''' 基于形状和色调的检测车牌号并提取车牌号图片 ''' import cv2 import numpy as np from numpy.linalg import norm import sys import os import json SZ = 20 # 训练图片长宽 MAX_WIDTH = 1000 # 原始图片最大宽度 Min_Area = 2000 # 车牌区域允许最大面积 PROVINCE_START = 1000 def point_limit(point): if point[0] < 0: point[0] = 0 if point[1] < 0: point[1] = 0 def accurate_place(card_img_hsv, limit1, limit2, color,cfg): row_num, col_num = card_img_hsv.shape[:2] xl = col_num xr = 0 yh = 0 yl = row_num #col_num_limit = cfg["col_num_limit"] row_num_limit = cfg["row_num_limit"] col_num_limit = col_num * 0.8 if color != "green" else col_num * 0.5 # 绿色有渐变 for i in range(row_num): count = 0 for j in range(col_num): H = card_img_hsv.item(i, j, 0) S = card_img_hsv.item(i, j, 1) V = card_img_hsv.item(i, j, 2) if limit1 < H <= limit2 and 34 < S and 46 < V: count += 1 if count > col_num_limit: if yl > i: yl = i if yh < i: yh = i for j in range(col_num): count = 0 for i in range(row_num): H = card_img_hsv.item(i, j, 0) S = card_img_hsv.item(i, j, 1) V = card_img_hsv.item(i, j, 2) if limit1 < H <= limit2 and 34 < S and 46 < V: count += 1 if count > row_num - row_num_limit: if xl > j: xl = j if xr < j: xr = j return xl, xr, yh, yl def CaridDetect(car_pic): # 加载图片 img = cv2.imread(car_pic) pic_hight, pic_width = img.shape[:2] if pic_width > MAX_WIDTH: resize_rate = MAX_WIDTH / pic_width img = cv2.resize(img, (MAX_WIDTH, int(pic_hight*resize_rate)), interpolation=cv2.INTER_AREA) # 车牌识别的部分参数保存在js中，便于根据图片分辨率做调整 f = open('config.js') j = json.load(f) for c in j["config"]: if c["open"]: cfg = c.copy() break else: raise RuntimeError('[ ERROR ] 没有设置有效配置参数.') blur = cfg["blur"] # 高斯去噪 if blur > 0: img = cv2.GaussianBlur(img, (blur, blur), 0) #图片分辨率调整 oldimg = img img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #equ = cv2.equalizeHist(img) #img = np.hstack((img, equ)) # 去掉图像中不会是车牌的区域 kernel = np.ones((20, 20), np.uint8) # morphologyEx 形态学变化函数 img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0); # 找到图像边缘 Canny边缘检测 ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) img_edge = cv2.Canny(img_thresh, 100, 200) # 使用开运算和闭运算让图像边缘成为一个整体 kernel = np.ones((cfg["morphologyr"], cfg["morphologyc"]), np.uint8) img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel) img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel) # 查找图像边缘整体形成的矩形区域，可能有很多，车牌就在其中一个矩形区域中 # cv2.findContours()函数来查找检测物体的轮廓 try: contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) except ValueError: image, contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area] # print('[ INFO ] len(contours): {}'.format(len(contours))) # 一一排除不是车牌的矩形区域，找到最小外接矩形的长宽比复合车牌条件的边缘检测到的物体 car_contours = [] for cnt in contours: rect = cv2.minAreaRect(cnt) # 生成最小外接矩形，点集 cnt 存放的就是该四边形的4个顶点坐标（点集里面有4个点） # 函数 cv2.minAreaRect() 返回一个Box2D结构rect：（最小外接矩形的中心（x，y），（宽度，高度），旋转角度）， # 但是要绘制这个矩形，我们需要矩形的4个顶点坐标box, 通过函数 cv2.boxPoints() 获得， # 返回形式[ [x0,y0], [x1,y1], [x2,y2], [x3,y3] ]。 # 得到的最小外接矩形的4个顶点顺序、中心坐标、宽度、高度、旋转角度（是度数形式，不是弧度数） # https://blog.csdn.net/lanyuelvyun/article/details/76614872 area_width, area_height = rect[1] if area_width < area_height: area_width, area_height = area_height, area_width wh_ratio = area_width / area_height #print(wh_ratio) # 要求矩形区域长宽比在2到5.5之间，2到5.5是车牌的长宽比，其余的矩形排除 一般的比例是3.5 if wh_ratio > 2 and wh_ratio < 5.5: car_contours.append(rect) box = cv2.boxPoints(rect) box = np.int0(box) #oldimg = cv2.drawContours(oldimg, [box], 0, (0, 0, 255), 2) #cv2.imshow("edge4", oldimg) #print(rect) # print("[ INFo ] len(car_contours): {}".format(len(car_contours))) # print("[ INFO ] 精确定位.") card_imgs = [] # 矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位 # 这个就是为什么我们不选择YOLO,SSD或其他的目标检测算法来检测车牌号的原因！！！