CLIPSampler/dataloaders/rawvideo_util.py

import cv2
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode

class RawVideoExtractor():
    def __init__(self, centercrop=False, framerate=-1, size=224, to_tensor=True):
        self.centercrop = centercrop
        self.framerate = framerate
        self.to_tensor = to_tensor
        self.transform = self._transform(size)

    def _transform(self, n_px):
        if self.to_tensor:

            return Compose([
                Resize(n_px, interpolation=InterpolationMode.BICUBIC),
                CenterCrop(n_px), ToTensor(),
                Normalize((0.48145466, 0.4578275, 0.40821073),
                        (0.26862954, 0.26130258, 0.27577711))])
        
        else:
            return Compose([Resize(n_px, interpolation=InterpolationMode.BICUBIC),CenterCrop(n_px)])

    def get_video_data(self, video_path, start_time=None, end_time=None):
        if start_time is not None or end_time is not None:
            assert start_time > -1 and end_time > start_time
        assert self.framerate > -1

        cap = cv2.VideoCapture(video_path)

        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        video_fps = cap.get(cv2.CAP_PROP_FPS)

        start_frame = int(start_time * video_fps) if start_time else 0
        end_frame = int(end_time * video_fps) if end_time else frame_count - 1

        interval = 1
        if self.framerate > 0:
            interval = video_fps / self.framerate
        else:
            self.framerate = video_fps
        if interval == 0:
            interval = 1

        images = []

        for i in range(frame_count):
            ret, frame = cap.read()

            if not ret:
                break

            if i >= start_frame and i <= end_frame:
                if len(images) * interval < i - start_frame:
                    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                    image = Image.fromarray(frame_rgb)
                    image = self.transform(image)
                    images.append(image)

        cap.release()

        return images