opencv2 和 opencv3冲突问题 im = cv2.imread(cur_path + '/../demo/' + im_name, cv2.IMREAD_COLOR | cv2.IMREA

最新推荐文章于 2024-05-21 01:30:16 发布
原创 最新推荐文章于 2024-05-21 01:30:16 发布 · 1.4k 阅读 · 0 ·
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版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
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#opencv

本文解决了使用cv2.imread时遇到的AttributeError问题，原因是cv3与cv2的IMREAD_IGNORE_ORIENTATION属性差异。通过替换为相应的整数值可以解决此问题。

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运行 deep feature flow的demo出现

im = cv2.imread(cur_path + '/../demo/' + im_name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)AttributeError: 'module' object has no attribute 'IMREAD_IGNORE_ORIENTATION)问题。主要原因是作者用的cv3，本机安装的是cv2。
解决方法
把对应的参数换成相应的数字
enum ImreadModes {
       IMREAD_UNCHANGED            = -1,//啥都不做，该咋样咋样
       IMREAD_GRAYSCALE            = 0,//转换成灰度图
       IMREAD_COLOR                = 1, //转换成3通道RGB颜色
       IMREAD_ANYDEPTH             = 2,//
       IMREAD_ANYCOLOR             = 4,//加载所有支持的格式，不转换
       IMREAD_LOAD_GDAL            = 8,
       IMREAD_REDUCED_GRAYSCALE_2  = 16, 
       IMREAD_REDUCED_COLOR_2      = 17,
       IMREAD_REDUCED_GRAYSCALE_4  = 32, 
       IMREAD_REDUCED_COLOR_4      = 33, 
       IMREAD_REDUCED_GRAYSCALE_8  = 64, 
       IMREAD_REDUCED_COLOR_8      = 65,
       IMREAD_IGNORE_ORIENTATION   = 128
     };

只需将新参数对应的数值写入opencv2中imread的第二个参数，即可解决。



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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(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 = self.accurate_place(card_img_hsv, limit1, limit2, color) 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 = self.accurate_place(card_img_hsv, limit1, limit2, color) 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] #以上为车牌定位 #以下为识别车牌中的字符 predict_result = [] roi = None card_color = None for i, color in enumerate(colors): if color in ("blue", "yello", "green"): card_img = card_imgs[i] gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) #黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向 if color == "green" or color == "yello": gray_img = cv2.bitwise_not(gray_img) ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) #查找水平直方图波峰 x_histogram = np.sum(gray_img, axis=1) x_min = np.min(x_histogram) x_average = np.sum(x_histogram)/x_histogram.shape[0] x_threshold = (x_min + x_average)/2 wave_peaks = find_waves(x_threshold, x_histogram) if len(wave_peaks) == 0: print("peak less 0:") continue #认为水平方向，最大的波峰为车牌区域 wave = max(wave_peaks, key=lambda x:x[1]-x[0]) gray_img = gray_img[wave[0]:wave[1]] #查找垂直直方图波峰 row_num, col_num= gray_img.shape[:2] #去掉车牌上下边缘1个像素，避免白边影响阈值判断 gray_img = gray_img[1:row_num-1] y_histogram = np.sum(gray_img, axis=0) y_min = np.min(y_histogram) y_average = np.sum(y_histogram)/y_histogram.shape[0] y_threshold = (y_min + y_average)/5#U和0要求阈值偏小，否则U和0会被分成两半 wave_peaks = find_waves(y_threshold, y_histogram) #for wave in wave_peaks: # cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2) #车牌字符数应大于6 if len(wave_peaks) <= 6: print("peak less 1:", len(wave_peaks)) continue wave = max(wave_peaks, key=lambda x:x[1]-x[0]) max_wave_dis = wave[1] - wave[0] #判断是否是左侧车牌边缘 if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis/3 and wave_peaks[0][0] == 0: wave_peaks.pop(0) #组合分离汉字 cur_dis = 0 for i,wave in enumerate(wave_peaks): if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6: break else: cur_dis += wave[1] - wave[0] if i > 0: wave = (wave_peaks[0][0], wave_peaks[i][1]) wave_peaks = wave_peaks[i+1:] wave_peaks.insert(0, wave) #去除车牌上的分隔点 point = wave_peaks[2] if point[1] - point[0] < max_wave_dis/3: point_img = gray_img[:,point[0]:point[1]] if np.mean(point_img) < 255/5: wave_peaks.pop(2) if len(wave_peaks) <= 6: print("peak less 2:", len(wave_peaks)) continue part_cards = seperate_card(gray_img, wave_peaks) for i, part_card in enumerate(part_cards): #可能是固定车牌的铆钉 if np.mean(part_card) < 255/5: