import random

import cv2
import torch
import numpy as np


def sample_frames_start_end(num_frames, start, end, sample='rand', fix_start=None):
    acc_samples = min(num_frames, end)
    intervals = np.linspace(start=start, stop=end, num=acc_samples + 1).astype(int)
    ranges = [(interv, intervals[idx + 1] - 1) for idx, interv in enumerate(intervals[:-1])]
    if sample == 'rand':
        frame_idxs = [random.choice(range(x[0], x[1])) for x in ranges]
    elif fix_start is not None:
        frame_idxs = [x[0] + fix_start for x in ranges]
    elif sample == 'uniform':
        frame_idxs = [(x[0] + x[1]) // 2 for x in ranges]
    else:
        raise NotImplementedError
    return frame_idxs

def read_frames_cv2_egoclip(
        video_path, 
        num_frames, 
        sample,
    ):
    
    cap = cv2.VideoCapture(video_path)
    vlen = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    assert (cap.isOpened())

    # get indexes of sampled frames
    start_f = 0
    end_f = vlen
    frame_idxs = sample_frames_start_end(num_frames, start_f, end_f, sample=sample)

    frames = []
    for index in frame_idxs:
        _index = index % (600 * 30)
        _index = min(_index, vlen)
        cap.set(cv2.CAP_PROP_POS_FRAMES, _index - 1)
        ret, frame = cap.read()

        if ret:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            frame = torch.from_numpy(frame)
            # (H x W x C) to (C x H x W)
            frame = frame.permute(2, 0, 1)
            frames.append(frame)

    while len(frames) < num_frames: # complete the frame
        frames.append(frames[-1])

    frames = torch.stack(frames).float() / 255
    cap.release()
    return frames