder=result_folder):
t0 = time.time()
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, canvas_size, interpolation=cv2.INTER_LINEAR, mag_ratio=mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
#cv2.imwrite("test.jpg",x)
print("###")
x = tf.expand_dims(x,0)
print(x.shape)
# forward pass
y, _ = net(x)
# make score and link map
score_text = y[0,:,:,0].numpy()
score_link = y[0,:,:,1].numpy()
t0 = time.time() - t0
t1 = time.time()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link, text_threshold, link_threshold, low_text, poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
t1 = time.time() - t1
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
#print("score")
#print(ret_score_text.shape)
cv2.imwrite(result_folder + filename + "_mask.jpg",ret_score_text)
#if show_time : print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, polys, ret_score_text
def __init__(self, args):
filelist, _, _ = file_utils.list_files('./data/train/data')
self.images = []
self.confmaps = []
self.scores_region = []
self.scores_link = []
for filename in filelist:
# get datapath
dataset = os.path.dirname(filename).split(os.sep)[-1]
filenum = os.path.splitext(os.path.basename(filename))
label_dir = './data/train/ground_truth/{}/gt_{}/'.format(
dataset, filenum)
# If not exists, generate ground truth
if not os.path.exists(label_dir):
continue
image = imgproc.loadImage(filename)
score_region = torch.load(label_dir + 'region.pt')
score_link = torch.load(label_dir + 'link.pt')
conf_map = torch.load(label_dir + 'conf.pt')
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
args.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=args.mag_ratio)
# Image Preprocess
x = imgproc.normalizeMeanVariance(img_resized)
x = x.transpose((2, 0, 1)) # [h, w, c] to [c, h, w]
h, w, _ = img_resized.shape
# GT reshape
score_region = cv2.resize(score_region, dsize=(h / 2, w / 2))
score_link = cv2.resize(score_link, dsize=(h / 2, w / 2))
conf_map = cv2.resize(conf_map, dsize=(h / 2, w / 2))
self.scores_region.append(score_region)
self.scores_link.append(score_link)
self.confmaps.append(conf_map)
self.images.append(x)
def test_net(net, image, text_threshold, link_threshold, low_text, cuda, poly):
t0 = time.time()
# リサイズ
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, args.canvas_size, interpolation=cv2.INTER_LINEAR, mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# 前処理
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# 順伝播
y, _ = net(x)
# スコア・リンクマップの作成
score_text = y[0,:,:,0].cpu().data.numpy()
score_link = y[0,:,:,1].cpu().data.numpy()
t0 = time.time() - t0
t1 = time.time()
# 後処理
boxes, polys = craft_utils.getDetBoxes(score_text, score_link, text_threshold, link_threshold, low_text, poly)
# 座標調整
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
t1 = time.time() - t1
# レンダリング結果(オプション)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
if args.show_time : print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, polys, ret_score_text
def gt_net(net, image, args):
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, args.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=args.mag_ratio)
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.tensor(x).permute(2, 0, 1).unsqueeze(0) # [h, w, c] to [b, c, h, w]
if args.cuda:
x = x.cuda()
# forward pass
with torch.no_grad():
y, feature = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
return score_text, target_ratio
def detect(self, image):
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, self.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=self.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if self.cuda:
