python opencv自动识别电表读数（识别LED数字）附源代码

# from aip import AipOcr import cv2 import numpy as np from num_recon import templating def img_devide(): kernel = np.ones((5, 5), np.uint8) global num global boxes num = 0 boxes = [] for i in range(1): path = input('Please input the filename:') print(path) # 缩放图像 img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) res = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC) cv2.imwrite('res.png', res) equ = cv2.equalizeHist(res) cv2.imwrite('equ.png', equ) _, thres = cv2.threshold( equ, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) cv2.imwrite('thres.png', thres) height, weight = thres.shape roi = thres[0:int(height*0.5), :] cv2.imwrite('roi.png', roi) opening = cv2.morphologyEx(roi, cv2.MORPH_OPEN, kernel) closing = cv2.morphologyEx( opening, cv2.MORPH_CLOSE, kernel, iterations=1) img_postion, contours, _ = cv2.findContours( opening, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours.sort(key=len, reverse=True) for i in range(len(contours)): max_contours = contours[i] epsilon = 0.015 * cv2.arcLength(max_contours, True) approx = cv2.approxPolyDP(max_contours, epsilon, True) brims = len(approx) perimeter = cv2.arcLength(max_contours, True) area = cv2.contourArea(max_contours) # 挑选边框 选择目标区域 if (brims >= 4 and (area/perimeter) > 50): # print(brims, area/perimeter) rect = cv2.minAreaRect(max_contours) box = np.int0(cv2.boxPoints(rect)) boxes.append(box) cv2.drawContours(opening, [box], -1, (128, 132, 125), 5) cv2.imwrite('opening1.png', opening) num = num + 1 if num == 2: num = 0 break # 保存识别的区域 for i in range(2): Xs = [i[0] for i in boxes[i]] Ys = [i[1] for i in boxes[i]] x1 = min(Xs) x2 = max(Xs) y1 = min(Ys) y2 = max(Ys) # print(x1, x2, y1, y2) roi_ = res[y1:y2, x1:x2] cv2.imwrite('roi' + str(i) + '.png', roi_) # 遍历数字图像，读出内容 for i in ['roi0.png', 'roi1.png']: img_0 = cv2.imread(i, cv2.IMREAD_GRAYSCALE) roi_h, roi_w = img_0.shape img_0 = img_0[int(roi_h*0.4):int(roi_h*0.8), int(roi_w*0.1):int(roi_w*0.93)] cv2.imwrite('num_roi.png', img_0) blurred = cv2.GaussianBlur(img_0, (1, 1), 0) _, roi_thres = cv2.threshold( blurred, 20, 255, cv2.THRESH_BINARY) cv2.imwrite('roi_thres.png', roi_thres) kernel_1 = np.ones((3, 3), np.uint8) kernel_2 = np.ones((1, 1), np.uint8) roi_close = cv2.morphologyEx(cv2.GaussianBlur( roi_thres.copy(), (1, 1), 0), cv2.MORPH_CLOSE, kernel_2, iterations=1) cv2.imwrite('roi_close.png', roi_close) kernel_3 = np.ones((2, 2), np.uint8) roi_erosion = cv2.erode(roi_close, kernel_3, iterations=1) cv2.imwrite('roi_erosion.png', roi_erosion) # 数字识别 templating('roi_erosion.png') if __name__ == '__main__': img_devide()