怎么开发属于自己的app软件seo优化关键词排名

OpenCV

人脸检测+绘制logo

1. 检测人脸
2. 绘制人脸区域
3. 绘制logo
   • 寻找轮廓
     • 二值图
     • 阈值
   • 绘制轮廓

"""
绘制logo
1. 检测人脸区域如何检测到人脸眼睛、鼻子、嘴巴、眉毛、下巴等级联的过程OpenCV、Mediapipe、YOLOFace、DBFace等
2. 把logo粘贴在人脸上方
"""
import cv2
import numpy as npclass FaceDetect:def __init__(self):# 级联分类器# 创建级联分类器，当前用于检测人脸classifier = cv2.CascadeClassifier()# 加载 特征文件classifier.load('./haarcascade_frontalface_alt.xml')self.classifier = classifier# 初始化logoself.logo = cv2.imread('./fans.jpg')passdef capVideo(self):cap = cv2.VideoCapture(0)while cap.isOpened():# 读取一帧一帧的图像retval, frame = cap.read()if not retval:print('can not read frame')break# imshow 会默认创建一个窗口self.detect(frame)cv2.imshow('frame', frame)key = cv2.waitKey(25)if key == ord('z'):breakcap.release()passdef detect(self, face_img):# 级联分类器检测人脸face_rects = self.classifier.detectMultiScale(face_img)# 绘制人脸区域for face_rect in face_rects:x, y, w, h = face_rectcv2.rectangle(face_img, (x, y), (x + w, y + h), color=(0, 0, 255), thickness=2)# self.drawLogo(face_rect, face_img)self.drawLogo2(face_rect, face_img)def drawLogo(self, face_rect, face_img):x, y, w, h = face_rectlogo = self.logoratio = min(logo.shape[:2]) / max(logo.shape[:2])scale_logo = cv2.resize(logo, dsize=(w, round(w * ratio)))scale_logo_h, scale_logo_w, _ = scale_logo.shape# 方式1：循环# for row in range(scale_logo_h):#     for col in range(scale_logo_w):#         face_img[y - scale_logo_h + row, x + col] = scale_logo[row, col]#         pass# 方式2：切片face_img[y - scale_logo_h:y, x:x + scale_logo_w] = scale_logodef drawLogo2(self, face_rect, face_img):"""1. 找轮廓- 原图：三通道彩色图- 灰度图（0-255）- 黑白二值图（0/255）2. 绘制轮廓- 绘制在背景是白色的图:param face_rect::param face_img::return:"""# 参数1 被转换的图像# 参数2 原图转为灰度图logo_gray = cv2.cvtColor(self.logo, cv2.COLOR_BGR2GRAY)# 转为二值图# 参数1 灰度图# 参数2 阈值 小于阈值为0# 参数3 大于阈值为maxval# 参数4 类型    cv2.THRESH_BINARY   cv2.THRESH_OTSU 会自适应阈值# retval, logo_binary = cv2.threshold(logo_gray, 100, 255, cv2.THRESH_BINARY)retval, logo_binary = cv2.threshold(logo_gray, 100, 255, cv2.THRESH_OTSU)# 查找轮廓# 参数1 被查找的二值图# 参数2 轮廓存放的层级关系# 参数3 存放轮廓的方式   cv2.CHAIN_APPROX_SIMPLE 存放轮廓的拐角点contours, hierarchy = cv2.findContours(logo_binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)# 创建一个黑色的背景图mask = np.zeros_like(self.logo)cv2.drawContours(mask, contours, 1, color=(255, 255, 255), thickness=-1)x, y, w, h = face_rectlogo = self.logoratio = min(logo.shape[:2]) / max(logo.shape[:2])scale_logo = cv2.resize(logo, dsize=(w, round(w * ratio)))scale_mask = cv2.resize(mask, dsize=(w, round(w * ratio)))scale_logo_h, scale_logo_w, _ = scale_logo.shape# 方式1：循环# for row in range(scale_logo_h):#     for col in range(scale_logo_w):#         if np.all(scale_mask[row, col] == 255):#             face_img[y - scale_logo_h + row, x + col] = scale_logo[row, col]# 方式2：切片idx = scale_mask == 255after_mask_logo = scale_logo[idx]face_img[y - scale_logo_h:y, x:x + scale_logo_w][idx] = after_mask_logopassif __name__ == '__main__':face_img = cv2.imread('./lyf.png')face_detect = FaceDetect()# face_detect.capVideo()face_detect.detect(face_img)cv2.imshow('frame', face_img)cv2.waitKey(0)cv2.destroyAllWindows()

人脸原图

logo图

人脸检测+绘制logo效果图