# @Author : lightXu # @File : pre_segment.py import time import numpy as np import cv2 from numpy import asarray import base64 import scipy.signal from segment.image_operation import utils def hough_rotate_cv(image): """ not Long time consuming, not Strong generalization ability, not high accuracy, more super parameters""" img_np = utils.resize_by_percent(asarray(image), 1) if len(img_np.shape) == 3: img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) canny_image = cv2.Canny(img_np, 0, 255, apertureSize=3) # cv2.imshow('canny', canny_image) # cv2.waitKey(10) lines = cv2.HoughLinesP(canny_image, 1, np.pi / 180, 160, minLineLength=500, maxLineGap=65) # lines = cv2.HoughLines(canny_image, 1, np.pi / 180, 160, max_theta=30, min_theta=0) # 寻找长度最长的线 distance = [] for line in lines: x1, y1, x2, y2 = line[0] dis = np.sqrt(pow((x2 - x1), 2) + pow((y2 - y1), 2)) distance.append(dis) max_dis_index = distance.index(max(distance)) max_line = lines[max_dis_index] x1, y1, x2, y2 = max_line[0] # 获取旋转角度 angle = cv2.fastAtan2((y2 - y1), (x2 - x1)) print(angle) if 0.5 <= angle <= 7: # 因为识别误差问题，根据实际情况设置旋转阈值 centerpoint = (image.shape[1] / 2, image.shape[0] / 2) rotate_mat = cv2.getRotationMatrix2D(centerpoint, angle, 1.0) # 获取旋转矩阵 correct_image = cv2.warpAffine(image, rotate_mat, (image.shape[1], image.shape[0]), borderValue=(255, 255, 255)) # cv2.imshow('test', resize_by_percent(correct_image, 0.1)) # cv2.waitKey(10) return correct_image else: return image def array_latter_subtracts_precious(nparray): array1 = nparray[:-1] array2 = nparray[1:] return array2 - array1 def split_by_index(im_raw, index): y_raw, x_raw, _ = im_raw.shape img_left = im_raw[1:y_raw, 1:index] img_right = im_raw[1:y_raw, index + 1:x_raw] return img_left, img_right def split_img_at_middle_by_y_axis(img_path, radio=0.10, thresh_std=5000): im_raw = utils.read_img(img_path) im_resize = utils.resize_by_percent(im_raw, radio) ry, rx, _ = im_resize.shape img_mtx0 = np.asarray(utils.rgb2binary(im_resize)) y_sum_array0 = img_mtx0.sum(axis=0) tmp = array_latter_subtracts_precious(y_sum_array0 / ry) std0 = np.std(tmp) # 计算标准差 # # plt.bar(range(len(y_sum_array0)), y_sum_array0) # # plt.show() # plt.plot(range(len(y_sum_array0)-1), tmp) # plt.show() y, x, _z = im_resize.shape x_bias = int(x * 0.15) y_bias = int(y * 0.30) middle_x = int(x / 2) middle_area_img = im_resize[y_bias:y, middle_x - x_bias:middle_x + x_bias] img_mtx = np.asarray(utils.rgb2binary(middle_area_img)) y_sum_array = img_mtx.sum(axis=0) std = np.std(y_sum_array) # 计算标准差 y_sum_list = list(y_sum_array) if std <= thresh_std: index = y_sum_list.index(max(y_sum_list)) else: index = y_sum_list.index(min(y_sum_list)) split_index = middle_x + index - int(len(y_sum_list) / 2) split_index = int(split_index / radio) y_raw, x_raw, _ = im_raw.shape img_left = im_raw[1:y_raw, 1:split_index] img_right = im_raw[1:y_raw, split_index + 1:x_raw] left_path = img_path.replace('.jpg', '_left.jpg') right_path = img_path.replace('.jpg', '_right.jpg') cv2.imencode('.jpg', img_left)[1].tofile(left_path) cv2.imencode('.jpg', img_right)[1].tofile(right_path) print(left_path) print(right_path) def smart_split_img_at_middle_by_x_axis(img_path, resize_radio=0.1): im_raw = utils.read_img(img_path) im_resize = utils.resize_by_percent(im_raw, resize_radio) bin_img = utils.rgb2binary(im_resize) ry, rx = bin_img.shape img_mtx0 = np.asarray(bin_img) y_sum_array0 = img_mtx0.sum(axis=0) # y轴求和 subtracts_arr = np.abs(array_latter_subtracts_precious(y_sum_array0 / ry)) # 长度减1 subtracts_arr_index = np.argsort(subtracts_arr, kind='quicksort', order=None) subtracts_arr_index = subtracts_arr_index[-10:] index_middle_distance_list = list(np.abs(subtracts_arr_index - int(rx / 2))) split_index = subtracts_arr_index[index_middle_distance_list.index(min(index_middle_distance_list))] + 1 split_index = int(split_index / resize_radio) img_left, img_right = split_by_index(im_raw, split_index) left_path = img_path.replace('.jpg', '_left.jpg') right_path = img_path.replace('.jpg', '_right.jpg') cv2.imencode('.jpg', img_left)[1].tofile(left_path) cv2.imencode('.jpg', img_right)[1].tofile(right_path) print(left_path) print(right_path) def segment2parts_by_pix(crop_img): p_image = utils.preprocess(crop_img) height, width = p_image.shape sum_x_axis = p_image.sum(axis=0) / (height*255) # sum_x_axis = (sum_x_axis / (255*height)).astype(float) kernel = np.array([-2, 0, 2]) sobel_filter = scipy.signal.convolve(sum_x_axis, kernel) # 一维卷积运算 temp = np.abs(sobel_filter[1:-1])/np.max(np.abs(sobel_filter[1:-1])) temp[temp < 0.6] = 0 temp[temp != 0] = 1 index = np.where(temp == 1)[0] width1 = width // 9 intervals = [(0, width1), (4 * width1, 5 * width1), (8 * width1, width)] # 左开右闭 index_list = [] for i, interval in enumerate(intervals): index_sec_list = [] for ele in index: if interval[0] < ele <= interval[1]: index_sec_list.append(ele) index_list.append(index_sec_list) left_x_point, middle_x_point, right_x_point = 9999, 9999, 9999 left_del_part = (0, left_x_point) middle_part = (left_x_point, middle_x_point) right_part = (middle_x_point, right_x_point) right_del_part = (right_x_point, width) # left if index_list[0]: left_x_point = index_list[0][-1] left_del_part = (0, left_x_point) # middle if index_list[1]: value_list = [abs(sobel_filter[index]) for index in index_list[1]] middle_x_point = index_list[1][value_list.index(max(value_list))] middle_part = (left_x_point, middle_x_point) # right if index_list[2]: right_x_point = index_list[2][0] right_part = (middle_x_point, right_x_point) right_del_part = (right_x_point, width) split_point = sorted(list(set(sorted(list(left_del_part + middle_part + right_part + right_del_part))) - {9999})) split_pairs = [] if len(split_point) > 2: a = split_point[:-1] b = split_point[1:] for i, ele in enumerate(a): if b[i] - ele > width1: split_pairs.append((ele, b[i])) return split_pairs def segment2parts(im_raw, save_path): img_parts_dict_list = [] # randon_img = radon_rotate_ski(im_raw) # 试卷顶部可能有黑边，切去3% yy, xx = im_raw.shape[0], im_raw.shape[1] y_crop_pix = int(yy*0.03) # x_crop_pix = int(xx*0.03) x_crop_pix = 0 im_crop = im_raw[y_crop_pix:yy-y_crop_pix, x_crop_pix:xx-x_crop_pix] split_pairs = segment2parts_by_pix(im_crop) if len(split_pairs) >= 2: for index, ele in enumerate(split_pairs): dst = im_raw[:, ele[0]:ele[1]] save_path_final = save_path.replace('.jpg', '') + '_{}_{}_{}.jpg'.format(ele[0], 0, index) cv2.imencode('.jpg', dst)[1].tofile(save_path_final) image = cv2.imencode('.jpg', dst)[1] base64_data = str(base64.b64encode(image))[2:-1] part_dict = {'img_part': base64_data, 'x_bias': ele[0] + x_crop_pix, 'y_bias': 0} img_parts_dict_list.append(part_dict) else: img = im_crop[:, split_pairs[0][0]:split_pairs[0][1]] resize_ratio = 0.3 im_resize = utils.resize_by_percent(img, resize_ratio) # gray if len(im_resize.shape) >= 3: gray_img = cv2.cvtColor(im_resize, cv2.COLOR_BGR2GRAY) else: gray_img = im_resize ry, rx = gray_img.shape # 高斯 glur_img = cv2.GaussianBlur(gray_img, (5, 5), 0) # otsu _ret, threshed_img = cv2.threshold(glur_img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) if ry < rx: x_kernel = int(10*resize_ratio) else: x_kernel = int(10 * resize_ratio) kernel = np.ones((glur_img.shape[0], x_kernel), np.uint8) # height, width dilation = cv2.dilate(threshed_img, kernel, iterations=1) # cv2.imshow(' ', dilation) # if cv2.waitKey(0) == 27: # cv2.destroyAllWindows() # _, cnts, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) (major, minor, _) = cv2.__version__.split(".") contours = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = contours[0] if int(major) > 3 else contours[1] box_list = [cv2.boundingRect(cnt) for cnt in cnts] box_array = np.asarray(box_list) box_array[:, 2] = box_array[:, 0] + box_array[:, 2] box_array[:, 3] = box_array[:, 1] + box_array[:, 3] middle_x = rx // 2 left_box = np.asarray([0, 0, 0, 0]) right_box = np.asarray([0, 0, 0, 0]) for box in box_array: x, y, xmax, ymax = box if x + (xmax-x)//2 <= middle_x: left_box = np.vstack([left_box, box]) else: right_box = np.vstack([right_box, box]) left_box_list = [] right_box_list = [] try: left_box_list = left_box[1:, :][:, :2].min(axis=0).tolist() + left_box[1:, :][:, 2:].max(axis=0).tolist() except Exception: pass # 单面的情况 try: right_box_list = right_box[1:, :][:, :2].min(axis=0).tolist() + right_box[1:, :][:, 2:].max(axis=0).tolist() except Exception: pass box_list = [left_box_list, right_box_list] bias = int(70 * resize_ratio) for index, box in enumerate(box_list): if len(box) > 0: xmin, ymin, xmax, ymax = box if xmin - bias > 0: xmin = xmin - bias else: xmin = 0 dst = im_crop[int(ymin / resize_ratio):int(ymax / resize_ratio), int(xmin / resize_ratio):int(xmax / resize_ratio)] save_path_final = save_path.replace('.jpg', '') + '_{}_{}_{}.jpg'.format(xmin, ymin, index) cv2.imencode('.jpg', dst)[1].tofile(save_path_final) image = cv2.imencode('.jpg', dst)[1] base64_data = str(base64.b64encode(image))[2:-1] part_dict = {'img_part': base64_data, 'x_bias': int(xmin/resize_ratio) + x_crop_pix + split_pairs[0][0], 'y_bias': int(ymin/resize_ratio) + y_crop_pix + 0} if (xmax - xmin)/resize_ratio > 100: # 去掉竖长条 img_parts_dict_list.append(part_dict) return img_parts_dict_list