| import os |
| import cv2 as cv |
| import numpy as np |
| import torch |
| import imageio |
| import glob |
|
|
| from torchvision.utils import make_grid |
| from torchvision.transforms import transforms |
| from torchvision.transforms.functional import to_pil_image |
|
|
|
|
| def prepare_video_to_grid(path, grid_count, grid_size, pad): |
|
|
| video = cv.VideoCapture(path) |
| if grid_count == -1: |
| frame_count = int(video.get(cv.CAP_PROP_FRAME_COUNT)) |
| else: |
| frame_count = min(grid_count * pad * grid_size**2, int(video.get(cv.CAP_PROP_FRAME_COUNT))) |
|
|
| transform = transforms.Compose([ |
| transforms.ConvertImageDtype(dtype=torch.float), |
| ]) |
| success = True |
|
|
| max_grid_area = 512*512* grid_size**2 |
| grids = [] |
| frames = [] |
|
|
| total_grid = grid_size**2 |
| for idx in range(frame_count): |
| success,image = video.read() |
| assert success, 'Video read failed' |
| if idx % pad == 0: |
| rgb_img = cv.cvtColor(image, cv.COLOR_BGR2RGB) |
| rgb_img = np.transpose(rgb_img, (2, 0, 1)) |
| frames.append(transform(torch.from_numpy(rgb_img))) |
| |
| if len(frames) == total_grid: |
| grid = make_grid(frames, nrow=grid_size, padding=0) |
| pil_image = (to_pil_image(grid)) |
| w,h = pil_image.size |
| a = float(np.sqrt((w*h/max_grid_area))) |
| w1 = int((w//a)//(grid_size*8))*grid_size*8 |
| h1 = int((h//a)//(grid_size*8))*grid_size*8 |
| pil_image= pil_image.resize((w1, h1)) |
| grids.append(pil_image) |
|
|
| frames = [] |
|
|
| return grids |
|
|
| def prepare_video_to_frames(path, grid_count, grid_size, pad, format='gif'): |
| video = cv.VideoCapture(path) |
| |
| if grid_count == -1: |
| frame_count = int(video.get(cv.CAP_PROP_FRAME_COUNT)) |
| |
| else: |
| frame_count = min(grid_count * pad * grid_size**2, int(video.get(cv.CAP_PROP_FRAME_COUNT))) |
| |
| frame_idx = 0 |
| frames = [] |
| frames_grid = [] |
|
|
| dir_path = os.path.dirname(path) |
| video_name = path.split('/')[-1].split('.')[0] |
| os.makedirs(os.path.join(dir_path, 'frames/', video_name), exist_ok=True) |
| os.makedirs(os.path.join(dir_path, 'video/', video_name), exist_ok=True) |
|
|
| for idx in range(frame_count): |
| success,image = video.read() |
| assert success, 'Video read failed' |
| if idx % pad == 0: |
| frames.append(image) |
|
|
| for frame in frames[:(len(frames)//(grid_size**2)*(grid_size**2))]: |
| frames_grid.append(frame) |
| cv.imwrite(os.path.join(dir_path, 'frames/', video_name, f'{str(frame_idx).zfill(5)}.png'), frame) |
| frame_idx += 1 |
|
|
|
|
| if format == 'gif': |
| with imageio.get_writer(os.path.join(dir_path, 'video/', f'{video_name}_fc{frame_idx}_pad{pad}_grid{grid_size}.gif'), mode='I') as writer: |
| for frame in frames_grid: |
| frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB) |
| writer.append_data(frame) |
| elif format == 'mp4': |
| image_files = sorted(glob.glob(os.path.join(dir_path, 'frames/', video_name, '*.png'))) |
| images = [imageio.imread(image_file) for image_file in image_files] |
| save_file_path = os.path.join(dir_path, 'video/', f'{video_name}_fc{frame_idx}_pad{pad}_grid{grid_size}.mp4') |
| imageio.mimsave(save_file_path, images, fps=20) |
|
|
| return frame_idx |
|
|
|
|
