def recognize(shape, rotate_img, rotate_flag, handword_model, handnum_model,
word_model, num_model, char_model, seal_model):
results = []
models = {
'handword': handword_model,
'handnum': handnum_model,
'num': num_model,
'word': word_model,
'char': char_model,
'seal': seal_model
}
for i, item in enumerate(shape):
print('*' * 30)
mode = item['class']
print('mode：', mode)
box = item['box']
theta = crop_mode.find_min_rect_angle(box)
tmp_img, vertice = crop_mode.rotate_img(rotate_img, box,
-theta / math.pi * 180)
x_min, x_max, y_min, y_max = crop_mode.get_boundary(vertice)
crop_img = tmp_img.crop(
(int(x_min), int(y_min), int(x_max), int(y_max)))
if rotate_flag == 0:
width = img.width
height = img.height
center_x = (width - 1) / 2
center_y = (height - 1) / 2
new_vertice = np.zeros(box.shape)
new_vertice[:] = rotate_vertices(
box, -math.pi, np.array([[center_x], [center_y]]))
box = new_vertice
str_box = []
for site in box:
str_box.append(str(int(site)))
print('box：', str_box)
alphabet = alphabetdict[mode]
converter = convert.strLabelConverter(alphabet)
now_model = models[mode]
result = text_recognize(crop_img, now_model, converter, mode)
print('name：', item['name'])
print('result：', result)
results.append([item['name'], result])
return results
def seal_detect(img, model, ratio_w, ratio_h):
boxes = detect.detect(img, model, device)
boxes = detect.adjust_ratio(boxes, ratio_w, ratio_h)
orig_vertices = []
theta = 0
if boxes is not None and boxes.size:
for box in boxes:
box = np.array(box[:8])
orig_vertices.append(box)
theta += crop_mode.find_min_rect_angle(box)
orig_vertices = np.array(orig_vertices)
theta /= len(boxes)
tmp_img, vertices = crop_mode.rotate_allimg(img, orig_vertices,
-theta / math.pi * 180)
dict_centers = {}
for i, vertice in enumerate(vertices):
avg_x = int(crop_mode.averagenum(vertice[::2]))
avg_y = int(crop_mode.averagenum(vertice[1::2]))
dict_centers[str(avg_x) + ',' + str(avg_y)] = i
centers = crop_mode.sort_centers(dict_centers, 1)
xcenters = []
for center in centers:
xcenters.append([center])
shape = []
for i, xcenter in enumerate(xcenters):
for center in xcenter:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'seal'
shape.append(anno)
return shape, boxes
def predict_mode3(img, img_name, rotate_model, seal_detect_model, detect_model,
handword_model, handnum_model, word_model, num_model,
char_model, seal_model):
rotate_flag = rotate_det(img, rotate_model)
if rotate_flag == 0:
rotate_img = img.rotate(180, Image.BILINEAR)
else:
rotate_img = img
rotate_img = rotate_img.convert("RGB")
w, h = rotate_img.size
ratio_w = 512 / w
ratio_h = 512 / h
img_tmp = rotate_img.resize((512, 512))
shape, seal_boxes = seal_detect(img_tmp, seal_detect_model, ratio_w,
ratio_h)
boxes = detect.detect(img_tmp, detect_model)
boxes = detect.adjust_ratio(boxes, ratio_w, ratio_h)
plot_img = detect.plot_boxes(rotate_img, boxes)
plot_img = detect.plot_boxes(plot_img, seal_boxes)
plot_img.save(
os.path.join('/home/flask_web/uploads',
img_name[:-4] + "_result" + ".jpg"))
print('detection result saved')
orig_vertices = []
theta = 0
for box in boxes:
box = np.array(box[:8])
orig_vertices.append(box)
theta += crop_mode.find_min_rect_angle(box)
orig_vertices = np.array(orig_vertices)
theta /= len(boxes)
tmp_img, vertices = crop_mode.rotate_allimg(rotate_img, orig_vertices,
-theta / math.pi * 180)
dict_centers = {}
for i, vertice in enumerate(vertices):
avg_x = int(crop_mode.averagenum(vertice[::2]))
avg_y = int(crop_mode.averagenum(vertice[1::2]))
dict_centers[str(avg_x) + ',' + str(avg_y)] = i
centers = crop_mode.sort_centers(dict_centers, 1)
k = 0
xcenters = []
index = [3, 3, 1, 2, 6, 3]
for i, j in enumerate(index):
xcenter = crop_mode.sort_xcenters(centers[k:k + j], 0)
if i == 4:
if int(xcenter[0][0].split(',')[1]) > int(
xcenter[1][0].split(',')[1]):
tmp = xcenter[0]
xcenter[0] = xcenter[1]
xcenter[1] = tmp
if int(xcenter[2][0].split(',')[1]) > int(
xcenter[3][0].split(',')[1]):
tmp = xcenter[2]
xcenter[2] = xcenter[3]
xcenter[3] = tmp
if int(xcenter[3][0].split(',')[1]) > int(
xcenter[4][0].split(',')[1]):
tmp = xcenter[3]
xcenter[3] = xcenter[4]
xcenter[4] = tmp
k += j
xcenters.append(xcenter)
