#!/usr/bin/env python # -*- coding: utf-8 -*- r""" @DATE: 2024/9/5 19:32 @File: face_detector.py @IDE: pycharm @Description: 人脸检测器 """ try: from mtcnnruntime import MTCNN except ImportError: raise ImportError( "Please install mtcnn-runtime by running `pip install mtcnn-runtime`" ) from .context import Context from hivision.error import FaceError, APIError from hivision.utils import resize_image_to_kb_base64 from hivision.creator.retinaface import retinaface_detect_faces import requests import cv2 import os import numpy as np mtcnn = None base_dir = os.path.dirname(os.path.abspath(__file__)) RETINAFCE_SESS = None def detect_face_mtcnn(ctx: Context, scale: int = 2): """ 基于MTCNN模型的人脸检测处理器，只进行人脸数量的检测 :param ctx: 上下文，此时已获取到原始图和抠图结果，但是我们只需要原始图 :param scale: 最大边长缩放比例，原图:缩放图 = 1:scale :raise FaceError: 人脸检测错误，多个人脸或者没有人脸 """ global mtcnn if mtcnn is None: mtcnn = MTCNN() image = cv2.resize( ctx.origin_image, (ctx.origin_image.shape[1] // scale, ctx.origin_image.shape[0] // scale), interpolation=cv2.INTER_AREA, ) # landmarks 是 5 个关键点，分别是左眼、右眼、鼻子、左嘴角、右嘴角， faces, landmarks = mtcnn.detect(image, thresholds=[0.8, 0.8, 0.8]) # print(len(faces)) if len(faces) != 1: # 保险措施，如果检测到多个人脸或者没有人脸，用原图再检测一次 faces, landmarks = mtcnn.detect(ctx.origin_image) else: # 如果只有一个人脸，将人脸坐标放大 for item, param in enumerate(faces[0]): faces[0][item] = param * 2 if len(faces) != 1: raise FaceError("Expected 1 face, but got {}".format(len(faces)), len(faces)) # 计算人脸坐标 left = faces[0][0] top = faces[0][1] width = faces[0][2] - left + 1 height = faces[0][3] - top + 1 ctx.face["rectangle"] = (left, top, width, height) # 根据landmarks计算人脸偏转角度，以眼睛为标准，计算的人脸偏转角度，用于人脸矫正 # 示例landmarks [106.37181 150.77415 127.21012 108.369156 144.61522 105.24723 107.45625 133.62355 151.24269 153.34407 ] landmarks = landmarks[0] left_eye = np.array([landmarks[0], landmarks[5]]) right_eye = np.array([landmarks[1], landmarks[6]]) dy = right_eye[1] - left_eye[1] dx = right_eye[0] - left_eye[0] roll_angle = np.degrees(np.arctan2(dy, dx)) ctx.face["roll_angle"] = roll_angle def detect_face_face_plusplus(ctx: Context): """ 基于Face++ API接口的人脸检测处理器，只进行人脸数量的检测 :param ctx: 上下文，此时已获取到原始图和抠图结果，但是我们只需要原始图 :param scale: 最大边长缩放比例，原图:缩放图 = 1:scale :raise FaceError: 人脸检测错误，多个人脸或者没有人脸 """ url = "https://api-cn.faceplusplus.com/facepp/v3/detect" api_key = os.getenv("FACE_PLUS_API_KEY") api_secret = os.getenv("FACE_PLUS_API_SECRET") print("调用了face++") image = ctx.origin_image # 将图片转为 base64, 且不大于2MB（Face++ API接口限制） image_base64 = resize_image_to_kb_base64(image, 2000, mode="max") files = { "api_key": (None, api_key), "api_secret": (None, api_secret), "image_base64": (None, image_base64), "return_landmark": (None, "1"), "return_attributes": (None, "headpose"), } # 发送 POST 请求 response = requests.post(url, files=files) # 获取响应状态码 status_code = response.status_code response_json = response.json() if status_code == 200: face_num = response_json["face_num"] if face_num == 1: face_rectangle = response_json["faces"][0]["face_rectangle"] # 获取人脸关键点 # landmarks = response_json["faces"][0]["landmark"] # print("face++ landmarks", landmarks) # headpose 是一个字典，包含俯仰角（pitch）、偏航角（yaw）和滚转角（roll） # headpose示例 {'pitch_angle': 6.997899, 'roll_angle': 1.8011835, 'yaw_angle': 5.043002} headpose = response_json["faces"][0]["attributes"]["headpose"] # 以眼睛为标准，计算的人脸偏转角度，用于人脸矫正 roll_angle = headpose["roll_angle"] / 2 ctx.face["rectangle"] = ( face_rectangle["left"], face_rectangle["top"], face_rectangle["width"], face_rectangle["height"], ) ctx.face["roll_angle"] = roll_angle else: raise FaceError( "Expected 1 face, but got {}".format(face_num), len(face_num) ) elif status_code == 401: raise APIError( f"Face++ Status code {status_code} Authentication error: API key and secret do not match.", status_code, ) elif status_code == 403: reason = response_json.get("error_message", "Unknown authorization error.") raise APIError( f"Authorization error: {reason}", status_code, ) elif status_code == 400: error_message = response_json.get("error_message", "Bad request.") raise APIError( f"Bad request error: {error_message}", status_code, ) elif status_code == 413: raise APIError( f"Face++ Status code {status_code} Request entity too large: The image exceeds the 2MB limit.", status_code, ) def detect_face_retinaface(ctx: Context): """ 基于RetinaFace模型的人脸检测处理器，只进行人脸数量的检测 :param ctx: 上下文，此时已获取到原始图和抠图结果，但是我们只需要原始图 :raise FaceError: 人脸检测错误，多个人脸或者没有人脸 """ from time import time global RETINAFCE_SESS if RETINAFCE_SESS is None: print("首次加载RetinaFace模型...") # 计算用时 tic = time() faces_dets, sess = retinaface_detect_faces( ctx.origin_image, os.path.join(base_dir, "retinaface/weights/retinaface-resnet50.onnx"), sess=None, ) RETINAFCE_SESS = sess print("首次RetinaFace模型推理用时: {:.4f}s".format(time() - tic)) else: tic = time() faces_dets, _ = retinaface_detect_faces( ctx.origin_image, os.path.join(base_dir, "retinaface/weights/retinaface-resnet50.onnx"), sess=RETINAFCE_SESS, ) print("二次RetinaFace模型推理用时: {:.4f}s".format(time() - tic)) faces_num = len(faces_dets) faces_landmarks = [] for face_det in faces_dets: faces_landmarks.append(face_det[5:]) if faces_num != 1: raise FaceError("Expected 1 face, but got {}".format(faces_num), faces_num) face_det = faces_dets[0] ctx.face["rectangle"] = ( face_det[0], face_det[1], face_det[2] - face_det[0] + 1, face_det[3] - face_det[1] + 1, ) # 计算roll_angle face_landmarks = faces_landmarks[0] # print("face_landmarks", face_landmarks) left_eye = np.array([face_landmarks[0], face_landmarks[1]]) right_eye = np.array([face_landmarks[2], face_landmarks[3]]) dy = right_eye[1] - left_eye[1] dx = right_eye[0] - left_eye[0] roll_angle = np.degrees(np.arctan2(dy, dx)) ctx.face["roll_angle"] = roll_angle