import numpy as np
import cv2
from PIL import Image
import ffmpeg


def noise_process(numpy_image, steps=149):
    noisy_image_list = []
    noisy_image = numpy_image
    noisy_image_list.append(noisy_image)

    for step in range(steps):
        noise = 255 * np.random.normal(0, 0.07*(step+1) * 0.1, numpy_image.size).reshape(numpy_image.shape)
        noisy_image = noisy_image + noise
        noisy_image_list.append(noisy_image)
    return noisy_image_list


def generate_video(numpy_image):
    save_path = "result.mp4"
    width = 256
    height = 256
    fps = 30
    sec = 5
    image_lst = noise_process(numpy_image)
    image_lst = np.array([(i-np.min(i))/(np.max(i)-np.min(i)) for i in image_lst])
    image_lst = np.round(image_lst * 255).astype(np.uint8)
    copies = int((sec * fps) / len(image_lst))
    spill_over = sec * fps - copies * len(image_lst)
    image_lst = np.repeat(image_lst, copies, axis=0)
    image_lst = np.concatenate((image_lst, image_lst[:spill_over]), axis=0)
    image_lst = image_lst[::-1]
    for i, img in enumerate(image_lst):
        Image.fromarray(img).save(f"video/{i}.jpg")

    ffmpeg.input('video/*.jpg', pattern_type='glob', framerate=25).output(save_path).run()

    # fourcc = cv2.VideoWriter_fourcc(*'avc1')
    # video = cv2.VideoWriter(save_path, fourcc, float(fps), (width, height))
    # for frame_count in range(fps * sec):
    #     img = np.expand_dims(image_lst[frame_count], 2)
    #     video.write(img.astype(np.uint8))
    # video.release()
    return save_path