ocr文字识别图片中的汉字，python编写

import numpy as np import cv2 import paddlehub as hub ocr = hub.Module(name="chinese_ocr_db_crnn_mobile") def char_recognition(pattern,*args): result_list = [] if pattern==3: for img in args: np_images = [img] results = ocr.recognize_text( images=np_images, use_gpu=False, output_dir='ocr_result', visualization=True, box_thresh=0.5, text_thresh=0.5) for result in results: data = result['data'] for index,infomation in enumerate(data): print('text', ':',infomation['text']) result_list.append(infomation['text']) return result_list def preprocessing(img): img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 对图片的通道进行转换为RGB img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) # 灰度图像处理 img_Gauassian = cv2.GaussianBlur(img_gray, (5, 5), 0) ret, Binary = cv2.threshold(img_Gauassian, 60, 255, cv2.THRESH_BINARY) # 二值化处理 周围像素影响 return Binary def find_ppt(img): edges = cv2.Canny(img, 100, 200) #边缘检测 _,contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 寻找轮廓，输出为三个参数 max_index = 0 #有很多个轮廓，面积最大的那个轮廓就是ppt for i in range(len(contours)): cnt = contours[i] area = cv2.contourArea(cnt) max_area = cv2.contourArea(contours[max_index]) if (area > max_area): max_index = i cv2.drawContours(img_bgr, [contours[max_index]], -1, (0, 0, 255), 1) #cv2.imshow('img_bgr2', img_bgr) #展示轮廓 return contours, max_index def find_ppt_zuobiao(img_bgr, contours, max_index): hull = cv2.convexHull(contours[max_index]) #凸包 #按道理来说，长方形凸包有4个点。但我们上面求出的凸包，因为不是正规长方形，所以有很多乱七八糟的点。我们从这些点中，选出长方形的四个顶点 #print(hull) points = [] #minRect = cv2.minAreaRect(points) #cv2.fourPoint2f[4] #points=minRect.points(cv2.fourPoint2f) #选出左上的点 min_x = 999 min_y = 999 for point in list(hull): if point[0][0] <= min_x+18 and point[0][1] <= min_y-5: min_x = point[0][0] min_y = point[0][1] points.append((min_x, min_y)) # 选出右上的点 max_x = 0 min_y = 999999 for point in list(hull): if point[0][0] >= max_x-5 and point[0][1] <= min_y - 5: max_x = point[0][0] min_y = point[0][1] points.append((max_x, min_y)) # 选出左下的点 min_x = 999999 max_y = 0 for point in list(hull): if point[0][0] <= min_x - 5 and point[0][1] >= max_y - 10: min_x = point[0][0] max_y = point[0][1] points.append((min_x, max_y)) # 选出右下的点 max_x = 0 max_y = 0 for point in list(hull): if point[0][0] >= max_x - 30 and point[0][1] >= max_y - 10: max_x = point[0][0] max_y = point[0][1] points.append((max_x, max_y)) #这样，points这个列表里就有了ppt的4个顶点坐标 cv2.drawContours(img_bgr, [contours[max_index]], -1, (0, 0, 255), 1) for i in range(4): cv2.circle(img_bgr, points[i], 3, (255, 0, 0), -1) #cv2.imshow('img_bgr', img_bgr) #看看坐标 return points def TouYingBianHuan(img_bgr, points): #坐标变换 dst = np.array([[0, 0], [w - 1, 0], [0, h - 1], [w - 1, h - 1]], np.float32) src = np.array(points, np.float32) P = cv2.getPerspectiveTransform(src, dst) # 计算投影矩阵 result = cv2.warpPerspective(img_bgr, P, (w, h), borderValue=125) res = cv2.resize(result, (1000, 700), interpolation=cv2.INTER_CUBIC) result = char_recognition(3,res) #print(result) cv2.imshow('res', res) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == '__main__': #一定要记住！！！opencv的原点在左上角，和我们正常的坐标是上下颠倒的！！！！！！！！！！！！ img_bgr = cv2.imread('2.jpg') # opencv默认的imread是以BGR的方式进行存储的 #img_bgr = cv2.resize(img_bgr, (1000, 1000), interpolation=cv2.INTER_CUBIC) #cv2.imshow('img', img_bgr) h, w = img_bgr.shape[:2] #图片的高和宽，注意，原点（0，0）在图片的左上角 img = preprocessing(img_bgr) #预处理，把图片二值化，变为只有黑白 contours, max_index = find_ppt(img) #把ppt那一块找出来 points = find_ppt_zuobiao(img_bgr, contours, max_index) #ppt是长方形，找出他4个坐标 #print(points) TouYingBianHuan(img_bgr, points) #投影变换，原本是斜的，现在把他变正