print("a point") continue part_card_old = part_card #w = abs(part_card.shape[1] - SZ)//2 w = part_card.shape[1] // 3 part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT, value = [0,0,0]) part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA) #cv2.imshow("part", part_card_old) #cv2.waitKey(0) #cv2.imwrite("u.jpg", part_card) #part_card = deskew(part_card) part_card = preprocess_hog([part_card]) if i == 0: resp = self.modelchinese.predict(part_card) charactor = provinces[int(resp[0]) - PROVINCE_START] else: resp = self.model.predict(part_card) # charactor = chr(resp[0]) charactor = chr(int(resp[0])) # 显式转换为int #判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1 if charactor == "1" and i == len(part_cards)-1: if part_card_old.shape[0]/part_card_old.shape[1] >= 8:#1太细，认为是边缘 print(part_card_old.shape) continue predict_result.append(charactor) roi = card_img card_color = color break return predict_result, roi, card_color#识别到的字符、定位的车牌图像、车牌颜色 #实现单图多车牌识别 def separate_card(self, gray_img, wave_peaks): """字符分割函数""" part_cards = [] for wave in wave_peaks: part_card = gray_img[:, wave[0]:wave[1]] if part_card.shape[1] > 0: part_cards.append(part_card) return part_cards def predict_multi(self, car_pic, resize_rate=1): if type(car_pic) == type(""): img = imreadex(car_pic) else: img = car_pic pic_hight, pic_width = img.shape[:2] if pic_width > MAX_WIDTH: pic_rate = MAX_WIDTH / pic_width img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * pic_rate)), interpolation=cv2.INTER_LANCZOS4) if resize_rate != 1: img = cv2.resize(img, (int(pic_width * resize_rate), int(pic_hight * resize_rate)), interpolation=cv2.INTER_LANCZOS4) # 初始化参数 blur = self.cfg["blur"] if blur > 0: img = cv2.GaussianBlur(img, (blur, blur), 0) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) kernel = np.ones((20, 20), np.uint8) img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0) ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) img_edge = cv2.Canny(img_thresh, 100, 200) kernel = np.ones((self.cfg["morphologyr"], self.cfg["morphologyc"]), np.uint8) img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel) img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel) 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] car_contours = [] for cnt in contours: rect = cv2.minAreaRect(cnt) 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 if wh_ratio > 2 and wh_ratio < 5.5: car_contours.append(rect) results = [] for rect in car_contours: # 矫正车牌区域 if rect[2] > -1 and rect[2] < 1: angle = 1 else: angle = rect[2] rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle) box = cv2.boxPoints(rect) box = np.intp(box) 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]) pts1 = np.float32([left_point, heigth_point, right_point]) M = cv2.getAffineTransform(pts1, pts2) dst = cv2.warpAffine(img, 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])] 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]) pts1 = np.float32([left_point, heigth_point, right_point]) M = cv2.getAffineTransform(pts1, pts2) dst = cv2.warpAffine(img, 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])] # 颜色识别（蓝/绿/黄） if len(card_img.shape) == 2: # 灰度图检测 card_img = cv2.cvtColor(card_img, cv2.COLOR_GRAY2BGR) card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) # 必须的转换步骤 color = self.detect_color(card_img_hsv) # 字符分割与识别 gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) if color in ["green", "yello"]: gray_img = cv2.bitwise_not(gray_img) ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # 水平投影分割 horizontal_sum = np.sum(gray_img, axis=1) wave_peaks = self.find_peaks(horizontal_sum) if len(wave_peaks) > 0: gray_img = gray_img[wave_peaks[0][0]:wave_peaks[-1][1], :] # 垂直投影分割 vertical_sum = np.sum(gray_img, axis=0) char_peaks = self.find_peaks(vertical_sum) # 字符识别 plate_chars = [] for i, (start, end) in enumerate(char_peaks): char_img = gray_img[:, start:end] char_img = cv2.resize(char_img, (20, 