import gradio as gr import cv2 from PIL import Image import numpy as np from transformers import pipeline import os import torch import torch.nn.functional as F from torchvision import transforms from torchvision.transforms import Compose import tempfile import spaces from depth_anything.dpt import DepthAnything from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet @torch.no_grad() def predict_depth(model, image): return model(image)["depth"] @spaces.GPU def make_video(video_path, outdir='./vis_video_depth',encoder='vitl'): if encoder not in ["vitl","vitb","vits"]: encoder = "vits" mapper = {"vits":"small","vitb":"base","vitl":"large"} # DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' # model = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(DEVICE).eval() # Define path for temporary processed frames temp_frame_dir = tempfile.mkdtemp() margin_width = 50 to_tensor_transform = transforms.ToTensor() DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' # depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{}14'.format(encoder)).to(DEVICE).eval() depth_anything = pipeline(task = "depth-estimation", model=f"nielsr/depth-anything-{mapper[encoder]}") # total_params = sum(param.numel() for param in depth_anything.parameters()) # print('Total parameters: {:.2f}M'.format(total_params / 1e6)) transform = Compose([ Resize( width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) if os.path.isfile(video_path): if video_path.endswith('txt'): with open(video_path, 'r') as f: lines = f.read().splitlines() else: filenames = [video_path] else: filenames = os.listdir(video_path) filenames = [os.path.join(video_path, filename) for filename in filenames if not filename.startswith('.')] filenames.sort() # os.makedirs(outdir, exist_ok=True) for k, filename in enumerate(filenames): print('Progress {:}/{:},'.format(k+1, len(filenames)), 'Processing', filename) raw_video = cv2.VideoCapture(filename) frame_width, frame_height = int(raw_video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(raw_video.get(cv2.CAP_PROP_FRAME_HEIGHT)) frame_rate = int(raw_video.get(cv2.CAP_PROP_FPS)) output_width = frame_width * 2 + margin_width filename = os.path.basename(filename) # output_path = os.path.join(outdir, filename[:filename.rfind('.')] + '_video_depth.mp4') with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmpfile: output_path = tmpfile.name #out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"avc1"), frame_rate, (output_width, frame_height)) fourcc = cv2.VideoWriter_fourcc(*'mp4v') out = cv2.VideoWriter(output_path, fourcc, frame_rate, (output_width, frame_height)) # count=0 while raw_video.isOpened(): ret, raw_frame = raw_video.read() if not ret: break frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2RGB) / 255.0 frame_pil = Image.fromarray((frame * 255).astype(np.uint8)) frame = transform({'image': frame})['image'] frame = torch.from_numpy(frame).unsqueeze(0).to(DEVICE) depth = to_tensor_transform(predict_depth(depth_anything, frame_pil)) depth = F.interpolate(depth[None], (frame_height, frame_width), mode='bilinear', align_corners=False)[0, 0] depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 depth = depth.cpu().numpy().astype(np.uint8) depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO) split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255 combined_frame = cv2.hconcat([raw_frame, split_region, depth_color]) # out.write(combined_frame) # frame_path = os.path.join(temp_frame_dir, f"frame_{count:05d}.png") # cv2.imwrite(frame_path, combined_frame) out.write(combined_frame) # count += 1 raw_video.release() out.release() return output_path css = """ #img-display-container { max-height: 100vh; } #img-display-input { max-height: 80vh; } #img-display-output { max-height: 80vh; } """ title = "# Depth Anything Video Demo" description = """Depth Anything on full video files. Please refer to our [paper](https://arxiv.org/abs/2401.10891), [project page](https://depth-anything.github.io), or [github](https://github.com/LiheYoung/Depth-Anything) for more details.""" transform = Compose([ Resize( width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) # @torch.no_grad() # def predict_depth(model, image): # return model(image) with gr.Blocks(css=css) as demo: gr.Markdown(title) gr.Markdown(description) gr.Markdown("### Video Depth Prediction demo") with gr.Row(): with gr.Column(): input_video = gr.Video(label="Input Video") submit = gr.Button("Submit") with gr.Column(): model_type = gr.Dropdown([["small", "vits"], ["base", "vitb"], ["large", "vitl"]], type="value", value="vitl", label='Model Type') processed_video = gr.Video(label="Processed Video") def on_submit(uploaded_video,model_type): # Process the video and get the path of the output video output_video_path = make_video(uploaded_video,encoder=model_type) return output_video_path submit.click(on_submit, inputs=[input_video, model_type], outputs=processed_video) example_files = os.listdir('assets/examples_video') example_files.sort() example_files = [os.path.join('assets/examples_video', filename) for filename in example_files] examples = gr.Examples(examples=example_files, inputs=[input_video], outputs=processed_video, fn=on_submit, cache_examples=True) if __name__ == '__main__': demo.queue().launch()