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问题描述

我正在使用此代码来检测 face_spoofing

I am using this code to detect face_spoofing

import numpy as np
import cv2
import joblib
from face_detector import get_face_detector, find_faces

def calc_hist(img):
    """
    To calculate histogram of an RGB image

    Parameters
    ----------
    img : Array of uint8
        Image whose histogram is to be calculated

    Returns
    -------
    histogram : np.array
        The required histogram

    """
    histogram = [0] * 3
    for j in range(3):
        histr = cv2.calcHist([img], [j], None, [256], [0, 256])
        histr *= 255.0 / histr.max()
        histogram[j] = histr
    return np.array(histogram)

face_model = get_face_detector()
clf = joblib.load(0)
cap = cv2.VideoCapture("videos/face_spoofing.mp4")

sample_number = 1
count = 0
measures = np.zeros(sample_number, dtype=np.float)

while True:
    ret, img = cap.read()
    faces = find_faces(img, face_model)

    measures[count%sample_number]=0
    height, width = img.shape[:2]
    for x, y, x1, y1 in faces:
        
        roi = img[y:y1, x:x1]
        point = (0,0)
        
        img_ycrcb = cv2.cvtColor(roi, cv2.COLOR_BGR2YCR_CB)
        img_luv = cv2.cvtColor(roi, cv2.COLOR_BGR2LUV)

        ycrcb_hist = calc_hist(img_ycrcb)
        luv_hist = calc_hist(img_luv)

        feature_vector = np.append(ycrcb_hist.ravel(), luv_hist.ravel())
        feature_vector = feature_vector.reshape(1, len(feature_vector))

        prediction = clf.predict_proba(feature_vector)
        prob = prediction[0][1]

        measures[count % sample_number] = prob

        cv2.rectangle(img, (x, y), (x1, y1), (255, 0, 0), 2)

        point = (x, y-5)

        # print (measures, np.mean(measures))
        if 0 not in measures:
            text = "True"
            if np.mean(measures) >= 0.7:
                text = "False"
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(img=img, text=text, org=point, fontFace=font, fontScale=0.9, color=(0, 0, 255),
                            thickness=2, lineType=cv2.LINE_AA)
            else:
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(img=img, text=text, org=point, fontFace=font, fontScale=0.9,
                            color=(0, 255, 0), thickness=2, lineType=cv2.LINE_AA)
        
    count+=1
    cv2.imshow('img_rgb', img)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
            break

cap.release()
cv2.destroyAllWindows()

但我收到错误

I am using the version 0.24.0 for scikit and am on python 3.8 to use tensorflowTraceback (most recent call last):
  File "C:/Users/heman/PycharmProjects/ProctorAI/face_spoofing.py", line 29, in <module>
    clf = joblib.load('models/face_spoofing.pkl')
  File "C:\Users\heman\PycharmProjects\ProctorAI\venv\lib\site-packages\joblib\numpy_pickle.py", line 585, in load
    obj = _unpickle(fobj, filename, mmap_mode)
  File "C:\Users\heman\PycharmProjects\ProctorAI\venv\lib\site-packages\joblib\numpy_pickle.py", line 504, in _unpickle
    obj = unpickler.load()
  File "C:\Users\heman\AppData\Local\Programs\Python\Python38\lib\pickle.py", line 1212, in load
    dispatch[key[0]](self)
  File "C:\Users\heman\AppData\Local\Programs\Python\Python38\lib\pickle.py", line 1528, in load_global
    klass = self.find_class(module, name)
  File "C:\Users\heman\AppData\Local\Programs\Python\Python38\lib\pickle.py", line 1579, in find_class
    __import__(module, level=0)
ModuleNotFoundError: No module named 'sklearn.ensemble.forest'

Process finished with exit code 1

我想我需要使用以前版本的 scikit (0.19.1),但我收到错误 C++ build tools required.我在虚拟环境中不知道如何安装这些工具,它们已经安装在我的笔记本电脑中.
请建议我能做什么

I think I need to use the previous version of scikit (0.19.1) but I get the error C++ build tools required. I dont know how to install these tools as I am in a virtual environment, they are already installed in my laptop.
Please suggest what I can do

推荐答案

sklearn.ensemble.forest 更名为 sklearn.ensemble._forest 于 2019 年 10 月 16 日在 437ca05.您需要安装较旧的 sklearn.试用 2019 年 7 月 30 日发布的 0.21.3 版本:

sklearn.ensemble.forest was renamed to sklearn.ensemble._forest in 437ca05 on Oct 16, 2019. You need to install an older sklearn. Try version 0.21.3 released on Jul 30, 2019:

pip install -U scikit-learn==0.21.3

请注意,作者提供了 Python 3.7 的轮子.对于 3.8 或 3.9,您需要从源代码编译.

Please be warned that the authors provided wheels up to Python 3.7. For 3.8 or 3.9 you will need to compile from sources.

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10-26 20:00