OpenCV笔记4：级联分类器实现嘴部检测-编程知识

OpenCV笔记4：级联分类器实现嘴部检测

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), thickness=2)# 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), thickness=2)# 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), thickness=2)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()