x = x.cuda()
# forward pass
with torch.no_grad():
y, feature = self.net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# refine link
if self.refine_net is not None:
with torch.no_grad():
y_refiner = self.refine_net(y, feature)
score_link = y_refiner[0, :, :, 0].cpu().data.numpy()
# Post-processing
boxes, _ = craft_utils.getDetBoxes(score_text, score_link, self.text_threshold, self.link_threshold,
self.low_text, self.poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
toRet = []
for box in boxes:
toRet.append(box2xyxy(box, image.shape[0: 2]))
return toRet
def test_net(net, image, text_threshold, link_threshold, low_text, cuda, poly):
t0 = time.time()
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, args.canvas_size, interpolation=cv2.INTER_LINEAR, mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
y, _ = net(x)
# make score and link map
score_text = y[0,:,:,0].cpu().data.numpy()
score_link = y[0,:,:,1].cpu().data.numpy()
return score_text
def test_net(self,
net,
image,
text_threshold,
link_threshold,
low_text,
poly,
refine_net=None):
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image, 1280, interpolation=cv.INTER_LINEAR, mag_ratio=1.5)
ratio_h = ratio_w = 1 / target_ratio
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
with torch.no_grad():
y, feature = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link,
text_threshold, link_threshold,
low_text, poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
return boxes, polys, ret_score_text
def __getitem__(self, i):
# Image loading
image = imgproc.loadImage(self.images[i])
# Preprocess image
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
self.args.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=self.args.mag_ratio)
img_resized = imgproc.fill_canvas(img_resized, self.args.canvas_size)
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.tensor(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
# Load labels
label_dir = self.labels[i]
region = torch.tensor(torch.load(label_dir + 'region.pt'),
dtype=torch.float64)
link = torch.tensor(torch.load(label_dir + 'link.pt'),
dtype=torch.float64)
conf = torch.tensor(torch.load(label_dir + 'conf.pt'),
dtype=torch.float64)
return x, region, link, conf
def inference_pursedo_bboxes(self, net, image, word_bbox, word, viz=False):
word_image, MM = self.crop_image_by_bbox(image, word_bbox)
real_word_without_space = word.replace('\s', '')
real_char_nums = len(real_word_without_space)
input = word_image.copy()
scale = 64.0 / input.shape[0]
input = cv2.resize(input, None, fx=scale, fy=scale)
img_torch = torch.from_numpy(imgproc.normalizeMeanVariance(input, mean=(0.485, 0.456, 0.406),
variance=(0.229, 0.224, 0.225)))
img_torch = img_torch.permute(2, 0, 1).unsqueeze(0)
img_torch = img_torch.type(torch.FloatTensor).cuda()
scores, _ = net(img_torch)
region_scores = scores[0, :, :, 0].cpu().data.numpy()
fmf = np.uint8(np.clip(region_scores, 0, 1) * 255)
bgr_region_scores = cv2.resize(region_scores, (input.shape[1], input.shape[0]))
bgr_region_scores = cv2.cvtColor(bgr_region_scores, cv2.COLOR_GRAY2BGR)
pursedo_bboxes = watershed(input, bgr_region_scores, False)
_tmp = []
for i in range(pursedo_bboxes.shape[0]):
if np.mean(pursedo_bboxes[i].ravel()) > 2:
_tmp.append(pursedo_bboxes[i])
else:
print("filter bboxes", pursedo_bboxes[i])
pursedo_bboxes = np.array(_tmp, np.float32)
if pursedo_bboxes.shape[0] > 1:
index = np.argsort(pursedo_bboxes[:, 0, 0])
pursedo_bboxes = pursedo_bboxes[index]
