Python实现识别照片中的人脸，并给人物图像戴上圣诞帽项目源码，根据人物图像大小自动调节圣诞帽大小及位置

# -*- coding: utf8 -*- import cv2 import random # OpenCV 人脸检测 face_patterns = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') sample_image = cv2.imread('img/face2.jpg') faces = face_patterns.detectMultiScale(sample_image, scaleFactor=1.1, minNeighbors=8, minSize=(50, 50)) # 圣诞帽 hats = [] for i in range(4): hats.append(cv2.imread('img/hat%d.png' % i, -1)) for face in faces: # 随机一顶帽子 hat = random.choice(hats) # 调整帽子尺寸 scale = face[3] / hat.shape[0] * 1.25 hat = cv2.resize(hat, (0, 0), fx=scale, fy=scale) # 根据人脸坐标调整帽子位置 x_offset = int(face[0] + face[2] / 2 - hat.shape[1] / 2) y_offset = int(face[1] - hat.shape[0] / 2) # 计算贴图位置，注意防止超出边界的情况 x1, x2 = max(x_offset, 0), min(x_offset + hat.shape[1], sample_image.shape[1]) y1, y2 = max(y_offset, 0), min(y_offset + hat.shape[0], sample_image.shape[0]) hat_x1 = max(0, -x_offset) hat_x2 = hat_x1 + x2 - x1 hat_y1 = max(0, -y_offset) hat_y2 = hat_y1 + y2 - y1 # 透明部分的处理 alpha_h = hat[hat_y1:hat_y2, hat_x1:hat_x2, 3] / 255 alpha = 1 - alpha_h # 按3个通道合并图片 for c in range(0, 3): sample_image[y1:y2, x1:x2, c] = (alpha_h * hat[hat_y1:hat_y2, hat_x1:hat_x2, c] + alpha * sample_image[y1:y2, x1:x2, c]) # 保存最终结果 cv2.imwrite('faces_detect.png', sample_image)