for i, xcenter in enumerate(xcenters):
if i == 0:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '年'
shape.append(anno)
if j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '月'
shape.append(anno)
if j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '日'
shape.append(anno)
else:
break
if i == 1:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '收款人'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '代理付款行'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '代理付款行号'
shape.append(anno)
else:
break
if i == 2:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票金额'
shape.append(anno)
else:
break
if i == 3:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '实际结算金额（大写）'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handnum'
anno['name'] = '实际结算金额（小写）'
shape.append(anno)
else:
break
if i == 4:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '账号'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '密押'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '申请人'
shape.append(anno)
elif j == 3:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '出票行'
shape.append(anno)
elif j == 4:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '备注'
shape.append(anno)
elif j == 5:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '出票行号'
shape.append(anno)
else:
break
if i == 5:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handnum'
anno['name'] = '多余金额'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '复核'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '记账'
shape.append(anno)
else:
break
results = recognize(shape, rotate_img, rotate_flag, handword_model,
handnum_model, word_model, num_model, char_model,
seal_model)
return results
def do_predict(app, img_path, origin_name):
img_name = img_path[-36:]
db_image = Image.query.filter_by(img_name=img_name).first()
img_mode = int(origin_name[14])
print('image mode is : ', img_mode)
img = PIL_Image.open(img_path).convert('L')
if img_mode == 1:
db_image.img_mode = 1
# results = predict.predict_mode1(img, img_name, rotate_model, seal_model_mode1, detect_model_mode1,
# handword_model, handnum_model, word_model, num_model, char_model, seal_model)
rotate_flag = rotate_det(img, rotate_model)
if rotate_flag == 0:
rotate_img = img.rotate(180, PIL_Image.BILINEAR)
else:
rotate_img = img
rotate_img = rotate_img.convert("RGB")
w, h = rotate_img.size
ratio_w = 512 / w
ratio_h = 512 / h
img_tmp = rotate_img.resize((512, 512))
seal_model_mode1 = EAST(pretrained=False).to(device)
seal_model_mode1.load_state_dict(torch.load(stamp_model_one, map_location=torch.device('cpu')))
seal_model_mode1.eval()
shape, seal_boxes = seal_detect(img_tmp, seal_model_mode1, ratio_w, ratio_h)
detect_model_mode1 = EAST(pretrained=False).to(device)
detect_model_mode1.load_state_dict(torch.load(detect_model_one, map_location=torch.device('cpu')))
detect_model_mode1.eval()
boxes = detect.detect(img_tmp, detect_model_mode1, device)
boxes = detect.adjust_ratio(boxes, ratio_w, ratio_h)
plot_img = detect.plot_boxes(rotate_img, boxes)
plot_img = detect.plot_boxes(plot_img, seal_boxes)
plot_img.save(os.path.join(app.config['UPLOAD_PATH'], img_name[:-4] + "_result" + ".jpg"))
print('detection result saved')
orig_vertices = []
theta = 0
for box in boxes:
box = np.array(box[:8])
orig_vertices.append(box)
theta += crop_mode.find_min_rect_angle(box)
orig_vertices = np.array(orig_vertices)
theta /= len(boxes)
tmp_img, vertices = crop_mode.rotate_allimg(rotate_img, orig_vertices, - theta / math.pi * 180)
dict_centers = {}
for i, vertice in enumerate(vertices):
avg_x = int(crop_mode.averagenum(vertice[::2]))
avg_y = int(crop_mode.averagenum(vertice[1::2]))
dict_centers[str(avg_x) + ',' + str(avg_y)] = i
centers = crop_mode.sort_centers(dict_centers, 1)
k = 0
xcenters = []
index = [6, 2, 2, 1, 2]
for i, j in enumerate(index):
xcenter = crop_mode.sort_xcenters(centers[k:k + j], 0)
if i == 0:
if int(xcenter[0][0].split(',')[1]) < int(xcenter[1][0].split(',')[1]):
tmp = xcenter[0]
xcenter[0] = xcenter[1]
xcenter[1] = tmp
if int(xcenter[5][0].split(',')[1]) < int(xcenter[4][0].split(',')[1]):
tmp = xcenter[5]
xcenter[5] = xcenter[4]
xcenter[4] = tmp
k += j
xcenters.append(xcenter)
for i, xcenter in enumerate(xcenters):
