def get_det_boxes(image, display=True, expand=True):
image = resize(image, height=height)
image_r = image.copy()
image_c = image.copy()
h, w = image.shape[:2]
image = image.astype(np.float32) - config.IMAGE_MEAN
image = torch.from_numpy(image.transpose(2, 0, 1)).unsqueeze(0).float()
with torch.no_grad():
image = image.to(device)
cls, regr = model(image)
cls_prob = F.softmax(cls, dim=-1).cpu().numpy()
regr = regr.cpu().numpy()
anchor = gen_anchor((int(h / 16), int(w / 16)), 16)
bbox = bbox_transfor_inv(anchor, regr)
bbox = clip_box(bbox, [h, w])
# print(bbox.shape)
fg = np.where(cls_prob[0, :, 1] > prob_thresh)[0]
# print(np.max(cls_prob[0, :, 1]))
select_anchor = bbox[fg, :]
select_score = cls_prob[0, fg, 1]
select_anchor = select_anchor.astype(np.int32)
# print(select_anchor.shape)
keep_index = filter_bbox(select_anchor, 16)
# nms
select_anchor = select_anchor[keep_index]
select_score = select_score[keep_index]
select_score = np.reshape(select_score, (select_score.shape[0], 1))
nmsbox = np.hstack((select_anchor, select_score))
keep = nms(nmsbox, 0.3)
# print(keep)
select_anchor = select_anchor[keep]
select_score = select_score[keep]
# text line-
textConn = TextProposalConnectorOriented()
text = textConn.get_text_lines(select_anchor, select_score, [h, w])
# expand text
if expand:
for idx in range(len(text)):
text[idx][0] = max(text[idx][0] - 10, 0)
text[idx][2] = min(text[idx][2] + 10, w - 1)
text[idx][4] = max(text[idx][4] - 10, 0)
text[idx][6] = min(text[idx][6] + 10, w - 1)
# print(text)
if display:
blank = np.zeros(image_c.shape, dtype=np.uint8)
for box in select_anchor:
pt1 = (box[0], box[1])
pt2 = (box[2], box[3])
blank = cv2.rectangle(blank, pt1, pt2, (50, 0, 0), -1)
image_c = image_c + blank
image_c[image_c > 255] = 255
for i in text:
s = str(round(i[-1] * 100, 2)) + '%'
i = [int(j) for j in i]
cv2.line(image_c, (i[0], i[1]), (i[2], i[3]), (0, 0, 255), 2)
cv2.line(image_c, (i[0], i[1]), (i[4], i[5]), (0, 0, 255), 2)
cv2.line(image_c, (i[6], i[7]), (i[2], i[3]), (0, 0, 255), 2)
cv2.line(image_c, (i[4], i[5]), (i[6], i[7]), (0, 0, 255), 2)
cv2.putText(image_c, s, (i[0] + 13, i[1] + 13),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2,
cv2.LINE_AA)
# dis(image_c)
# print(text)
return text, image_c, image_r
# if __name__ == '__main__':
# img_path = 'images/t1.png'
# image = cv2.imread(img_path)
# text,image = get_det_boxes(image)
# dis(image)
def get_det_boxes(image, display=True, expand=True):
image = resize(image, height=height) #resize hình ảnh
image_r = image.copy() #copy
image_c = image.copy()
h, w = image.shape[:2] #lấy ra chiều dài và chiều rộng
image = image.astype(
np.float32) - config.IMAGE_MEAN #quy định mảng hình ảnh float32
image = torch.from_numpy(image.transpose(
2, 0, 1)).unsqueeze(0).float() #tạo ra tensor từ ma trận chuyển vị
with torch.no_grad():
#sẽ làm cho tất cả các hoạt động trong khối không có gradient.
image = image.to(device)
cls, regr = model(image)
cls_prob = F.softmax(cls, dim=-1).cpu().numpy()
regr = regr.cpu().numpy()
anchor = gen_anchor((int(h / 16), int(w / 16)), 16)
bbox = bbox_transfor_inv(anchor, regr)
bbox = clip_box(bbox, [h, w])
# print(bbox.shape)
fg = np.where(cls_prob[0, :, 1] > prob_thresh)[0]
# print(np.max(cls_prob[0, :, 1]))
select_anchor = bbox[fg, :]
select_score = cls_prob[0, fg, 1]
select_anchor = select_anchor.astype(np.int32)
# print(select_anchor.shape)
keep_index = filter_bbox(select_anchor, 16)
# nms
select_anchor = select_anchor[keep_index]
select_score = select_score[keep_index]
select_score = np.reshape(select_score, (select_score.shape[0], 1))
nmsbox = np.hstack((select_anchor, select_score))
keep = nms(nmsbox, 0.3)
# print(keep)
select_anchor = select_anchor[keep]
select_score = select_score[keep]
# text line-
textConn = TextProposalConnectorOriented()
text = textConn.get_text_lines(select_anchor, select_score, [h, w])
# expand text
if expand:
for idx in range(len(text)):
text[idx][0] = max(text[idx][0] - 10, 0)
text[idx][2] = min(text[idx][2] + 10, w - 1)
text[idx][4] = max(text[idx][4] - 10, 0)
text[idx][6] = min(text[idx][6] + 10, w - 1)
# print(text)
if display:
blank = np.zeros(image_c.shape, dtype=np.uint8)
for box in select_anchor:
pt1 = (box[0], box[1])
pt2 = (box[2], box[3])
blank = cv2.rectangle(blank, pt1, pt2, (50, 0, 0), -1)
image_c = image_c + blank
image_c[image_c > 255] = 255
for i in text:
s = str(round(i[-1] * 100, 2)) + '%'
i = [int(j) for j in i]
cv2.line(image_c, (i[0], i[1] - 2), (i[2], i[3] - 2),
(0, 0, 255), 1)
cv2.line(image_c, (i[0], i[1]), (i[4], i[5]), (0, 0, 255), 1)
cv2.line(image_c, (i[6], i[7]), (i[2], i[3]), (0, 0, 255), 1)
cv2.line(image_c, (i[4], i[5] + 2), (i[6], i[7] + 2),
(0, 0, 255), 1)
# cv2.putText(image_c, str(k), (i[0]+13, i[1]+13),
# cv2.FONT_HERSHEY_SIMPLEX,
# 1,
# (255,0,0),
# 2,
# cv2.LINE_AA)
return text, image_c, image_r
