opencv-python实战

系统管理员 2021-10-01 05:30 262阅读 0赞

# （一）信用卡数字识别 #

*  **使用模板匹配进行数字识别**
 *  **将信用卡里取取来的数字与模板里的每一个数字进行匹配，得分高者即是该数字**

#python3 ocr_template_match.py -i images/credit_card_01.png -t images/ocr_a_reference.png
    
    # 导入工具包
    from imutils import contours
    import numpy as np
    import argparse
    import cv2
    import myutils
    
    # 设置参数
    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", required=True,
    	help="path to input image")
    ap.add_argument("-t", "--template", required=True,
    	help="path to template OCR-A image")
    args = vars(ap.parse_args())
    
    # 指定信用卡类型
    FIRST_NUMBER = {
    	"3": "American Express",
    	"4": "Visa",
    	"5": "MasterCard",
    	"6": "Discover Card"
    }
    # 绘图展示
    def cv_show(name,img):
    	cv2.imshow(name, img)
    	cv2.waitKey(0)
    	cv2.destroyAllWindows()
    # 读取一个模板图像
    img = cv2.imread(args["template"])
    cv_show('img',img)
    # 灰度图
    ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cv_show('ref',ref)
    # 二值图像
    ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
    cv_show('ref2',ref)
    
    # 计算轮廓
    #cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图）,cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_SIMPLE只保留终点坐标
    #返回的list中每个元素都是图像中的一个轮廓
    
    _,refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    
    cv2.drawContours(img,refCnts,-1,(0,0,255),3) 
    cv_show('img',img)
    print (np.array(refCnts).shape)
    refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] #排序，从左到右，从上到下
    digits = {}
    
    # 遍历每一个轮廓
    for (i, c) in enumerate(refCnts):
    	# 计算外接矩形并且resize成合适大小
    	(x, y, w, h) = cv2.boundingRect(c)
    	roi = ref[y:y + h, x:x + w]
    	roi = cv2.resize(roi, (57, 88))
    
    	# 每一个数字对应每一个模板
    	digits[i] = roi
    
    # 初始化卷积核
    rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
    sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    
    #读取输入图像，预处理
    image = cv2.imread(args["image"])
    cv_show('image',image)
    image = myutils.resize(image, width=300)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    cv_show('gray',gray)
    
    #礼帽操作，突出更明亮的区域
    tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel) 
    cv_show('tophat',tophat) 
    # 
    gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于用3*3的
    	ksize=-1)
    
    gradX = np.absolute(gradX)
    (minVal, maxVal) = (np.min(gradX), np.max(gradX))
    gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
    gradX = gradX.astype("uint8")
    
    print (np.array(gradX).shape)
    cv_show('gradX',gradX)
    
    #通过闭操作（先膨胀，再腐蚀）将数字连在一起
    gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) 
    cv_show('gradX',gradX)
    #THRESH_OTSU会自动寻找合适的阈值，适合双峰，需把阈值参数设置为0
    thresh = cv2.threshold(gradX, 0, 255,
    	cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 
    cv_show('thresh',thresh)
    
    #再来一个闭操作
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
    cv_show('thresh',thresh)
    
    # 计算轮廓
    _,threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
    	cv2.CHAIN_APPROX_SIMPLE)
    cnts = threshCnts
    cur_img = image.copy()
    cv2.drawContours(cur_img,cnts,-1,(0,0,255),3) 
    cv_show('img2',cur_img)
    locs = []
    
    # 遍历轮廓
    for (i, c) in enumerate(cnts):
    	# 计算矩形
    	(x, y, w, h) = cv2.boundingRect(c)
    	ar = w / float(h)
    
    	# 选择合适的区域，根据实际任务来，这里的基本都是四个数字一组
    	if ar > 2.5 and ar < 4.0:
    
    		if (w > 40 and w < 55) and (h > 10 and h < 20):
    			#符合的留下来
    			locs.append((x, y, w, h))
    
    # 将符合的轮廓从左到右排序
    locs = sorted(locs, key=lambda x:x[0])
    output = []
    
    # 遍历每一个轮廓中的数字
    for (i, (gX, gY, gW, gH)) in enumerate(locs):
    	# initialize the list of group digits
    	groupOutput = []
    