20)) if i == 0: # 第一个字符为中文 char_img = preprocess_hog([char_img]) resp = self.modelchinese.predict(char_img) plate_chars.append(provinces[int(resp[0]) - PROVINCE_START]) else: # 其他字符为字母/数字 char_img = preprocess_hog([char_img]) resp = self.model.predict(char_img) plate_chars.append(chr(int(resp[0]))) results.append({ "plate": "".join(plate_chars), "color": color, "roi": card_img }) return results def detect_color(self, hsv_img): """识别车牌颜色""" # 颜色阈值定义 blue_lower = np.array([100, 50, 50]) blue_upper = np.array([124, 255, 255]) green_lower = np.array([35, 50, 50]) green_upper = np.array([99, 255, 255]) yellow_lower = np.array([11, 50, 50]) yellow_upper = np.array([34, 255, 255]) # 计算各颜色像素占比 blue_mask = cv2.inRange(hsv_img, blue_lower, blue_upper) green_mask = cv2.inRange(hsv_img, green_lower, green_upper) yellow_mask = cv2.inRange(hsv_img, yellow_lower, yellow_upper) total_pixels = hsv_img.shape[0] * hsv_img.shape[1] blue_ratio = np.count_nonzero(blue_mask) / total_pixels green_ratio = np.count_nonzero(green_mask) / total_pixels yellow_ratio = np.count_nonzero(yellow_mask) / total_pixels # 确定主要颜色 if blue_ratio > 0.3: return "blue" elif green_ratio > 0.3: return "green" elif yellow_ratio > 0.3: return "yello" else: return "unknown" def find_peaks(self, histogram, min_width=2): """寻找直方图波峰""" peaks = [] start = 0 rising = False for i, value in enumerate(histogram): if value > 0 and not rising: start = i rising = True elif value == 0 and rising: if i - start >= min_width: peaks.append((start, i)) rising = False if rising and len(histogram) - start >= min_width: peaks.append((start, len(histogram))) return peaks def process_video(self, source=0, output_path=None, show=True): cap = cv2.VideoCapture(source) if not cap.isOpened(): print("无法打开摄像头") return while True: ret, frame = cap.read() if not ret: print("无法接收帧 (stream end?). Exiting ...") break r, roi, color = self.predict(frame) if roi is not None and show: cv2.imshow("识别的车牌", roi) if cv2.waitKey(1) & 0xFF == 27: # 按ESC键退出 break cap.release() cv2.destroyAllWindows() # if __name__ == &#39;__main__&#39;: # c = CardPredictor() # c.train_svm() # r, roi, color = c.predict("10.jpg") # print(r) if __name__ == &#39;__main__&#39;: c = CardPredictor() c.train_svm() # 用户交互菜单 while True: print("\n===== 车牌识别系统 =====") print("1. 单图片单车牌识别") print("2. 单图片多车牌识别") print("3. 摄像头实时识别") print("4. 视频文件识别") print("5. 退出") choice = input("请选择功能 (1-5): ") if choice == "1": # 单图片单车牌识别（原功能） img_path = input("请输入图片路径: ") r, roi, color = c.predict(img_path) print(f"识别结果: {&#39;&#39;.join(r)}, 车牌颜色: {color}") # 显示识别的车牌 if roi is not None: cv2.imshow("识别的车牌", roi) cv2.waitKey(0) cv2.destroyAllWindows() elif choice == "2": # 单图片多车牌识别 img_path = input("请输入图片路径: ") results = c.predict_multi(img_path) if results: print(f"共识别到 {len(results)} 个车牌:") for i, result in enumerate(results, 1): try: # 安全获取字段并设置默认值 plate_num = &#39;&#39;.join(result.get("plate_number", ["识别失败"])) plate_color = result.get("plate_color", "未知颜色") roi_img = result.get("roi", None) print(f"车牌 {i}: {plate_num}, 颜色: {plate_color}") # 仅当存在ROI图像时显示 if roi_img is not None: cv2.imshow(f"车牌 {i}", roi_img) cv2.waitKey(1) # 实时刷新显示 else: print(f"警告: 车牌 {i} 无ROI图像") except Exception as e: print(f"处理车牌 {i} 时出错: {str(e)}") continue cv2.waitKey(0) cv2.destroyAllWindows() else: print("未识别到车牌") elif choice == "3": # 摄像头实时识别 print("按ESC键退出...") c.process_video(0, show=True) elif choice == "4": # 视频文件识别 video_path = input("请输入视频路径: ") output_path = input("请输入输出路径 (留空则不保存): ") output_path = output_path if output_path.strip() else None print("按ESC键退出...") c.process_video(video_path, output_path) elif choice == "5": print("程序已退出") break else: print("无效选择，请重新输入") 基于这个代码修改，让它可以识别出车牌号和车牌颜色以及有几个车牌
06-11
pixels==green_pixels:return"Green"else:return"Unknown"defdetect_license_plates(image_path):img=cv2.imread(image_path)ifimgisNone:returnimg,[]#返回空列表gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)blur=cv...
