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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import time
import cv2
import imageio
import numpy as np
import pycocotools.mask
import torch
from natsort import natsorted
from PIL import Image
from torchvision import transforms
from cosmos_transfer1.diffusion.datasets.augmentors.control_input import (
decode_partial_rle_width1,
segmentation_color_mask,
)
from cosmos_transfer1.utils import log
def write_video(frames, output_path, fps=30):
"""
expects a sequence of [H, W, 3] or [H, W] frames
"""
with imageio.get_writer(output_path, fps=fps, macro_block_size=8) as writer:
for frame in frames:
if len(frame.shape) == 2: # single channel
frame = frame[:, :, None].repeat(3, axis=2)
writer.append_data(frame)
def capture_fps(input_video_path: str):
cap = cv2.VideoCapture(input_video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
return fps
def video_to_frames(input_loc, output_loc):
"""Function to extract frames from input video file
and save them as separate frames in an output directory.
Args:
input_loc: Input video file.
output_loc: Output directory to save the frames.
Returns:
None
"""
try:
os.mkdir(output_loc)
except OSError:
pass
# Log the time
time_start = time.time()
# Start capturing the feed
cap = cv2.VideoCapture(input_loc)
# Find the number of frames
video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print(f"Number of frames: {video_length}")
count = 0
print("Converting video..\n")
# Start converting the video
while cap.isOpened():
# Extract the frame
ret, frame = cap.read()
if not ret:
continue
# Write the results back to output location.
cv2.imwrite(output_loc + "/%#05d.jpg" % (count + 1), frame)
count = count + 1
# If there are no more frames left
if count > (video_length - 1):
# Log the time again
time_end = time.time()
# Release the feed
cap.release()
# Print stats
print("Done extracting frames.\n%d frames extracted" % count)
print("It took %d seconds forconversion." % (time_end - time_start))
break
# Function to generate video
def convert_masks_to_frames(masks: list, num_masks_max: int = 100):
T, H, W = shape = masks[0]["segmentation_mask_rle"]["mask_shape"]
frame_start, frame_end = 0, T
num_masks = min(num_masks_max, len(masks))
mask_ids_select = np.arange(num_masks).tolist()
all_masks = np.zeros((num_masks, T, H, W), dtype=np.uint8)
for idx, mid in enumerate(mask_ids_select):
mask = masks[mid]
num_byte_per_mb = 1024 * 1024
# total number of elements in uint8 (1 byte) / num_byte_per_mb
if shape[0] * shape[1] * shape[2] / num_byte_per_mb > 256:
rle = decode_partial_rle_width1(
mask["segmentation_mask_rle"]["data"],
frame_start * shape[1] * shape[2],
frame_end * shape[1] * shape[2],
)
partial_shape = (frame_end - frame_start, shape[1], shape[2])
rle = rle.reshape(partial_shape) * 255
else:
rle = pycocotools.mask.decode(mask["segmentation_mask_rle"]["data"])
rle = rle.reshape(shape) * 255
# Select the frames that are in the video
frame_indices = np.arange(frame_start, frame_end).tolist()
rle = np.stack([rle[i] for i in frame_indices])
all_masks[idx] = rle
del rle
all_masks = segmentation_color_mask(all_masks) # NTHW -> 3THW
all_masks = all_masks.transpose(1, 2, 3, 0)
return all_masks
def generate_video_from_images(masks: list, output_file_path: str, fps, num_masks_max: int = 100):
all_masks = convert_masks_to_frames(masks, num_masks_max)
write_video(all_masks, output_file_path, fps)
print("Video generated successfully!")
def generate_tensor_from_images(
image_path_str: str, output_file_path: str, fps, search_pattern: str = None, weight_scaler: float = None
):
images = list()
image_path = os.path.abspath(image_path_str)
if search_pattern is None:
images = [img for img in natsorted(os.listdir(image_path))]
else:
for img in natsorted(os.listdir(image_path)):
if img.__contains__(search_pattern):
images.append(img)
transform = transforms.ToTensor()
image_tensors = list()
for image in images:
img_tensor = transform(Image.open(os.path.join(image_path, image)))
image_tensors.append(img_tensor.squeeze(0))
tensor = torch.stack(image_tensors) # [T, H, W], binary values, float
if weight_scaler is not None:
log.info(f"scaling the tensor by the specified scale: {weight_scaler}")
tensor = tensor * weight_scaler
log.info(f"saving tensor shape: {tensor.shape} to {output_file_path}")
torch.save(tensor, output_file_path)
if __name__ == "__main__":
input_loc = "cosmos_transfer1/models/sam2/assets/input_video.mp4"
output_loc = os.path.abspath(tempfile.TemporaryDirectory().name)
print(f"output_loc --- {output_loc}")
video_to_frames(input_loc, output_loc)
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