    	# 根据坐标提取每一个组
    	group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]
    	cv_show('group',group)
    	# 预处理
    	group = cv2.threshold(group, 0, 255,
    		cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    	cv_show('group',group)
    	# 计算每一组的轮廓
    	_,digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
    		cv2.CHAIN_APPROX_SIMPLE)
    	digitCnts = contours.sort_contours(digitCnts,
    		method="left-to-right")[0]
    
    	# 计算每一组中的每一个数值
    	for c in digitCnts:
    		# 找到当前数值的轮廓，resize成合适的的大小
    		(x, y, w, h) = cv2.boundingRect(c)
    		roi = group[y:y + h, x:x + w]
    		roi = cv2.resize(roi, (57, 88))
    		cv_show('roi',roi)
    
    		# 计算匹配得分
    		scores = []
    
    		# 在模板中计算每一个得分
    		for (digit, digitROI) in digits.items():
    			# 模板匹配
    			result = cv2.matchTemplate(roi, digitROI,
    				cv2.TM_CCOEFF)
    			(_, score, _, _) = cv2.minMaxLoc(result)
    			scores.append(score)
    		# 得到最合适的数字
    		groupOutput.append(str(np.argmax(scores)))
    		
    	# 画出来
    	cv2.rectangle(image, (gX - 5, gY - 5),
    		(gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)
    	cv2.putText(image, "".join(groupOutput), (gX, gY - 15),
    		cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
    
    	# 得到结果
    	output.extend(groupOutput)
    # 打印结果
    print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
    print("Credit Card #: {}".format("".join(output)))
    cv2.imshow("Image", image)
    cv2.waitKey(0)

## ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1poYW5nWGlhb3l1X3N5_size_16_color_FFFFFF_t_70][] ##

# （二）答题卡识别 #

*  **找到选项的轮廓，从上到下，从左到右依次扫描，根据阈值判断是否选中**  
     
    
        #python3 get_answer.py -i images/example_test.png
        #导入工具包
        import numpy as np
        import argparse
        import imutils
        import cv2
        
        # 设置参数
        ap = argparse.ArgumentParser()
        ap.add_argument("-i", "--image", required=True,
        	help="path to the input image")
        args = vars(ap.parse_args())
        
        # 正确答案
        ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}
        
        def order_points(pts):
        	# 一共4个坐标点
        	rect = np.zeros((4, 2), dtype = "float32")
        
        	# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
        	# 计算左上，右下
        	s = pts.sum(axis = 1)
        	rect[0] = pts[np.argmin(s)]
        	rect[2] = pts[np.argmax(s)]
        
        	# 计算右上和左下
        	diff = np.diff(pts, axis = 1)
        	rect[1] = pts[np.argmin(diff)]
        	rect[3] = pts[np.argmax(diff)]
        
        	return rect
        
        def four_point_transform(image, pts):
        	# 获取输入坐标点
        	rect = order_points(pts)
        	(tl, tr, br, bl) = rect
        
        	# 计算输入的w和h值
        	widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
        	widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
        	maxWidth = max(int(widthA), int(widthB))
        
        	heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
        	heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
        	maxHeight = max(int(heightA), int(heightB))
        
        	# 变换后对应坐标位置
        	dst = np.array([
        		[0, 0],
        		[maxWidth - 1, 0],
        		[maxWidth - 1, maxHeight - 1],
        		[0, maxHeight - 1]], dtype = "float32")
        
        	# 计算变换矩阵
        	M = cv2.getPerspectiveTransform(rect, dst)
        	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
        
        	# 返回变换后结果
        	return warped
        def sort_contours(cnts, method="left-to-right"):
            reverse = False
            i = 0
            if method == "right-to-left" or method == "bottom-to-top":
                reverse = True
            if method == "top-to-bottom" or method == "bottom-to-top":
                i = 1
            boundingBoxes = [cv2.boundingRect(c) for c in cnts]
            (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
                                                key=lambda b: b[1][i], reverse=reverse))
            return cnts, boundingBoxes
        def cv_show(name,img):
                cv2.imshow(name, img)
                cv2.waitKey(0)
                cv2.destroyAllWindows()  
        
        # 预处理
        image = cv2.imread(args["image"])
        contours_img = image.copy()
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
        cv_show('blurred',blurred)
        edged = cv2.Canny(blurred, 75, 200)
        cv_show('edged',edged)
        