confidence = self.get_confidence(real_char_nums, len(pursedo_bboxes))
bboxes = []
if confidence <= 0.5:
width = input.shape[1]
height = input.shape[0]
width_per_char = width / len(word)
for i, char in enumerate(word):
if char == ' ':
continue
left = i * width_per_char
right = (i + 1) * width_per_char
bbox = np.array([[left, 0], [right, 0], [right, height],
[left, height]])
bboxes.append(bbox)
bboxes = np.array(bboxes, np.float32)
confidence = 0.5
else:
bboxes = pursedo_bboxes
if False:
_tmp_bboxes = np.int32(bboxes.copy())
_tmp_bboxes[:, :, 0] = np.clip(_tmp_bboxes[:, :, 0], 0, input.shape[1])
_tmp_bboxes[:, :, 1] = np.clip(_tmp_bboxes[:, :, 1], 0, input.shape[0])
for bbox in _tmp_bboxes:
cv2.polylines(np.uint8(input), [np.reshape(bbox, (-1, 1, 2))], True, (255, 0, 0))
region_scores_color = cv2.applyColorMap(np.uint8(region_scores), cv2.COLORMAP_JET)
region_scores_color = cv2.resize(region_scores_color, (input.shape[1], input.shape[0]))
target = self.gaussianTransformer.generate_region(region_scores_color.shape, [_tmp_bboxes])
target_color = cv2.applyColorMap(target, cv2.COLORMAP_JET)
viz_image = np.hstack([input[:, :, ::-1], region_scores_color, target_color])
cv2.imshow("crop_image", viz_image)
cv2.waitKey()
bboxes /= scale
try:
for j in range(len(bboxes)):
ones = np.ones((4, 1))
tmp = np.concatenate([bboxes[j], ones], axis=-1)
I = np.matrix(MM).I
ori = np.matmul(I, tmp.transpose(1, 0)).transpose(1, 0)
bboxes[j] = ori[:, :2]
except Exception as e:
print(e, gt_path)
# for j in range(len(bboxes)):
# ones = np.ones((4, 1))
# tmp = np.concatenate([bboxes[j], ones], axis=-1)
# I = np.matrix(MM).I
# ori = np.matmul(I, tmp.transpose(1, 0)).transpose(1, 0)
# bboxes[j] = ori[:, :2]
bboxes[:, :, 1] = np.clip(bboxes[:, :, 1], 0., image.shape[0] - 1)
bboxes[:, :, 0] = np.clip(bboxes[:, :, 0], 0., image.shape[1] - 1)
return bboxes, region_scores, confidence
def test_net(net, image, text_threshold, link_threshold, low_text, cuda, poly):
t0 = time.time()
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
args.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
y, _ = net(x)
# # make score and link map
# score_text = y[0,:,:,0].cpu().data.numpy()
# score_link = y[0,:,:,1].cpu().data.numpy()
gh_pred = y[0, :, :, :].permute((2, 0, 1)).cpu().data.numpy()
t0 = time.time() - t0
t1 = time.time()
boxes, polys = None, None
# # Post-processing
# boxes, polys = craft_utils.getDetBoxes(score_text, text_threshold, low_text, poly)
postproc = [
craft_utils.getDetBoxes(score_text, text_threshold, low_text, poly)
for score_text in gh_pred
]
boxes_pred, polys_pred = zip(*postproc)
# # coordinate adjustment
# boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
# polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for boxes, polys in zip(boxes_pred, polys_pred):
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
t1 = time.time() - t1
# # render results (optional)
# render_img = score_text.copy()
# render_img = np.hstack((render_img, score_link))
# ret_score_text = imgproc.cvt2HeatmapImg(render_img)
if args.show_time:
print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return gh_pred, boxes_pred, polys_pred, size_heatmap
return boxes, polys, ret_score_text
def test_net(net,
image,
text_threshold,
link_threshold,
low_text,
cuda,
poly,
refine_net=None,
overlap=0.0):
t0 = time.time()
# resize
# img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, args.canvas_size, interpolation=cv2.INTER_LINEAR, mag_ratio=args.mag_ratio)
# ratio_h = ratio_w = 1 / target_ratio
img_resized = image
ratio_h = ratio_w = 1