if i == 0:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '收款人'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（年）'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（月）'
shape.append(anno)
elif j == 3:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（日）'
shape.append(anno)
elif j == 4:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '出票人账号'
shape.append(anno)
elif j == 5:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '付款行名称'
shape.append(anno)
else:
break
if i == 1:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '人民币（大写）'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handnum'
anno['name'] = '人民币（小写）'
shape.append(anno)
else:
break
if i == 2:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '行号'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '用途'
shape.append(anno)
else:
break
if i == 3:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '密码'
shape.append(anno)
else:
break
if i == 4:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '复核'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '记账'
shape.append(anno)
else:
break
results = recognize(shape, rotate_img, rotate_flag)
elif img_mode == 2:
db_image.img_mode = 2
# results = predict.predict_mode2(img, img_name, rotate_model, seal_model_mode2, detect_model_mode2,
# handword_model, handnum_model, word_model, num_model, char_model, seal_model)
rotate_flag = rotate_det(img, rotate_model)
if rotate_flag == 0:
rotate_img = img.rotate(180, PIL_Image.BILINEAR)
else:
rotate_img = img
rotate_img = rotate_img.convert("RGB")
w, h = rotate_img.size
ratio_w = 512 / w
ratio_h = 512 / h
img_tmp = rotate_img.resize((512, 512))
seal_model_mode2 = EAST(pretrained=False).to(device)
seal_model_mode2.load_state_dict(torch.load(stamp_model_two, map_location=torch.device('cpu')))
seal_model_mode2.eval()
shape, seal_boxes = seal_detect(img_tmp, seal_model_mode2, ratio_w, ratio_h)
detect_model_mode2 = EAST(pretrained=False).to(device)
detect_model_mode2.load_state_dict(torch.load(detect_model_two, map_location=torch.device('cpu')))
detect_model_mode2.eval()
boxes = detect.detect(img_tmp, detect_model_mode2, device)
boxes = detect.adjust_ratio(boxes, ratio_w, ratio_h)
plot_img = detect.plot_boxes(rotate_img, boxes)
plot_img = detect.plot_boxes(plot_img, seal_boxes)
plot_img.save(os.path.join(app.config['UPLOAD_PATH'], img_name[:-4] + "_result" + ".jpg"))
print('detection result saved')
orig_vertices = []
theta = 0
for box in boxes:
box = np.array(box[:8])
orig_vertices.append(box)
theta += crop_mode.find_min_rect_angle(box)
orig_vertices = np.array(orig_vertices)
theta /= len(boxes)
tmp_img, vertices = crop_mode.rotate_allimg(rotate_img, orig_vertices, - theta / math.pi * 180)
dict_centers = {}
for i, vertice in enumerate(vertices):
avg_x = int(crop_mode.averagenum(vertice[::2]))
avg_y = int(crop_mode.averagenum(vertice[1::2]))
dict_centers[str(avg_x) + ',' + str(avg_y)] = i
centers = crop_mode.sort_centers(dict_centers, 1)
k = 0
xcenters = []
index = [3, 2, 2, 1, 4]
for i, j in enumerate(index):
xcenter = crop_mode.sort_xcenters(centers[k:k + j], 0)
k += j
xcenters.append(xcenter)
for i, xcenter in enumerate(xcenters):
if i == 0:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（年）'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（月）'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（日）'
shape.append(anno)
else:
break
if i == 1:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '人民币（大写）'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handnum'
anno['name'] = '人民币（小写）'
shape.append(anno)
else:
break
if i == 2:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'char'
anno['name'] = '使用现金'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '行号'
shape.append(anno)
else:
break
if i == 3:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '密押'
shape.append(anno)
else:
break
if i == 4:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '备注'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '经办'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '复核'
shape.append(anno)
elif j == 3:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '出纳'
shape.append(anno)
else:
break
results = recognize(shape, rotate_img, rotate_flag)
