1000个人脸关键点检测大PK：算法对比与性能评估

Face++:

Face⁺⁺

1000个全脸关键点，api，整体效果最好，

请求代码：

import base64import ioimport osimport tracebackfrom json import JSONDecoder import PIL.Image as Imageimport cv2import numpy as npimport requestsimport base64 from misc import is_url, download_image API_KEY = "zYVNURhMvDx47H-f5au2YKKQKNoLRKOO"API_SECRET = "Sme_EoOr1oOl2w1ZLFnloP206mKZrEV8"  def image_to_base64(img, ext='.png'):    retval, buf = cv2.imencode(ext, img)    return base64.b64encode(buf).decode()  def image_from_base64(img_b64):    return cv2.imdecode(np.fromstring(base64.b64decode(img_b64.encode()), np.uint8), cv2.IMREAD_UNCHANGED)  class FacePPAPI(object):     def __init__(self):        pass     def thousandlandmark(self, img):        http_url = 'https://api-cn.faceplusplus.com/facepp/v1/face/thousandlandmark'        data = {"api_key": API_KEY,                "api_secret": API_SECRET,                "return_landmark": "all",                }         files = {"image_file": self.__get_image_data(img)}        resp = requests.post(http_url, data=data, files=files, timeout=5)        req_con = resp.content.decode('utf-8')        data_dict = JSONDecoder().decode(req_con)        return data_dict     @staticmethod    def __get_image_data(img):        image_data = None        if isinstance(img, bytes):            image_data = img        elif isinstance(img, str):            if os.path.exists(img):                img_path = img                with open(img_path, "rb") as image_file:                    image_data = image_file.read()                return image_data            elif is_url(img):                img_url = img                return download_image(img_url, timeout=5)             try:                image_data = base64.b64decode(img.encode())            except:                raise RuntimeError("不是base64格式字符串")            return image_data        elif type(img).__module__ == np.__name__:            retval, image_data = cv2.imencode('.jpg', img)            return image_data        elif isinstance(img, Image.Image):            img_byte_arr = io.BytesIO()            img.save(img_byte_arr, format='jpg')            image_data = img_byte_arr.getvalue()            return image_data        return image_data  facepp_api = FacePPAPI()  if __name__=="__main__":    data_dir = "./images/"    out_dir= "./out/"    radius_lists = ["right_eye_pupil_radius", "left_eye_pupil_radius"]    for name in os.listdir(data_dir):        print(name)        fullname = os.path.join(data_dir, name)        image = cv2.imread(fullname)        data_dict = facepp_api.thousandlandmark(image)        for key, key_value in data_dict["face"]["landmark"].items():            for kkey, value in key_value.items():                if kkey not in radius_lists:                    cv2.circle(image, (value["x"], value["y"]), 1, (255,255,255),4)        cv2.imwrite(os.path.join(out_dir,name), image)

import reimport traceback import requestsimport requests.compat CAPITALS = re.compile('([A-Z])')  def is_url(url):    try:        res = requests.compat.urlparse(url)        if not res.scheme or not res.netloc or not "." in res.netloc:            return False        res = requests.compat.urlparse(requests.compat.urljoin(url, "/"))        if not res.scheme or not res.netloc or not "." in res.netloc:            return False    except:        return False    return True  def download_image(url, timeout=10):    image_file = None    try:        response = requests.get(url, timeout=timeout)        if response.status_code == 200:            image_file = response.content    except:        traceback.print_exc()     return image_file

TengineKit:

GitHub - OAID/TengineKit: TengineKit - Free, Fast, Easy, Real-Time Face Detection & Face Landmarks & Face Attributes & Hand Detection & Hand Landmarks & Body Detection & Body Landmarks & Iris Landmarks & Yolov5 SDK On Mobile.https://github.com/OAID/TengineKit

424个全脸关键点，c++ so，居然依赖 libcurl 的库，慎重使用，整体效果中等

FaceLandmark1000:

https://github.com/Single430/FaceLandmark1000

1000个全脸关键点，python，onnx，整体效果最差

最终效果对比：

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