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
# x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
# x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
#
# if cuda:
# x = x.cuda()
#
# # forward pass
# with torch.no_grad():
# y, feature = net(x)
#
# # make score and link map
# score_text = y[0,:,:,0].cpu().data.numpy()
#
# if refine_net is None:
# score_link = y[0,:,:,1].cpu().data.numpy()
# else:
# # refine link
# with torch.no_grad():
# y_refiner = refine_net(y, feature)
#
# score_link = y_refiner[0,:,:,0].cpu().data.numpy()
split_coord = []
if overlap > 0.0 and overlap < 1.0:
x, split_coord = splitOverlap(x, overlap)
x = torch.from_numpy(x).permute(0, 3, 1, 2) # [h, w, c] to [c, h, w]
# x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
with torch.no_grad():
y, feature = net(x)
# make score and link map
score_text = joinOverlap(y[:, :, :, 0].cpu().data.numpy(), split_coord)
if refine_net is None:
score_link = joinOverlap(y[:, :, :, 1].cpu().data.numpy(), split_coord)
else:
# refine link
with torch.no_grad():
y_refiner = refine_net(y, feature)
score_link = joinOverlap(y_refiner[:, :, :, 0].cpu().data.numpy(),
split_coord)
t0 = time.time() - t0
t1 = time.time()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link,
text_threshold, link_threshold,
low_text, poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
t1 = time.time() - t1
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
if args.show_time:
print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, polys, ret_score_text
def main():
import os
os.makedirs('result', exist_ok=True)
text_render.prepare_renderer()
with open('alphabet-all-v5.txt', 'r') as fp:
dictionary = [s[:-1] for s in fp.readlines()]
model_ocr = OCR(dictionary, 768)
model_ocr.load_state_dict(torch.load('ocr.ckpt', map_location='cpu'),
strict=False)
model_ocr.eval()
model = CRAFT_net()
sd = torch.load('detect.ckpt', map_location='cpu')
model.load_state_dict(sd['model'])
model = model.cpu()
model.eval()
img = cv2.imread(args.image)
img_bbox = np.copy(img)
img_bbox_all = np.copy(img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_resized, target_ratio, _, pad_w, pad_h = imgproc.resize_aspect_ratio(
img, args.size, cv2.INTER_LINEAR, mag_ratio=1)
img_to_overlay = np.copy(img_resized)
ratio_h = ratio_w = 1 / target_ratio
img_resized = imgproc.normalizeMeanVariance(img_resized)
print(img_resized.shape)
rscore, ascore, mask = test(model, img_resized)
overlay = imgproc.cvt2HeatmapImg(rscore + ascore)
boxes, polys = craft_utils.getDetBoxes(rscore, ascore, args.text_threshold,
args.link_threshold, args.low_text,
False)
boxes = craft_utils.adjustResultCoordinates(boxes,
ratio_w,
ratio_h,
ratio_net=2)
polys = craft_utils.adjustResultCoordinates(polys,
ratio_w,
ratio_h,
ratio_net=2)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
# merge textlines
polys = merge_bboxes(polys, can_merge_textline)
for [tl, tr, br, bl] in polys:
x = int(tl[0])
y = int(tl[1])
width = int(tr[0] - tl[0])
height = int(br[1] - tr[1])
cv2.rectangle(img_bbox_all, (x, y), (x + width, y + height),
color=(255, 0, 0),
thickness=2)
# run OCR for each textline
textlines = run_ocr(img_bbox, polys, dictionary, model_ocr, 32)
# merge textline to text region, filter textlines without characters
text_regions: List[BBox] = []
new_textlines = []
for (poly_regions, textline_indices,
majority_dir) in merge_bboxes_text_region(textlines):
[tl, tr, br, bl] = poly_regions
x = int(tl[0]) - 5
y = int(tl[1]) - 5
width = int(tr[0] - tl[0]) + 10
height = int(br[1] - tr[1]) + 10
text = ''
logprob_lengths = []