elif img_mode == 3:
db_image.img_mode = 3
# results = predict.predict_mode3(img, img_name, rotate_model, seal_model_mode3, detect_model_mode3,
# handword_model, handnum_model, word_model, num_model, char_model, seal_model)
rotate_flag = rotate_det(img, rotate_model)
if rotate_flag == 0:
rotate_img = img.rotate(180, PIL_Image.BILINEAR)
else:
rotate_img = img
rotate_img = rotate_img.convert("RGB")
w, h = rotate_img.size
ratio_w = 512 / w
ratio_h = 512 / h
img_tmp = rotate_img.resize((512, 512))
seal_model_mode3 = EAST(pretrained=False).to(device)
seal_model_mode3.load_state_dict(torch.load(stamp_model_three, map_location=torch.device('cpu')))
seal_model_mode3.eval()
shape, seal_boxes = seal_detect(img_tmp, seal_model_mode3, ratio_w, ratio_h)
detect_model_mode3 = EAST(pretrained=False).to(device)
detect_model_mode3.load_state_dict(torch.load(detect_model_three, map_location=torch.device('cpu')))
detect_model_mode3.eval()
boxes = detect.detect(img_tmp, detect_model_mode3, device)
boxes = detect.adjust_ratio(boxes, ratio_w, ratio_h)
plot_img = detect.plot_boxes(rotate_img, boxes)
plot_img = detect.plot_boxes(plot_img, seal_boxes)
plot_img.save(os.path.join(app.config['UPLOAD_PATH'], img_name[:-4] + "_result" + ".jpg"))
print('detection result saved')
orig_vertices = []
theta = 0
for box in boxes:
box = np.array(box[:8])
orig_vertices.append(box)
theta += crop_mode.find_min_rect_angle(box)
orig_vertices = np.array(orig_vertices)
theta /= len(boxes)
tmp_img, vertices = crop_mode.rotate_allimg(rotate_img, orig_vertices, - theta / math.pi * 180)
dict_centers = {}
for i, vertice in enumerate(vertices):
avg_x = int(crop_mode.averagenum(vertice[::2]))
avg_y = int(crop_mode.averagenum(vertice[1::2]))
dict_centers[str(avg_x) + ',' + str(avg_y)] = i
centers = crop_mode.sort_centers(dict_centers, 1)
k = 0
xcenters = []
index = [3, 3, 1, 2, 6, 3]
for i, j in enumerate(index):
xcenter = crop_mode.sort_xcenters(centers[k:k + j], 0)
if i == 4:
if int(xcenter[0][0].split(',')[1]) > int(xcenter[1][0].split(',')[1]):
tmp = xcenter[0]
xcenter[0] = xcenter[1]
xcenter[1] = tmp
if int(xcenter[2][0].split(',')[1]) > int(xcenter[3][0].split(',')[1]):
tmp = xcenter[2]
xcenter[2] = xcenter[3]
xcenter[3] = tmp
if int(xcenter[3][0].split(',')[1]) > int(xcenter[4][0].split(',')[1]):
tmp = xcenter[3]
xcenter[3] = xcenter[4]
xcenter[4] = tmp
k += j
xcenters.append(xcenter)
for i, xcenter in enumerate(xcenters):
if i == 0:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（年）'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（月）'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票日期（日）'
shape.append(anno)
else:
break
if i == 1:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '收款人'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '代理付款行'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '代理付款行号'
shape.append(anno)
else:
break
if i == 2:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '出票金额'
shape.append(anno)
else:
break
if i == 3:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handword'
anno['name'] = '实际结算金额（大写）'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handnum'
anno['name'] = '实际结算金额（小写）'
shape.append(anno)
else:
break
if i == 4:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '账号'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '密押'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '申请人'
shape.append(anno)
elif j == 3:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '出票行'
shape.append(anno)
elif j == 4:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'word'
anno['name'] = '备注'
shape.append(anno)
elif j == 5:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '出票行号'
shape.append(anno)
else:
break
if i == 5:
for j, center in enumerate(xcenter):
if j == 0:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'handnum'
anno['name'] = '多余金额'
shape.append(anno)
elif j == 1:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '复核'
shape.append(anno)
elif j == 2:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'num'
anno['name'] = '记账'
shape.append(anno)
else:
break
results = recognize(shape, rotate_img, rotate_flag)
else:
print('the image mode is wrong !')
