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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 trimesh | |
| from geometry import create_triangles | |
| import tempfile | |
| from functools import partial | |
| import spaces | |
| from zipfile import ZipFile | |
| import json | |
| from depth_anything.dpt import DepthAnything | |
| from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet | |
| from moviepy.editor import * | |
| frame_selected = 0 | |
| frames = [] | |
| depths = [] | |
| masks = [] | |
| locations = [] | |
| mesh = [] | |
| 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" | |
| 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): | |
| 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)) | |
| global masks | |
| 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_GRAY2BGR) | |
| # 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") | |
| masks.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() | |
| global frame_selected | |
| global depths | |
| global frames | |
| frames = orig_frames | |
| depths = depth_frames | |
| return final_vid, final_zip, frames, masks[frame_selected] #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() | |
| pts3d = [[0,0,0]] | |
| uv = [[0,0]] | |
| for i in range(0, 1): #(0,2) | |
| for j in range(0, 1): #(0,2) | |
| #rnd_lon = (np.random.rand(depth.shape[0]*depth.shape[1]) - 0.5) / 8 | |
| #rnd_lat = (np.random.rand(depth.shape[0]*depth.shape[1]) - 0.5) / 8 | |
| d_lon = lon + i/2 * np.pi*2 / depth.shape[1] | |
| d_lat = lat + j/2 * np.pi / depth.shape[0] | |
| # Convert to cartesian coordinates | |
| x = radius * np.cos(d_lon) * np.sin(d_lat) | |
| y = radius * np.cos(d_lat) | |
| z = radius * np.sin(d_lon) * np.sin(d_lat) | |
| pts = np.stack([x, y, z], axis=1) | |
| uvs = np.stack([lon, lat], axis=1) | |
| pts3d = np.concatenate((pts3d, pts), axis=0) | |
| uv = np.concatenate((uv, uvs), axis=0) | |
| #print(f'i: {i}, j: {j}') | |
| j = j+1 | |
| i = i+1 | |
| return [pts3d, uv] | |
| def rgb2gray(rgb): | |
| return np.dot(rgb[...,:3], [0.333, 0.333, 0.333]) | |
| def get_mesh(image, depth, blur_data, loadall): | |
| global locations | |
| global mesh | |
| if loadall == False: | |
| mesh = [] | |
| fnum = frame_selected | |
| #print(image[fnum][0]) | |
| #print(depth["composite"]) | |
| depthc = cv2.cvtColor(depth["composite"], cv2.COLOR_RGBA2RGB) | |
| blur_img = blur_image(image[fnum][0], depthc, blur_data) | |
| gdepth = rgb2gray(depthc) | |
| 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') | |
| rgba = cv2.cvtColor(blur_img, cv2.COLOR_RGB2RGBA) | |
| colors = rgba.reshape(-1, 4) | |
| clrs = [[128, 128, 128, 0]] | |
| for i in range(0,1): #(0,4) | |
| clrs = np.concatenate((clrs, colors), axis=0) | |
| i = i+1 | |
| #mesh = trimesh.Trimesh(vertices=verts, faces=triangles, vertex_colors=colors) | |
| mesh.append(trimesh.PointCloud(verts, colors=clrs)) | |
| #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 blur_image(image, depth, blur_data): | |
| blur_a = blur_data.split() | |
| print(f'blur data {blur_data}') | |
| blur_frame = image.copy() | |
| j = 0 | |
| while j < 256: | |
| i = 255 - j | |
| blur_lo = np.array([i,i,i]) | |
| blur_hi = np.array([i+1,i+1,i+1]) | |
| blur_mask = cv2.inRange(depth, blur_lo, blur_hi) | |
| print(f'kernel size {int(blur_a[j])}') | |
| blur = cv2.GaussianBlur(image, (int(blur_a[j]), int(blur_a[j])), 0) | |
| blur_frame[blur_mask>0] = blur[blur_mask>0] | |
| j = j + 1 | |
| return blur_frame | |
| def loadurl(url): | |
| return url | |
| def select_frame(v, evt: gr.SelectData): | |
| global frame_selected | |
| global masks | |
| if evt.index != frame_selected: | |
| masks[frame_selected] = v | |
| frame_selected = evt.index | |
| return masks[frame_selected], frame_selected | |
| def switch_rows(v): | |
| global frames | |
| global depths | |
| if v == True: | |
| print(depths[0]) | |
| return depths | |
| else: | |
| print(frames[0]) | |
| return frames | |
| def draw_mask(ksize, th, v, d, evt: gr.EventData): | |
| print(v) | |
| vv = json.loads(v) | |
| pts = np.array(vv, np.int32) | |
| pts = pts.reshape((-1,1,2)) | |
| d["layers"][0] = cv2.fillPoly(d["layers"][0], [pts], (0,0,0,255)) | |
| d["composite"] = cv2.fillPoly(d["composite"], [pts], (0,0,0,255)) | |
| img = cv2.cvtColor(d["background"], cv2.COLOR_RGB2GRAY) | |
| rows, cols = img.shape | |
| crow, ccol = rows//2, cols//2 | |
| f = np.fft.fft2(img) | |
| fshift = np.fft.fftshift(f) | |
| fshift[crow-int(ksize/2):crow+int(ksize/2+1), ccol-int(ksize/2):ccol+int(ksize/2+1)] = 0 | |
| f_ishift = np.fft.ifftshift(fshift) | |