for textline_idx in textline_indices:
if not text:
text = textlines[textline_idx].text
else:
last_ch = text[-1]
cur_ch = textlines[textline_idx].text[0]
if ord(last_ch) > 255 and ord(cur_ch) > 255:
text += textlines[textline_idx].text
else:
text += ' ' + textlines[textline_idx].text
logprob_lengths.append((np.log(textlines[textline_idx].prob),
len(textlines[textline_idx].text)))
vc = count_valuable_text(text)
total_logprobs = 0.0
for (logprob, length) in logprob_lengths:
total_logprobs += logprob * length
total_logprobs /= sum([x[1] for x in logprob_lengths])
# filter text region without characters
if vc > 1:
region = BBox(x, y, width, height, text, np.exp(total_logprobs))
region.textline_indices = []
region.majority_dir = majority_dir
text_regions.append(region)
for textline_idx in textline_indices:
region.textline_indices.append(len(new_textlines))
new_textlines.append(textlines[textline_idx])
textlines = new_textlines
# create mask
from text_mask_utils import filter_masks, main_process
mask_resized = cv2.resize(mask, (mask.shape[1] * 2, mask.shape[0] * 2),
interpolation=cv2.INTER_LINEAR)
if pad_h > 0:
mask_resized = mask_resized[:-pad_h, :]
elif pad_w > 0:
mask_resized = mask_resized[:, :-pad_w]
mask_resized = cv2.resize(mask_resized,
(img.shape[1] // 2, img.shape[0] // 2),
interpolation=cv2.INTER_LINEAR)
img_resized_2 = cv2.resize(img, (img.shape[1] // 2, img.shape[0] // 2),
interpolation=cv2.INTER_LINEAR)
mask_resized[mask_resized > 250] = 255
text_lines = [(a.x // 2, a.y // 2, a.w // 2, a.h // 2) for a in textlines]
mask_ccs, cc2textline_assignment = filter_masks(mask_resized, text_lines)
cv2.imwrite('result/mask_filtered.png', reduce(cv2.bitwise_or, mask_ccs))
final_mask, textline_colors = main_process(img_resized_2, mask_ccs,
text_lines,
cc2textline_assignment)
final_mask = cv2.resize(final_mask, (img.shape[1], img.shape[0]),
interpolation=cv2.INTER_LINEAR)
# run inpainting
img_inpainted = run_inpainting(img, final_mask)
# translate text region texts
texts = '\n'.join([r.text for r in text_regions])
trans_ret = baidu_translator.translate('ja', 'zh-CN', texts)
translated_sentences = []
batch = len(text_regions)
if len(trans_ret) < batch:
translated_sentences.extend(trans_ret)
translated_sentences.extend([''] * (batch - len(trans_ret)))
elif len(trans_ret) > batch:
translated_sentences.extend(trans_ret[:batch])
else:
translated_sentences.extend(trans_ret)
# render translated texts
img_canvas = np.copy(img_inpainted)
for trans_text, region in zip(translated_sentences, text_regions):
print(region.text)
print(trans_text)
print(region.majority_dir, region.x, region.y, region.w, region.h)
img_bbox = cv2.rectangle(img_bbox, (region.x, region.y),
(region.x + region.w, region.y + region.h),
color=(0, 0, 255),
thickness=2)
for idx in region.textline_indices:
txtln = textlines[idx]
img_bbox = cv2.rectangle(img_bbox, (txtln.x, txtln.y),
(txtln.x + txtln.w, txtln.y + txtln.h),
color=textline_colors[idx],
thickness=2)
if region.majority_dir == 'h':
text_render.put_text_horizontal(img_canvas, trans_text,
len(region.textline_indices),
region.x, region.y, region.w,
region.h, textline_colors[idx],
None)
else:
text_render.put_text_vertical(img_canvas, trans_text,
len(region.textline_indices),
region.x, region.y, region.w,
region.h, textline_colors[idx], None)
cv2.imwrite('result/rs.png', imgproc.cvt2HeatmapImg(rscore))
cv2.imwrite('result/as.png', imgproc.cvt2HeatmapImg(ascore))
cv2.imwrite('result/textline.png', overlay)
cv2.imwrite('result/bbox.png', img_bbox)
cv2.imwrite('result/bbox_unfiltered.png', img_bbox_all)