        # 轮廓检测
        cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
        	cv2.CHAIN_APPROX_SIMPLE)[1]
        cv2.drawContours(contours_img,cnts,-1,(0,0,255),3) 
        cv_show('contours_img',contours_img)
        docCnt = None
        
        # 确保检测到了
        if len(cnts) > 0:
        	# 根据轮廓大小进行排序
        	cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
        
        	# 遍历每一个轮廓
        	for c in cnts:
        		# 近似
        		peri = cv2.arcLength(c, True)
        		approx = cv2.approxPolyDP(c, 0.02 * peri, True)
        
        		# 准备做透视变换
        		if len(approx) == 4:
        			docCnt = approx
        			break
        
        # 执行透视变换
        
        warped = four_point_transform(gray, docCnt.reshape(4, 2))
        cv_show('warped',warped)
        # Otsu's 阈值处理
        thresh = cv2.threshold(warped, 0, 255,
        	cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] 
        cv_show('thresh',thresh)
        thresh_Contours = thresh.copy()
        # 找到每一个圆圈轮廓
        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
        	cv2.CHAIN_APPROX_SIMPLE)[1]
        cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3) 
        cv_show('thresh_Contours',thresh_Contours)
        questionCnts = []
        
        # 遍历
        for c in cnts:
        	# 计算比例和大小
        	(x, y, w, h) = cv2.boundingRect(c)
        	ar = w / float(h)
        
        	# 根据实际情况指定标准
        	if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:
        		questionCnts.append(c)
        
        # 按照从上到下进行排序
        questionCnts = sort_contours(questionCnts,
        	method="top-to-bottom")[0]
        correct = 0
        
        # 每排有5个选项
        for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):
        	# 排序
        	cnts = sort_contours(questionCnts[i:i + 5])[0]
        	bubbled = None
        
        	# 遍历每一个结果
        	for (j, c) in enumerate(cnts):
        		# 使用mask来判断结果
        		mask = np.zeros(thresh.shape, dtype="uint8")
        		cv2.drawContours(mask, [c], -1, 255, -1) #-1表示填充
        		cv_show('mask',mask)
        		# 通过计算非零点数量来算是否选择这个答案
        		mask = cv2.bitwise_and(thresh, thresh, mask=mask)
        		total = cv2.countNonZero(mask)
        
        		# 通过阈值判断
        		if bubbled is None or total > bubbled[0]:
        			bubbled = (total, j)
        
        	# 对比正确答案
        	color = (0, 0, 255)
        	k = ANSWER_KEY[q]
        
        	# 判断正确
        	if k == bubbled[1]:
        		color = (0, 255, 0)
        		correct += 1
        
        	# 绘图
        	cv2.drawContours(warped, [cnts[k]], -1, color, 3)
        
        
        score = (correct / 5.0) * 100
        print("[INFO] score: {:.2f}%".format(score))
        cv2.putText(warped, "{:.2f}%".format(score), (10, 30),
        	cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
        cv2.imshow("Original", image)
        cv2.imshow("Exam", warped)
        cv2.waitKey(0)
    
    ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1poYW5nWGlhb3l1X3N5_size_16_color_FFFFFF_t_70 1][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1poYW5nWGlhb3l1X3N5_size_16_color_FFFFFF_t_70 2][]

# （三）车道线检测 #

*  **利用掩膜抠出车道线区域，再利用霍夫变换找出直线，再进行左右直线的拟合**
    
        import cv2
        import numpy as np
        from moviepy.editor import VideoFileClip
        
        # 高斯滤波核大小
        blur_ksize = 5
        
        # Canny边缘检测高低阈值
        canny_lth = 50
        canny_hth = 150
        
        # 霍夫变换参数
        rho = 1
        theta = np.pi / 180
        threshold = 15
        min_line_len = 40
        max_line_gap = 20
        
        
        def process_an_image(img):
            # 1. 灰度化、滤波和Canny
            gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
            blur_gray = cv2.GaussianBlur(gray, (blur_ksize, blur_ksize), 1)
            edges = cv2.Canny(blur_gray, canny_lth, canny_hth)
        
            # 2. 标记四个坐标点用于ROI截取
            rows, cols = edges.shape
            points = np.array([[(0, rows), (460, 325), (520, 325), (cols, rows)]])
            # [[[0 540], [460 325], [520 325], [960 540]]]
            roi_edges = roi_mask(edges, points)
        