return
with open(os.path.join(app.config['UPLOAD_PATH'], img_name[:-4] + '.txt'), 'w') as writer:
for result in results:
line = result[0] + ";" + result[1] + '\n'
writer.write(line)
db_image.result_flag = 'Done!!!'
db.session.commit()
print("Task is done!")
def recognize(shape, rotate_img, rotate_flag):
results = []
'''
models = {'handword': handword_model, 'handnum': handnum_model, 'num': num_model, 'word': word_model, 'char': char_model, 'seal': seal_model}
for i, item in enumerate(shape):
print('*' * 30)
mode = item['class']
print('mode：', mode)
box = item['box']
theta = crop_mode.find_min_rect_angle(box)
tmp_img, vertice = crop_mode.rotate_img(rotate_img, box, - theta / math.pi * 180)
x_min, x_max, y_min, y_max = crop_mode.get_boundary(vertice)
crop_img = tmp_img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
if rotate_flag == 0:
width = rotate_img.width
height = rotate_img.height
center_x = (width - 1) / 2
center_y = (height - 1) / 2
new_vertice = np.zeros(box.shape)
new_vertice[:] = rotate_vertices(box, - math.pi, np.array([[center_x], [center_y]]))
box = new_vertice
str_box = []
for site in box:
str_box.append(str(int(site)))
print('box：', str_box)
alphabet = alphabetdict[mode]
converter = convert.strLabelConverter(alphabet)
now_model = models[mode]
'''
for mode in ['handword', 'handnum', 'num', 'word', 'char', 'seal']:
alphabet = alphabetdict[mode]
n_class = len(alphabet) + 1
converter = convert.strLabelConverter(alphabet)
now_model = recog_model.CRNN(class_num=n_class, backbone='resnet', pretrain=False)
state_dict = torch.load(os.path.join('/home/flask_web/static/pths', modeldict[mode]),
map_location=torch.device('cpu'))
now_model.load_state_dict(state_dict=state_dict)
now_model.to(device)
now_model.eval()
for i, item in enumerate(shape):
if item['class'] == mode:
print('*' * 30)
print('mode：', mode)
box = item['box']
theta = crop_mode.find_min_rect_angle(box)
tmp_img, vertice = crop_mode.rotate_img(rotate_img, box, - theta / math.pi * 180)
x_min, x_max, y_min, y_max = crop_mode.get_boundary(vertice)
crop_img = tmp_img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
if rotate_flag == 0:
width = rotate_img.width
height = rotate_img.height
center_x = (width - 1) / 2
center_y = (height - 1) / 2
new_vertice = np.zeros(box.shape)
new_vertice[:] = rotate_vertices(box, - math.pi, np.array([[center_x], [center_y]]))
box = new_vertice
str_box = []
for site in box:
str_box.append(str(int(site)))
print('box：', str_box)
result = text_recognize(crop_img, now_model, converter, mode)
print('name', item['name'])
print('result', result)
results.append([item['name'], result])
return results