（给自己偷懒找个台阶 :) ) for rect in car_contours: if rect[2] > -1 and rect[2] < 1:#创造角度，使得左、高、右、低拿到正确的值 angle = 1 else: angle = rect[2] rect = (rect[0], (rect[1][0]+5, rect[1][1]+5), angle)#扩大rect范围，避免车牌边缘被排除 box = cv2.boxPoints(rect) # 避免边界超出图像边界 heigth_point = right_point = [0, 0] left_point = low_point = [pic_width, pic_hight] for point in box: if left_point[0] > point[0]: left_point = point if low_point[1] > point[1]: low_point = point if heigth_point[1] < point[1]: heigth_point = point if right_point[0] < point[0]: right_point = point if left_point[1] <= right_point[1]: # 正角度 new_right_point = [right_point[0], heigth_point[1]] pts2 = np.float32([left_point, heigth_point, new_right_point])#字符只是高度需要改变 pts1 = np.float32([left_point, heigth_point, right_point]) M = cv2.getAffineTransform(pts1, pts2) # 仿射变换 dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight)) point_limit(new_right_point) point_limit(heigth_point) point_limit(left_point) card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])] card_imgs.append(card_img) #cv2.imshow("card", card_img) #cv2.waitKey(0) elif left_point[1] > right_point[1]: # 负角度 new_left_point = [left_point[0], heigth_point[1]] pts2 = np.float32([new_left_point, heigth_point, right_point])#字符只是高度需要改变 pts1 = np.float32([left_point, heigth_point, right_point]) M = cv2.getAffineTransform(pts1, pts2) dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight)) point_limit(right_point) point_limit(heigth_point) point_limit(new_left_point) card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])] card_imgs.append(card_img) #cv2.imshow("card", card_img) #cv2.waitKey(0) # 开始使用颜色定位，排除不是车牌的矩形，目前只识别蓝、绿、黄车牌 colors = [] for card_index,card_img in enumerate(card_imgs): green = yello = blue = black = white = 0 card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) #有转换失败的可能，原因来自于上面矫正矩形出错 if card_img_hsv is None: continue row_num, col_num= card_img_hsv.shape[:2] card_img_count = row_num * col_num for i in range(row_num): for j in range(col_num): H = card_img_hsv.item(i, j, 0) S = card_img_hsv.item(i, j, 1) V = card_img_hsv.item(i, j, 2) if 11 < H <= 34 and S > 34:#图片分辨率调整 yello += 1 elif 35 < H <= 99 and S > 34:#图片分辨率调整 green += 1 elif 99 < H <= 124 and S > 34:#图片分辨率调整 blue += 1 if 0 < H <180 and 0 < S < 255 and 0 < V < 46: black += 1 elif 0 < H <180 and 0 < S < 43 and 221 < V < 225: white += 1 color = "no" limit1 = limit2 = 0 if yello*2 >= card_img_count: color = "yello" limit1 = 11 limit2 = 34#有的图片有色偏偏绿 elif green*2 >= card_img_count: color = "green" limit1 = 35 limit2 = 99 elif blue*2 >= card_img_count: color = "blue" limit1 = 100 limit2 = 124#有的图片有色偏偏紫 elif black + white >= card_img_count*0.7: #TODO color = "bw" # print("[ INFO ] color: {}".format(color)) colors.append(color) # print(blue, green, yello, black, white, card_img_count) #cv2.imshow("color", card_img) #cv2.waitKey(0) if limit1 == 0: continue #以上为确定车牌颜色 #以下为根据车牌颜色再定位，缩小边缘非车牌边界 xl, xr, yh, yl = accurate_place(card_img_hsv, limit1, limit2, color,cfg) if yl == yh and xl == xr: continue need_accurate = False if yl >= yh: yl = 0 yh = row_num need_accurate = True if xl >= xr: xl = 0 xr = col_num need_accurate = True card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh-yl)//4 else card_img[yl-(yh-yl)//4:yh, xl:xr] if need_accurate:#可能x或y方向未缩小，需要再试一次 card_img = card_imgs[card_index] card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) xl, xr, yh, yl = accurate_place(card_img_hsv, limit1, limit2, color,cfg) if yl == yh and xl == xr: continue if yl >= yh: yl = 0 yh = row_num if xl >= xr: xl = 0 xr = col_num card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh-yl)//4 else card_img[yl-(yh-yl)//4:yh, xl:xr] roi = card_img card_color = color labels = (int(right_point[1]), int(heigth_point[1]), int(left_point[0]), int(right_point[0])) return roi,labels, card_color#定位的车牌图像、车牌颜色 if __name__ == '__main__': for pic_file in os.listdir("./test_img"): roi, label,color = CaridDetect(os.path.join("./test_img",pic_file)) cv2.imwrite(os.path.join("./result",pic_file),roi) print("*"*50) print("[ ROI ] {}".format(roi)) print("[ Color ] {}".format(color)) print("[ Label ] {}".format(label))