            # 3. 霍夫直线提取
            drawing, lines = hough_lines(roi_edges, rho, theta,
                                         threshold, min_line_len, max_line_gap)
        
            # 4. 车道拟合计算
            draw_lanes(drawing, lines)
        
            # 5. 最终将结果合在原图上
            result = cv2.addWeighted(img, 0.9, drawing, 0.2, 0)
        
            return result
        
        
        def roi_mask(img, corner_points):
            # 创建掩膜
            mask = np.zeros_like(img)
            cv2.fillPoly(mask, corner_points, 255)
        
            masked_img = cv2.bitwise_and(img, mask)
            return masked_img
        
        
        def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
            # 统计概率霍夫直线变换
            lines = cv2.HoughLinesP(img, rho, theta, threshold,
                                    minLineLength=min_line_len, maxLineGap=max_line_gap)
        
            # 新建一副空白画布
            drawing = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
            # 画出直线检测结果
            # draw_lines(drawing, lines)
        
            return drawing, lines
        
        
        def draw_lines(img, lines, color=[0, 0, 255], thickness=1):
            for line in lines:
                for x1, y1, x2, y2 in line:
                    cv2.line(img, (x1, y1), (x2, y2), color, thickness)
        
        
        def draw_lanes(img, lines, color=[255, 0, 0], thickness=8):
            # a. 划分左右车道
            left_lines, right_lines = [], []
            for line in lines:
                for x1, y1, x2, y2 in line:
                    k = (y2 - y1) / (x2 - x1)
                    if k < 0:
                        left_lines.append(line)
                    else:
                        right_lines.append(line)
        
            if (len(left_lines) <= 0 or len(right_lines) <= 0):
                return
        
            # b. 清理异常数据
            clean_lines(left_lines, 0.1)
            clean_lines(right_lines, 0.1)
        
            # c. 得到左右车道线点的集合，拟合直线
            left_points = [(x1, y1) for line in left_lines for x1, y1, x2, y2 in line]
            left_points = left_points + [(x2, y2)
                                         for line in left_lines for x1, y1, x2, y2 in line]
        
            right_points = [(x1, y1)
                            for line in right_lines for x1, y1, x2, y2 in line]
            right_points = right_points + \
                [(x2, y2) for line in right_lines for x1, y1, x2, y2 in line]
        
            left_results = least_squares_fit(left_points, 325, img.shape[0])
            right_results = least_squares_fit(right_points, 325, img.shape[0])
        
            # 注意这里点的顺序
            vtxs = np.array(
                [[left_results[1], left_results[0], right_results[0], right_results[1]]])
            # d.填充车道区域
            cv2.fillPoly(img, vtxs, (0, 255, 0))
        
            # 或者只画车道线
            # cv2.line(img, left_results[0], left_results[1], (0, 255, 0), thickness)
            # cv2.line(img, right_results[0], right_results[1], (0, 255, 0), thickness)
        
        
        def clean_lines(lines, threshold):
            # 迭代计算斜率均值，排除掉与差值差异较大的数据
            slope = [(y2 - y1) / (x2 - x1)
                     for line in lines for x1, y1, x2, y2 in line]
            while len(lines) > 0:
                mean = np.mean(slope)
                diff = [abs(s - mean) for s in slope]
                idx = np.argmax(diff)
                if diff[idx] > threshold:
                    slope.pop(idx)
                    lines.pop(idx)
                else:
                    break
        
        
        def least_squares_fit(point_list, ymin, ymax):
            # 最小二乘法拟合
            x = [p[0] for p in point_list]
            y = [p[1] for p in point_list]
        
            # polyfit第三个参数为拟合多项式的阶数，所以1代表线性
            fit = np.polyfit(y, x, 1)
            fit_fn = np.poly1d(fit)  # 获取拟合的结果
        
            xmin = int(fit_fn(ymin))
            xmax = int(fit_fn(ymax))
        
            return [(xmin, ymin), (xmax, ymax)]
        
        
        if __name__ == "__main__":
            output = 'test_videos/yellow_lane_mark.mp4'
            clip = VideoFileClip("test_videos/yellow_lane.mp4")
            out_clip = clip.fl_image(process_an_image)
            out_clip.write_videofile(output, audio=False)
    
    ![20190817100512347.gif][]

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