cv2.imwrite(
'result/overlay.png',
cv2.cvtColor(
overlay_image(
img_to_overlay,
cv2.resize(overlay,
(img_resized.shape[1], img_resized.shape[0]),
interpolation=cv2.INTER_LINEAR)),
cv2.COLOR_RGB2BGR))
cv2.imwrite('result/mask.png', final_mask)
cv2.imwrite('result/masked.png',
cv2.cvtColor(img_inpainted, cv2.COLOR_RGB2BGR))
cv2.imwrite('result/final.png', cv2.cvtColor(img_canvas,
cv2.COLOR_RGB2BGR))
net = net.cuda()
#net = torch.nn.DataParallel(net)
net.load_state_dict(copyStateDict(torch.load('./weights/craft_mlt_25k.pth')))
net.eval()
# load data
image = imgproc.loadImage('./test_data/chi/0021_crop.jpg')
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image, 384, interpolation=cv2.INTER_LINEAR, mag_ratio=1.5)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
onnx_input = x.data.numpy()
x = x.cuda()
# trace export
torch.onnx.export(net,
x,
'./craft_opset10.onnx',
export_params=True,
verbose=True,
opset_version=10)
# test the inference process
def test_net(net,
image,
text_threshold,
link_threshold,
low_text,
cuda,
poly,
image_path,
refine_net=None):
t0 = time.time()
img_h, img_w, c = image.shape
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
args.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
h, w, c = image.shape
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
y, feature = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy() #리전 스코어 Region score
score_link = y[0, :, :, 1].cpu().data.numpy() #어피니티 스코어
# refine link
if refine_net is not None:
y_refiner = refine_net(y, feature)
score_link = y_refiner[0, :, :, 0].cpu().data.numpy()
t0 = time.time() - t0
t1 = time.time()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link,
text_threshold, link_threshold, 0.4,
poly) # CRAFT에서 박스를 그려주는 부분
# # coordinate adjustment #좌표설정
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
#print(scores)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
t1 = time.time() - t1
# render results (optional)
render_img = score_text.copy()
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
Plus_score_text = imgproc.cvMakeScores(render_img) ##
filename, file_ext = os.path.splitext(os.path.basename(image_path))
if args.show_time:
print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
post_folder = './output/post' # 원본이미지를 이진화한 이미지 저장
resize_folder = './output/resize' # resize된 원본 이미지 저장
if not os.path.isdir(resize_folder + '/'):
os.makedirs(resize_folder + '/')
resize_file = resize_folder + "/resize_" + filename + '_mask.jpg' #오리지널 이미지
IMG_RGB2 = cv2.cvtColor(img_resized,
cv2.COLOR_BGR2RGB) #craft에서 resize한 이미지를 RGB로 컨버트
# 합성 이미지를 만들기 위한 부분
pil_image = Image.fromarray((IMG_RGB2 * 255).astype(np.uint8))
images = np.array(pil_image)
images = cv2.cvtColor(images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(images, 0, 255, cv2.THRESH_BINARY +
cv2.THRESH_OTSU) #+ cv2.THRESH_OTSU
# 이미지 합성을 위해 이진화
text_score = cv2.resize(Plus_score_text,
None,
fx=2,
fy=2,
interpolation=cv2.INTER_LINEAR) # 다시 원본 사이즈로 조절
thresh = cv2.resize(thresh, (img_w, img_h)) # 원본 이진화 이미지
text_score = cv2.resize(text_score, (img_w, img_h)) # Region 스코어 이진화 이미지
text_score = Image.fromarray((text_score).astype(np.uint8))
text_score = np.array(text_score)
if not os.path.isdir('./output/og_bri' + '/'): # 원본 이진화 이미지 저장 폴더
os.makedirs('./output/og_bri' + '/')
if not os.path.isdir('./output/score/'): # 스코어 이진화 이미지 저장 폴더
os.makedirs('./output/score/')
cv2.imwrite('./output/og_bri' + "/og_" + filename + '.jpg',
thresh) # 원본 이진화 이미지 저장
cv2.imwrite('./output/score' + "/score_" + filename + '.jpg',
