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| 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 | |
| from moviepy.editor import * | |
| def create_video(frames, fps, type): | |
| print("building video result") | |
| clip = ImageSequenceClip(frames, fps=fps) | |
| clip.write_videofile(type + "_result.mp4", fps=fps) | |
| return type + "_result.mp4" | |
| def predict_depth(model, image): | |
| return model(image)["depth"] | |
| def make_video(video_path, outdir='./vis_video_depth', encoder='vits'): | |
| 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)) | |
| cframes = int(raw_video.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| print(f'frames: {cframes}, fps: {frame_rate}') | |
| # 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 | |
| depth_frames = [] | |
| 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_BONE) | |
| depth_color = cv2.cvtColor(depth_color, cv2.COLOR_RGBA2GRAY) | |
| depth_color = cv2.cvtColor(depth_color, cv2.COLOR_GRAY2RGB) | |
| # Remove white border around map: | |
| # define lower and upper limits of white | |
| white_lo = np.array([250,250,250]) | |
| white_hi = np.array([255,255,255]) | |
| # mask image to only select white | |
| mask = cv2.inRange(depth_color, white_lo, white_hi) | |
| # change image to black where we found white | |
| depth_color[mask>0] = (0,0,0) | |
| # 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) | |
| cv2.imwrite(f"frame{count}.jpg", depth_color) | |
| depth_frames.append(f"frame{count}.jpg") | |
| count += 1 | |
| final_vid = create_video(depth_frames, frame_rate, "depth") | |
| raw_video.release() | |
| # out.release() | |
| cv2.destroyAllWindows() | |
| return final_vid #output_path | |
| def loadurl(url): | |
| return url | |
| 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_url = gr.Textbox(value="./examples/streetview.mp4", label="URL") | |
| input_video = gr.Video(label="Input Video", format="mp4") | |
| input_url.change(fn=loadurl, inputs=[input_url], outputs=[input_video]) | |
| submit = gr.Button("Submit") | |
| with gr.Column(): | |
| model_type = gr.Dropdown([("small", "vits"), ("base", "vitb"), ("large", "vitl")], type="value", value="vits", label='Model Type') | |
| processed_video = gr.Video(label="Processed Video", format="mp4") | |
| 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('examples') | |
| example_files.sort() | |
| example_files = [os.path.join('examples', 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() |