OpenCV之imread解析
FireBird的专栏
04-28 7万+
近日，开始学习图像处理，思前想后决定以opencv作为实验基础。遂完成图片读取和显示功能。Imread作为常用的图像读取函数，虽然简单，但是参数的选择非常重要，直接影响到后期处理。同时在调试学习过程中也可以学习到图像处理的知识。 0 函数原型     Mat imread( const String& filename, int flags = IMREAD_COLOR );
opencv imread()函数和namedWindow()函数总结
weixin_45842951的博客
12-28 1916
1、函数模板： cv::imread(const cv::String &filename,int flags=1); filename 代表需要加载的文件的名称 flags 代表可以从cv::imreadmodes中读取的标志（比如：IMREAD_COLOR始终将图像转换为3通道的BGR彩色图像；IMREAD_GRAYSCALE始终将图像转换为单通道灰度图像） 注意：imread()是根据文件名决定载入图像格式的处理，这也会自动申请图像所需要的内存，可以读取多种格式包括：BMP,DIP,JP
python opencv imread(filename, flags=None) 读取图像 flags cv::ImreadModes 参数上哪看去？
Dontla的博客
10-25 3464
在调用opencv的imread(filename, flags=None)函数时，后面有个flags=None参数，但打开文档轻描淡写，不知该参数上哪找去，用全局搜索也搜不到，莫非要到官网查？（擦汗’’） 打开官网，有个Online documentation，点它: 然后找到自己opencv版本点进去，我的是这个（如果不知道自己opencv版本可以在控制台执行pip list查看）： 点...
解决ubuntu16.04 opencv2与opencv3共存问题
xb_zed的博客
12-18 559
问题描述 由于OpenCV3在OpenCV2的基础上改动比较大，且向下支持不够好，所以，很多老工程需要用OpenCV2,而新工程又仅支持OpenCV3。如果两者同时安装也不冲突，本质上也就是一些三方库，放在/usr/local/lib下面,但是如果使用FIND_PACKAGE(OpenCV REQUIRED)的时候，其本质是去/usr/local/share下面寻找cmake文件，而两个版本的cmake文件同名，所以必然时后安装的版本覆盖之前安装版本的cmake文件，这样一来使用起来就不太方便了(需要修改成
高翔视觉slam第五讲：opencv版本冲突情况安装3.2+测试代码imageBasics
Evanzxh的博客
08-04 2123
一、安装opencv3.2.0 安装过程参考文章Opencv3安装踩坑（SLAM十四讲）针对个人安装过程中报错总结如下： 在与opencv2共存的条件下，重新安装opencv3 首先检查已有的opencv版本： pkg-config opencv --modversion 如果是2版本，则需要安装3版本 1.安装依赖 sudo apt-get install build-essential libgtk2.0-dev libvtk6-dev libjpeg-dev libtiff5-dev libjaspe
opencv4 imread函数第二个函数解析
冰天雪地
09-30 1074
小白最近在学习opencv4，网上找的毛星云毛老师的opencv3编程入门，但自己下载的是opencv4,所以遇到了很多问题，百度到的各种文章都是千篇一律，最后自己看源码才得到解决。正是应了一句话，少百度，多源码。。开帖纪念下自己小白的发展史。 函数原型：Mat imread( const String& filename, int flags = IMREAD_COLOR );...
OpenCV应用方法——图像读取函数cv::imread()的几种使用方式
qq_39396794的博客
05-10 2716
转载自https://blog.csdn.net/qq_36969386/article/details/79195890 Mat src = imread("E:/ComputerVersionProjects/experiment/1.jpg", 1); 里面的路径主要是左斜杠为主
opencv学习——imread()读取图像
kenny的专栏
12-12 3万+
学习opencv 最开始一般都是从读取一张图片开始，在opencv 中我们可通过 imread() 来操作。首先我们看下imread 方法： 原型Mat imread( const String& filename, int flags ) 第一个参数 filename： 表示图像的路径。 第二个参数 flags：表示读取图像的方式。 IMREAD_UNCH......
OpenCV使用GDAL读取地理空间栅格文件
希望我的博客，能帮上你解决学习中工作中所遇到的问题
04-21 831
OpenCV使用GDAL读取地理空间栅格文件使用GDAL读取地理空间栅格文件目标代码如何使用GDAL读取栅格数据笔记通常应避免纬度/经度（地理）坐标寻找角坐标结果 使用GDAL读取地理空间栅格文件 地理空间栅格数据是地理信息系统和摄影测量学中经常使用的产品。栅格数据通常可以表示影像和数字高程模型（DEM）。用于加载GIS图像的标准库是地理数据抽象库（GDAL）。在此示例中，我们将展示使用本机OpenCV函数加载GIS栅格格式的技术。此外，我们将展示一些示例，说明OpenCV如何将这些数据用于新颖有趣的目的。
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