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 trimesh from geometry import create_triangles import tempfile from functools import partial import spaces from zipfile import ZipFile from depth_anything.dpt import DepthAnything from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet from moviepy.editor import * def zip_files(files_in, files_out): with ZipFile("depth_result.zip", "w") as zipObj: for idx, file in enumerate(files_in): zipObj.write(file, file.split("/")[-1]) for idx, file in enumerate(files_out): zipObj.write(file, file.split("/")[-1]) return "depth_result.zip" 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" @torch.no_grad() def predict_depth(model, image): return model(image)["depth"] @spaces.GPU 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): file_size = os.path.getsize(filename)/1024/1024 if file_size > 128.0: print(f'File size of {filename} larger than 128Mb, sorry!') return filename 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)) if frame_rate < 1: frame_rate = 1 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 = [] orig_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_gray = cv2.cvtColor(depth_color, cv2.COLOR_RGBA2GRAY) depth_color = cv2.cvtColor(depth_gray, 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"f{count}.jpg", raw_frame) orig_frames.append(f"f{count}.jpg") cv2.imwrite(f"f{count}_dmap.jpg", depth_color) depth_frames.append(f"f{count}_dmap.jpg") count += 1 final_vid = create_video(depth_frames, frame_rate, "depth") final_zip = zip_files(orig_frames, depth_frames) raw_video.release() # out.release() cv2.destroyAllWindows() return final_vid, final_zip, orig_frames[0], depth_frames[0] #output_path def depth_edges_mask(depth): """Returns a mask of edges in the depth map. Args: depth: 2D numpy array of shape (H, W) with dtype float32. Returns: mask: 2D numpy array of shape (H, W) with dtype bool. """ # Compute the x and y gradients of the depth map. depth_dx, depth_dy = np.gradient(depth) # Compute the gradient magnitude. depth_grad = np.sqrt(depth_dx ** 2 + depth_dy ** 2) # Compute the edge mask. mask = depth_grad > 0.05 return mask def pano_depth_to_world_points(depth): """ 360 depth to world points given 2D depth is an equirectangular projection of a spherical image Treat depth as radius longitude : -pi to pi latitude : -pi/2 to pi/2 """ # Convert depth to radius radius = 255 - depth.flatten() lon = np.linspace(0, np.pi*2, depth.shape[1]) lat = np.linspace(0, np.pi, depth.shape[0]) lon, lat = np.meshgrid(lon, lat) lon = lon.flatten() lat = lat.flatten() # Convert to cartesian coordinates x = radius * np.cos(lon) * np.sin(lat) y = radius * np.cos(lat) z = radius * np.sin(lon) * np.sin(lat) pts3d = np.stack([x, y, z], axis=1) uv = np.stack([lon, lat], axis=1) return [pts3d, uv] def rgb2gray(rgb): return np.dot(rgb[...,:3], [0.333, 0.333, 0.333]) def get_mesh(image, depth): gdepth = rgb2gray(depth) print('depth to gray - ok') points = pano_depth_to_world_points(gdepth) pts3d = points[0] uv = points[1] print('radius from depth - ok') # Create a trimesh mesh from the points # Each pixel is connected to its 4 neighbors # colors are the RGB values of the image verts = pts3d.reshape(-1, 3) triangles = create_triangles(image.shape[0], image.shape[1]) print('triangles - ok') colors = image.reshape(-1, 3) #mesh = trimesh.Trimesh(vertices=verts, faces=triangles, vertex_colors=colors) mesh = trimesh.PointCloud(verts, colors=colors) #material = trimesh.visual.texture.SimpleMaterial(image=image) #texture = trimesh.visual.TextureVisuals(uv=uv, image=image, material=material) #mesh.visual = texture scene = trimesh.Scene([mesh]) print('mesh - ok') # Save as glb glb_file = tempfile.NamedTemporaryFile(suffix='.glb', delete=False) glb_path = glb_file.name scene.export(glb_path) print('file - ok') return glb_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. Mesh rendering from [ZoeDepth](https://huggingface.co/spaces/shariqfarooq/ZoeDepth) ([github](https://github.com/isl-org/ZoeDepth/tree/main/ui)).""" 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") output_frame = gr.Image(label="Frame", type='numpy') output_depth = gr.Image(label="Depth", type='numpy') render = gr.Button("Render") 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="Output Video", format="mp4") processed_zip = gr.File(label="Output Archive") result = gr.Model3D(label="3D Mesh", clear_color=[0.0, 0.0, 0.0, 0.0], camera_position=[0, 90, 0]) 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, processed_zip, output_frame, output_depth]) render.click(partial(get_mesh), inputs=[output_frame, output_depth], outputs=[result]) 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, processed_zip, output_frame, output_depth], fn=on_submit, cache_examples=True) if __name__ == '__main__': demo.queue().launch()