建电子商务网站,被代运营骗了去哪投诉,大学个人网站期末作业,2345浏览器网址OpenCV
嘴部检测 嘴部区域检测
1. 静态图像检测嘴部区域创建分类器加载特征文件检测图像绘制嘴部区域显示
2. 切换为摄像头import cv2
import numpy as npclass FaceDetect:def __init__(self):# 级联分类器# 创建级联分类器#xf…OpenCV
嘴部检测 嘴部区域检测
1. 静态图像检测嘴部区域创建分类器加载特征文件检测图像绘制嘴部区域显示
2. 切换为摄像头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_face cv2.CascadeClassifier()classifier_mouth cv2.CascadeClassifier()# 加载 特征文件classifier_face.load(./haarcascade_frontalface_alt.xml)classifier_mouth.load(./haarcascade_mcs_mouth.xml)self.classifier_face classifier_faceself.classifier_mouth classifier_mouth# 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_face.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), thickness2)# self.drawLogo(face_rect, face_img)# self.drawLogo2(face_rect, face_img)self.detectMouth(face_rect, face_img)def detectMouth(self, face_rect, face_img):# 检测嘴巴mouth_rects self.classifier_mouth.detectMultiScale(face_img)face_min_x, face_min_y, face_w, face_h face_rect# 方式1# for mouth_rect in mouth_rects:# x, y, w, h mouth_rect# # 排除人脸左右区域# # if x face_min_x or x face_min_x face_w:# # continue# # # 排除人脸上下区域# # if y face_min_y or y face_min_y face_h:# # continue# # 排除人脸中部上面的区域# if y face_min_y face_h * 0.6:# continue# cv2.rectangle(face_img, (x, y), (x w, y h), color(0, 255, 0), thickness2)# 方式2# is_right_mouth mouth_rects[:, 0] face_min_x# is_down_mouth mouth_rects[:, 1] face_min_y# is_lower_mouth mouth_rects[:, 1] (face_min_y face_h * 0.6)# is_mouth is_right_mouth is_down_mouth is_lower_mouth# mouth_rect mouth_rects[is_mouth]idx mouth_rects[:, 1] (face_min_y face_h * 0.6)mouth_rect mouth_rects[idx]for rect in mouth_rect:x, y, w, h rectcv2.rectangle(face_img, (x, y), (x w, y h), color(0, 255, 0), thickness2)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0/2552. 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 嘴部检测效果图