text_score) # 스코어 이진화 이미지 저장
img_h = thresh.shape[0]
img_w = thresh.shape[1]
IMG_RGB2 = cv2.resize(IMG_RGB2, (img_w, img_h)) # 다시 원본 사이즈로 resize
cv2.imwrite(resize_file, IMG_RGB2)
return boxes, polys, ret_score_text
def test_net(net,
image,
text_threshold,
link_threshold,
low_text,
cuda,
poly,
refine_net=None):
t0 = time.time()
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(
image,
args.canvas_size,
interpolation=cv2.INTER_LINEAR,
mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
with torch.no_grad():
y, feature = net(x)
# make score and link map
score_text = y[0, :, :, 0].cpu().data.numpy()
score_link = y[0, :, :, 1].cpu().data.numpy()
# refine link
if refine_net is not None:
with torch.no_grad():
y_refiner = refine_net(y, feature)
score_link = y_refiner[0, :, :, 0].cpu().data.numpy()
t0 = time.time() - t0
t1 = time.time()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link,
text_threshold, link_threshold,
low_text, poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
'处理裂开的box，相邻的放在同一组'
# 广度优先合并相邻的框
# 距离矩阵构建
all_rect_cx_cy = np.zeros((len(boxes), 2))
for i in range(len(boxes)):
box = boxes[i]
left = min(box[0][0], box[1][0], box[2][0], box[3][0])
right = max(box[0][0], box[1][0], box[2][0], box[3][0])
top = min(box[0][1], box[1][1], box[2][1], box[3][1])
bottom = max(box[0][1], box[1][1], box[2][1], box[3][1])
top = int(top)
bottom = int(bottom)
left = int(left)
right = int(right)
all_rect_cx_cy[i][0] = ((left + right) / 2) / 4
#减少x轴的影响
#还需调整
all_rect_cx_cy[i][1] = ((top + bottom) / 2)
mat_distance = []
for i in range(len(all_rect_cx_cy)):
mat_distance.append(
np.sqrt(np.sum((all_rect_cx_cy - all_rect_cx_cy[i])**2, axis=-1)))
print("generate distance mat;len:", len(mat_distance))
segment_group = []
ind_group = -1
search_queue = deque()
cnt_processed = 0
processed = set()
#广度优先
while cnt_processed < len(all_rect_cx_cy): # 只要搜索队列中有数据就一直遍历下去
if (len(search_queue) == 0):
for i in range(len(all_rect_cx_cy)):
if (i not in processed):
search_queue.append(i)
segment_group.append([])
ind_group += 1
break
current_node = search_queue.popleft() # 从队列前边获取节点，即先进先出，这是BFS的核心
if current_node not in processed: # 当前节点是否被访问过
cnt_processed += 1
processed.add(current_node)
inds = np.argsort(mat_distance[current_node])
segment_group[ind_group].append(boxes[current_node])
cnt_company = 0
distance_threshold = 20 #max(all_rect[current_node][2],all_rect[current_node][3])
# print(distance_threshold)
for index in inds: # 遍历相邻节点，判断相邻节点是否已经在搜索队列
if mat_distance[current_node][index] > distance_threshold:
break
cnt_company += 1
if cnt_company > 200:
print("error")
exit()
if index not in search_queue: # 如果相邻节点不在搜索队列则进行添加
search_queue.append(index)
'合并在同一组的框'
merge_boxes = []
for segment in segment_group:
left_s = []
right_s = []
top_s = []
bottom_s = []
for box in segment:
left = min(box[0][0], box[1][0], box[2][0], box[3][0])
right = max(box[0][0], box[1][0], box[2][0], box[3][0])
top = min(box[0][1], box[1][1], box[2][1], box[3][1])
bottom = max(box[0][1], box[1][1], box[2][1], box[3][1])
top = math.floor(top)
bottom = math.floor(bottom)
left = math.floor(left)
right = math.floor(right)
left_s.append(left)
right_s.append(right)
top_s.append(top)
bottom_s.append(bottom)
merge_boxes.append(
[min(left_s), min(top_s),
max(right_s),
max(bottom_s)])
for rect in merge_boxes:
threshold_hw = min(rect[3] - rect[1], rect[2] - rect[0]) * 0.2
crop = i_image[rect[1]:rect[3], rect[0]:rect[2]]
ret, binary_img = cv2.threshold(
crop, 175, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
