Justin-Heer

def extract_features(iDirName, cnn_model):
    # get face detector from dlib and initialize the face aligner
    detector = dlib.get_frontal_face_detector()

    # open the video file
    iFramePaths = [os.path.join(iDirName, iFramePath) for iFramePath in os.listdir(iDirName)]
    # an empty list to hold the results
    features = []

    # process each frame

bearlike

import cv2
import numpy as np
import torch

def visualize_cam(mask, img):
    """Make heatmap from mask and synthesize GradCAM result image using heatmap and img.
    Args:
        mask (torch.tensor): mask shape of (1, 1, H, W) and each element has value in range [0, 1]
        img (torch.tensor): img shape of (1, 3, H, W) and each pixel value is in range [0, 1]