def detect(self, im):
"""
Detecting texts from an image
:return: the bounding boxes of the detected texts
"""
text_proposals, scores = self.text_proposal_detector.detect(
im, cfg.MEAN)
keep_inds = np.where(scores > cfg.TEXT_PROPOSALS_MIN_SCORE)[0]
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores = text_proposals[sorted_indices], scores[
sorted_indices]
# nms for text proposals
keep_inds = nms(np.hstack((text_proposals, scores)),
cfg.TEXT_PROPOSALS_NMS_THRESH)
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
scores = normalize(scores)
text_lines = self.text_proposal_connector.get_text_lines(
text_proposals, scores, im.shape[:2])
keep_inds = self.filter_boxes(text_lines)
text_lines = text_lines[keep_inds]
# nms for text lines
if text_lines.shape[0] != 0:
keep_inds = nms(text_lines, cfg.TEXT_LINE_NMS_THRESH)
text_lines = text_lines[keep_inds]
text_lines = sorted(text_lines,
key=lambda x: -float(x[1]),
reverse=True)
return text_lines
def detect(self, im):
"""
Detecting texts from an image
:return: the bounding boxes of the detected texts
"""
text_proposals, scores=self.text_proposal_detector.detect(im, cfg.MEAN)
keep_inds=np.where(scores>cfg.TEXT_PROPOSALS_MIN_SCORE)[0]
text_proposals, scores=text_proposals[keep_inds], scores[keep_inds]
sorted_indices=np.argsort(scores.ravel())[::-1]
text_proposals, scores=text_proposals[sorted_indices], scores[sorted_indices]
# nms for text proposals
keep_inds=nms(np.hstack((text_proposals, scores)), cfg.TEXT_PROPOSALS_NMS_THRESH)
text_proposals, scores=text_proposals[keep_inds], scores[keep_inds]
scores=normalize(scores)
text_lines=self.text_proposal_connector.get_text_lines(text_proposals, scores, im.shape[:2])
keep_inds=self.filter_boxes(text_lines)
text_lines=text_lines[keep_inds]
# nms for text lines
if text_lines.shape[0]!=0:
keep_inds=nms(text_lines, cfg.TEXT_LINE_NMS_THRESH)
text_lines=text_lines[keep_inds]
return text_lines
def detect(root, img_file, model_file):
net = Net.CTPN()
net.load_state_dict(torch.load(model_file))
# net.cuda()
print(net)
net.eval()
text_connector = TextProposalConnector()
im = cv2.imread(os.path.join(root, img_file))
# im = Dataset.scale_img(im, None, shortest_side=600)
img = copy.deepcopy(im)
img = img.transpose(2, 0, 1)
img = img[np.newaxis, :, :, :]
img = torch.Tensor(img)
# img = img.cuda()
v, score, side = net(img, val=True)
score = score.cpu().detach().numpy()[:, :, :, 1]
result = np.where(score > 0.7)
for_nms = []
for anchor, height, width in zip(result[0], result[1], result[2]):
vc = v[anchor, 0, height, width]
vh = v[anchor, 1, height, width]
cya = height * 16 + 7.5
ha = anchor_height[anchor]
cy = vc * ha + cya
h = math.pow(10, vh) * ha
pt = other.trans_to_2pt(width, cy, h)
for_nms.append(
[pt[0], pt[1], pt[2], pt[3], score[anchor, height, width]])
for_nms = np.array(for_nms, dtype=np.float32)
nms_result = nms.cpu_nms(for_nms, TEXT_LINE_NMS_THRESH)
text_proposals = []
text_proposal_score = []
for i in nms_result:
text_proposals.append(for_nms[i, 0:4])
text_proposal_score.append(for_nms[i, 4])
text_proposals = np.array(text_proposals)
text_proposal_score = np.array(text_proposal_score)
text_proposal_score = other.normalize(text_proposal_score)
text_lines = text_connector.get_text_lines(text_proposals,
text_proposal_score,
im.shape[:2])
keep_index = filter_boxes(text_lines)
text_lines = text_lines[keep_index]
# nms for text lines
if text_lines.shape[0] != 0:
keep_inds = nms.cpu_nms(text_lines, TEXT_LINE_NMS_THRESH)
text_lines = text_lines[keep_inds]
rec = other.draw_boxes(im, text_lines)
rec_file = open(os.path.join(img_dir,
img_file.split('.')[0] + '.txt'), 'w')
rec = rec.tolist()
for box in rec:
box = [str(pt) for pt in box]
rec_file.write(','.join(box))
rec_file.write('\n')
def detect(self, im):
"""
Detecting text boxes from an image
:return: the bounding boxes of the detected texts
"""
text_proposals, scores = self.text_proposal_detector.detect(
im, cfg.MEAN)
keep_inds = np.where(scores > cfg.TEXT_PROPOSALS_MIN_SCORE)[0]
text_proposals = text_proposals[keep_inds]
scores = scores[keep_inds]
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores = text_proposals[sorted_indices], scores[
keep_inds]
# nms for text propoasls
keep_inds = nms(np.hstack((text_proposals, scores)),
cfg.TEXT_PROPOSALS_NMS_THRESH)
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
# 得分归一化统一为[0,1]之间
scores = normalize(scores)
text_lines = self.text_proposal_connector.get_text_lines(
text_proposals, scores, im.shape[:2])
# text_lines:(x1, x2, y1, y2, score)
# 筛选text boxes
keep_inds = self.filter_boxes(text_lines)
text_lines = text_lines[keep_inds]
if text_lines.shape[0] != 0:
keep_inds = nms(text_lines, cfg.TETEXT_LINE_NMS_THRESHXL)
text_lines = text_lines[keep_inds]
return text_lines
def detect(self, im):
"""
Detecting texts from an image
:return: the bounding boxes of the detected texts
"""
# import cv2
# cv2.imshow('111', im)
# cv2.waitKey(0)
text_proposals, scores = self.text_proposal_detector.detect(
im, cfg.MEAN)
keep_inds = np.where(scores > cfg.TEXT_PROPOSALS_MIN_SCORE)[0]
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores = text_proposals[sorted_indices], scores[
sorted_indices]
# nms for text proposals
keep_inds = nms(np.hstack((text_proposals, scores)),
cfg.TEXT_PROPOSALS_NMS_THRESH)
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
scores = normalize(scores)
text_lines = self.text_proposal_connector.get_text_lines_rotate(
text_proposals, scores, im.shape[:2])
keep_inds = self.filter_boxes(text_lines)
text_lines = text_lines[keep_inds]
# nms for text lines
if text_lines.shape[0] != 0:
keep_inds = nms(text_lines, cfg.TEXT_LINE_NMS_THRESH)
text_lines = text_lines[keep_inds]
return text_lines
