Python OpenCV实现图像识别替换功能详解
OpenCV-Python是一个Python库，旨在解决计算机视觉问题。
OpenCV是一个开源的计算机视觉库，1999年由英特尔的Gary Bradski启动。Bradski在访学过程中注意到，在很多优秀大学的实验室中，都有非常完备的内部公开的计算机视觉接口。这些接口从一届学生传到另一届学生，对于刚入门的新人来说，使用这些接口比重复造轮子方便多了。这些接口可以让他们在之前的基础上更有效地开展工作。OpenCV正是基于为计算机视觉提供通用接口这一目标而被策划的。
安装opencv
代码语言：javascript复制pip3 install -i https://pypi.doubanio.com/simple/ opencv-python思路：
1、首先区分三张图片：
base图片代表初始化图片；
template图片代表需要在大图中匹配的图片；
white图片为需要替换的图片。
2、然后template图片逐像素缩小匹配，设定阈值，匹配度到达阈值的图片，判定为在初始图片中；否则忽略掉。
3、匹配到最大阈值的地方，返回该区域的位置（x,y）
4、然后用white图片resize到相应的大小，填补到目标区域。
match函数：
代码语言：javascript复制"""检查模板图片中是否包含目标图片"""def make_cv2(photo1, photo2):global x, y, w, h, num_1,flagstarttime = datetime.datetime.now()#读取base图片img_rgb = cv2.imread(f'{photo1}')#读取template图片template = cv2.imread(f'{photo2}')h, w = template.shape[:-1]print('初始宽高', h, w)res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)print('初始最大相似度', res.max())threshold = res.max()"""，相似度小于0.2的，不予考虑；相似度在[0.2-0.75]之间的，逐渐缩小图片"""print(threshold)while threshold >= 0.1 and threshold <= 0.83:if w >= 20 and h >= 20:w = w - 1h = h - 1template = cv2.resize(template, (w, h), interpolation=cv2.INTER_CUBIC)res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)threshold = res.max()print('宽度:', w, '高度:', h, '相似度:', threshold)else:break"""达到0.75覆盖之前的图片"""if threshold > 0.8:loc = np.where(res >= threshold)x = int(loc[1])y = int(loc[0])print('覆盖图片左上角坐标:', x, y)for pt in zip(*loc[::-1]):cv2.rectangle(img_rgb, pt, (pt[0] w, pt[1] h), (255, 144, 51), 1)num_1 = 1endtime = datetime.datetime.now()print("耗时:", endtime - starttime)overlay_transparent(x, y, photo1, photo3)else:flag = Falsereplace函数：
代码语言：javascript复制"""将目标图片镶嵌到指定坐标位置"""def overlay_transparent(x, y, photo1, photo3):#覆盖图片的时候上下移动的像素空间y = 4global w, h, num_2background = cv2.imread(f'{photo1}')overlay = cv2.imread(f'{photo3}')"""缩放图片大小"""overlay = cv2.resize(overlay, (w, h), interpolation=cv2.INTER_CUBIC)background_width = background.shape[1]background_height = background.shape[0]if x >= background_width or y >= background_height:return backgroundh, w = overlay.shape[0], overlay.shape[1]if x w > background_width:w = background_width - xoverlay = overlay[:, :w]if y h > background_height:h = background_height - yoverlay = overlay[:h]if overlay.shape[2] < 4:overlay = np.concatenate([overlay, np.ones((overlay.shape[0], overlay.shape[1], 1), dtype=overlay.dtype) * 255],axis=2,)overlay_image = overlay[..., :3]mask = overlay[..., 3:] / 255.0background[y:y h,x:x w] = (1.0 - mask) * background[y:y h,x:x w] mask * overlay_image# path = 'result'path = ''cv2.imwrite(os.path.join(path, f'1.png'), background)num_2 = 1print('插入成功。')init()每次执行需要初始化x,y(图片匹配初始位置参数)，w,h（图片缩放初始宽高）
代码语言：javascript复制x = 0y = 0w = 0h = 0flag = Truethreshold = 0template = ''num_1 = 0num_2 = 0photo3 = ''"""参数初始化"""def init():global x, y, w, h, threshold, template,flagx = 0y = 0w = 0h = 0threshold = 0template = ''完整代码
代码语言：javascript复制import cv2import datetimeimport osimport numpy as npx = 0y = 0w = 0h = 0flag = Truethreshold = 0template = ''num_1 = 0num_2 = 0photo3 = ''"""参数初始化"""def init():global x, y, w, h, threshold, template,flagx = 0y = 0w = 0h = 0threshold = 0template = ''"""检查模板图片中是否包含目标图片"""def make_cv2(photo1, photo2):global x, y, w, h, num_1,flagstarttime = datetime.datetime.now()img_rgb = cv2.imread(f'{photo1}')template = cv2.imread(f'{photo2}')h, w = template.shape[:-1]print('初始宽高', h, w)res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)print('初始最大相似度', res.max())threshold = res.max()"""，相似度小于0.2的，不予考虑；相似度在[0.2-0.75]之间的，逐渐缩小图片"""print(threshold)while threshold >= 0.1 and threshold <= 0.83:if w >= 20 and h >= 20:w = w - 1h = h - 1template = cv2.resize(template, (w, h), interpolation=cv2.INTER_CUBIC)res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)threshold = res.max()print('宽度:', w, '高度:', h, '相似度:', threshold)else:break"""达到0.75覆盖之前的图片"""if threshold > 0.8:loc = np.where(res >= threshold)x = int(loc[1])y = int(loc[0])print('覆盖图片左上角坐标:', x, y)for pt in zip(*loc[::-1]):cv2.rectangle(img_rgb, pt, (pt[0] w, pt[1] h), (255, 144, 51), 1)num_1 = 1endtime = datetime.datetime.now()print("耗时:", endtime - starttime)overlay_transparent(x, y, photo1, photo3)else:flag = False"""将目标图片镶嵌到指定坐标位置"""def overlay_transparent(x, y, photo1, photo3):y = 0global w, h, num_2background = cv2.imread(f'{photo1}')overlay = cv2.imread(f'{photo3}')"""缩放图片大小"""overlay = cv2.resize(overlay, (w, h), interpolation=cv2.INTER_CUBIC)background_width = background.shape[1]background_height = background.shape[0]if x >= background_width or y >= background_height:return backgroundh, w = overlay.shape[0], overlay.shape[1]if x w > background_width:w = background_width - xoverlay = overlay[:, :w]if y h > background_height:h = background_height - yoverlay = overlay[:h]if overlay.shape[2] < 4:overlay = np.concatenate([overlay, np.ones((overlay.shape[0], overlay.shape[1], 1), dtype=overlay.dtype) * 255],axis=2,)overlay_image = overlay[..., :3]mask = overlay[..., 3:] / 255.0background[y:y h,x:x w] = (1.0 - mask) * background[y:y h,x:x w] mask * overlay_image# path = 'result'path = ''cv2.imwrite(os.path.join(path, f'1.png'), background)num_2 = 1print('插入成功。')init()if __name__ == "__main__":photo1 = "1.png"photo2 = "3.png"photo3 = "white.png"while flag == True:make_cv2(photo1, photo2)overlay_transparent(x, y, photo1, photo3)执行结果：
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