Cleanup unused dependencies

cogvideox_fun/utils.py

@@ -1,20 +1,10 @@
 import os
 import gc
-import imageio
 import numpy as np
 import torch
-import torchvision
-import cv2
-from einops import rearrange
 from PIL import Image
 
 # Copyright (c) OpenMMLab. All rights reserved.
-import os
-import cv2
-import numpy as np
-import torch
-from PIL import Image
-
 
 def tensor2pil(image):
     return Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
@@ -73,60 +63,6 @@ def get_width_and_height_from_image_and_base_resolution(image, base_resolution):
     height_slider = round(original_height * ratio)
     return height_slider, width_slider
 
-def color_transfer(sc, dc):
-    """
-    Transfer color distribution from of sc, referred to dc.
-
-    Args:
-        sc (numpy.ndarray): input image to be transfered.
-        dc (numpy.ndarray): reference image
-
-    Returns:
-        numpy.ndarray: Transferred color distribution on the sc.
-    """
-
-    def get_mean_and_std(img):
-        x_mean, x_std = cv2.meanStdDev(img)
-        x_mean = np.hstack(np.around(x_mean, 2))
-        x_std = np.hstack(np.around(x_std, 2))
-        return x_mean, x_std
-
-    sc = cv2.cvtColor(sc, cv2.COLOR_RGB2LAB)
-    s_mean, s_std = get_mean_and_std(sc)
-    dc = cv2.cvtColor(dc, cv2.COLOR_RGB2LAB)
-    t_mean, t_std = get_mean_and_std(dc)
-    img_n = ((sc - s_mean) * (t_std / s_std)) + t_mean
-    np.putmask(img_n, img_n > 255, 255)
-    np.putmask(img_n, img_n < 0, 0)
-    dst = cv2.cvtColor(cv2.convertScaleAbs(img_n), cv2.COLOR_LAB2RGB)
-    return dst
-
-def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=12, imageio_backend=True, color_transfer_post_process=False):
-    videos = rearrange(videos, "b c t h w -> t b c h w")
-    outputs = []
-    for x in videos:
-        x = torchvision.utils.make_grid(x, nrow=n_rows)
-        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
-        if rescale:
-            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
-        x = (x * 255).numpy().astype(np.uint8)
-        outputs.append(Image.fromarray(x))
-
-    if color_transfer_post_process:
-        for i in range(1, len(outputs)):
-            outputs[i] = Image.fromarray(color_transfer(np.uint8(outputs[i]), np.uint8(outputs[0])))
-
-    os.makedirs(os.path.dirname(path), exist_ok=True)
-    if imageio_backend:
-        if path.endswith("mp4"):
-            imageio.mimsave(path, outputs, fps=fps)
-        else:
-            imageio.mimsave(path, outputs, duration=(1000 * 1/fps))
-    else:
-        if path.endswith("mp4"):
-            path = path.replace('.mp4', '.gif')
-        outputs[0].save(path, format='GIF', append_images=outputs, save_all=True, duration=100, loop=0)
-
 def get_image_to_video_latent(validation_image_start, validation_image_end, video_length, sample_size):
     if validation_image_start is not None and validation_image_end is not None:
         if type(validation_image_start) is str and os.path.isfile(validation_image_start):
@@ -224,18 +160,7 @@ def get_image_to_video_latent(validation_image_start, validation_image_end, vide
     return  input_video, input_video_mask, clip_image
 
 def get_video_to_video_latent(input_video_path, video_length, sample_size):
-    if type(input_video_path) is str:
+    input_video = input_video_path
-        cap = cv2.VideoCapture(input_video_path)
-        input_video = []
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            frame = cv2.resize(frame, (sample_size[1], sample_size[0]))
-            input_video.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-        cap.release()
-    else:
-        input_video = input_video_path
 
     input_video = torch.from_numpy(np.array(input_video))[:video_length]
     input_video = input_video.permute([3, 0, 1, 2]).unsqueeze(0) / 255