_, contours, _ = cv2.findContours(binary_img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
group = []
for i in range(len(contours)):
rect_char = cv2.boundingRect(contours[i])
group.append(rect_char)
group.sort(key=lambda rect: rect[0])
last_x_start = group[0][0]
last_x_end = group[0][0] + group[0][2]
last = group[0]
i = 1
'合并=/等符号'
while i < len(group) and i >= 1:
now = group[i]
cx = now[0] + now[2] / 2
cy = now[1] + now[3] / 2
last_cy = last[1] + last[3] / 2
y_near = abs(last_cy - cy) < (last_x_end - last_x_start) * 0.6
if (last_x_start < cx and cx < last_x_end and y_near):
group.pop(i)
i -= 1
x1 = min(now[0], group[i][0])
y1 = min(now[1], group[i][1])
x2 = max(now[0] + now[2], group[i][0] + group[i][2])
y2 = max(now[1] + now[3], group[i][1] + group[i][3])
group[i] = (x1, y1, x2 - x1, y2 - y1)
else:
last_x_start = group[i][0]
last_x_end = group[i][0] + group[i][2]
last = group[i]
i += 1
if (len(group) < 4 or len(group) > 16):
continue
'检测每个框及其结果'
rect_set = []
res_set = []
def detect_rect(rect_char, binary_img):
crop_char = binary_img[rect_char[1]:rect_char[1] + rect_char[3],
rect_char[0]:rect_char[0] + rect_char[2]]
crop_char = torch.tensor(crop_char, dtype=torch.int)
crop_char = adapt_size(crop_char)
crop_char = crop_char.float().cuda()
res = classifer_box.eval(
crop_char.unsqueeze(0)).squeeze().int().item()
debug_write(
crop_char[0].cpu().int().numpy().astype(np.uint8) * 255,
config.CLASS_toString[res])
return res
for i in range(len(group)):
rect_char = group[i]
if max(rect_char[2], rect_char[3]) < threshold_hw:
continue
res = detect_rect(rect_char, binary_img)
res_set.append(res)
rect_set.append(rect_char)
res_str = ''
for i in range(len(res_set)):
res = res_set[i]
res_str += config.CLASS_toString[res]
# print('left',res)
'等号右边颜色浅 针对右边进行二值化后重新检测'
if (config.CLASS_is_eq(res)):
rect_char = rect_set[i]
crop = i_image[rect[1]:rect[3],
rect[0]:rect[2]][:,
rect_char[0] + rect_char[2]:]
if (crop.shape[0] * crop.shape[1] < 4):
break
crop = convert_to_binary_inv(crop)
debug_write(crop, '')
_, contours_right, _ = cv2.findContours(
crop, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
group_right = []
for i in range(len(contours_right)):
rect_char_right = cv2.boundingRect(contours_right[i])
group_right.append(rect_char_right)
group_right.sort(key=lambda rect: rect[0])
for rect_char in group_right:
if (max(rect_char[2], rect_char[3]) < crop.shape[0] * 0.3):
continue
res_right = detect_rect(rect_char, crop)
res_str += config.CLASS_toString[res_right]
break
eq = res_str.split('=')
if (len(eq) == 2):
global i_image_3_color
res_str = res_str.replace("/", "d")
print(res_str)
if str_to_num(eq[0]) == str_to_num(eq[1]):
cv2.rectangle(i_image_3_color, (rect[0], rect[1]),
(rect[2], rect[3]), (46, 255, 87), 2)
cv2.imwrite('./res/' + res_str + '.png',
i_image[rect[1]:rect[3], rect[0]:rect[2]])
elif eq[1] == "":
cv2.rectangle(i_image_3_color, (rect[0], rect[1]),
(rect[2], rect[3]), (46, 87, 255), 2)
cv2.imwrite('./res/' + res_str + '.png',
i_image[rect[1]:rect[3], rect[0]:rect[2]])
else:
cv2.rectangle(i_image_3_color, (rect[0], rect[1]),
(rect[2], rect[3]), (255, 46, 87), 2)
cv2.imwrite('./res/x_' + res_str + '.png',
i_image[rect[1]:rect[3], rect[0]:rect[2]])
# print(str_to_num(eq[0])
# print(str_to_num(eq[1])
# cv2.imwrite('./res/'+res_str+'.png', binary_img)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
cv2.imshow('', i_image_3_color)
cv2.waitKey()
t1 = time.time() - t1
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
if args.show_time:
print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, polys, ret_score_text
