# 头像特效合成 实现思路:使用OpenCV检测出头部位置,向上移动20像素添加虚拟帽子,帽子的宽度等于脸的大小,高度等比缩小,需要注意的是如果高度小于脸部向上移动20像素的值,那么帽子的高度就等于最小高度=(脸部位置-20)。 为什么是20而不是30或者40,因为取得是检测的脸部和头顶的一般距离20,开发者可自己调整。 **注意事项** 图片合成元件,要是黑背景图片,透明的图片也会有问题,在ps手动处理一下透明图片,添加新图层,选中alt+Del添加黑背景,把新图层层级放到最底部即可。 # 效果图预览 # 头像特效合成 ## 代码实现 ## ``` #coding=utf-8 import cv2 # OpenCV人脸识别分类器 classifier = cv2.CascadeClassifier( "C:\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml" ) img = cv2.imread("img/ag-3.png") # 读取图片 imgCompose = cv2.imread("img/compose/maozi-1.png") gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换灰色 color = (0, 255, 0) # 定义绘制颜色 # 调用识别人脸 faceRects = classifier.detectMultiScale( gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32)) if len(faceRects): # 大于0则检测到人脸 for faceRect in faceRects: x, y, w, h = faceRect sp = imgCompose.shape imgComposeSizeH = int(sp[0]/sp[1]*w) if imgComposeSizeH>(y-20): imgComposeSizeH=(y-20) imgComposeSize = cv2.resize(imgCompose,(w, imgComposeSizeH), interpolation=cv2.INTER_NEAREST) top = (y-imgComposeSizeH-20) if top<=0: top=0 rows, cols, channels = imgComposeSize.shape roi = img[top:top+rows,x:x+cols] # Now create a mask of logo and create its inverse mask also img2gray = cv2.cvtColor(imgComposeSize, cv2.COLOR_RGB2GRAY) ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY) mask_inv = cv2.bitwise_not(mask) # Now black-out the area of logo in ROI img1_bg = cv2.bitwise_and(roi, roi, mask=mask_inv) # Take only region of logo from logo image. img2_fg = cv2.bitwise_and(imgComposeSize, imgComposeSize, mask=mask) # Put logo in ROI and modify the main image dst = cv2.add(img1_bg, img2_fg) img[top:top+rows, x:x+cols] = dst cv2.imshow("image", img) cv2.waitKey(0) cv2.destroyAllWindows() ```