| img_back = np.fft.ifft2(f_ishift) | |
| img_back = np.real(img_back).astype(np.uint8) | |
| #scale = 1 | |
| #delta = 0 | |
| #ddepth = cv2.CV_16S | |
| #gray = cv2.cvtColor(d["background"], cv2.COLOR_BGR2GRAY) | |
| #grad_x = cv2.Sobel(gray, ddepth, 1, 0, ksize=ksize, scale=scale, delta=delta, borderType=cv2.BORDER_DEFAULT) | |
| # Gradient-Y | |
| # grad_y = cv2.Scharr(gray,ddepth,0,1) | |
| #grad_y = cv2.Sobel(gray, ddepth, 0, 1, ksize=ksize, scale=scale, delta=delta, borderType=cv2.BORDER_DEFAULT) | |
| #abs_grad_x = cv2.convertScaleAbs(grad_x) | |
| #abs_grad_y = cv2.convertScaleAbs(grad_y) | |
| #grad = cv2.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0) | |
| #bw = cv2.cvtColor(cv2.threshold(255-img_back, th, 255, cv2.THRESH_BINARY)[1], cv2.COLOR_GRAY2RGB) | |
| bw = cv2.cvtColor(255-img_back, cv2.COLOR_GRAY2RGB) | |
| d["background"] = bw | |
| d["composite"] = bw | |
| print(d["composite"]) | |
| return gr.ImageEditor(value=d) | |
| 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]) | |
| output_frame = gr.Gallery(label="Frames", preview=True, columns=8192, type="numpy") | |
| output_switch = gr.Checkbox(label="Show depths") | |
| output_switch.input(fn=switch_rows, inputs=[output_switch], outputs=[output_frame]) | |
| output_mask = gr.ImageEditor(layers=True, sources=None, type="numpy", interactive=True, transforms=(None,), eraser=gr.Eraser(), brush=gr.Brush(default_size=0, colors=['black', '#505050', '#a0a0a0', 'white']), elem_id="image_edit") | |
| ksize = gr.Slider(label="Kernel", value=128, minimum=0, maximum=1024, step=1) | |
| threshold = gr.Slider(label="Threshold", value=128, minimum=0, maximum=255, step=1) | |
| selector = gr.HTML(value="""<div id='select_cursor' style='width:1px;height:1px;position:absolute;border:3px dotted #ffffff;'></div> | |
| <a href='#' id='selector' onclick='this.style.fontWeight=\"bold\"; | |
| document.body.appendChild(document.getElementById(\"select_cursor\")); | |
| document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].oncontextmenu = function(e){e.preventDefault();} | |
| document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].ondrag = function(e){e.preventDefault();} | |
| document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].onclick = function(e) { | |
| var x = parseInt((e.clientX-e.target.getBoundingClientRect().x)*e.target.width/e.target.getBoundingClientRect().width); | |
| var y = parseInt((e.clientY-e.target.getBoundingClientRect().y)*e.target.height/e.target.getBoundingClientRect().height); | |
| var p = document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].value.slice(1, -1); | |
| if (p != \"\") { p += \", \"; } | |
| p += \"[\" + x + \", \" + y + \"]\"; | |
| document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].value = \"[\" + p + \"]\"; | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"input\", true, false); | |
| document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].dispatchEvent(evt); | |
| document.getElementById(\"select_cursor\").style.left = e.clientX-1 + \"px\"; | |
| document.getElementById(\"select_cursor\").style.top = e.clientY-1 + \"px\"; | |
| } | |
| document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].onpointermove = function(e) { | |
| if (document.getElementById(\"select_cursor\").style.borderColor != \"#ffffff\") { | |
| var x = parseInt((e.clientX-e.target.getBoundingClientRect().x)*e.target.width/e.target.getBoundingClientRect().width); | |
| var y = parseInt((e.clientY-e.target.getBoundingClientRect().y)*e.target.height/e.target.getBoundingClientRect().height); | |
| var p = document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].value.slice(1, -1); | |
| if (p == \"\") { | |
| p = \"[\" + x + \", \" + y + \"]\"; | |
| } else { | |
| p = p.substr(0, p.lastIndexOf(\"[\")); | |
| p += \"[\" + x + \", \" + y + \"]\"; | |
| } | |
| document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].value = \"[\" + p + \"]\"; | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"input\", true, false); | |
| document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].dispatchEvent(evt); | |
| document.getElementById(\"select_cursor\").style.left = e.clientX-1 + \"px\"; | |
| document.getElementById(\"select_cursor\").style.top = e.clientY-1 + \"px\"; | |
| } | |
| } | |
| document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].onblur = function(e) { | |
| document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].value = \"[]\"; | |
| document.getElementById(\"select_cursor\").style.left = 0; | |
| document.getElementById(\"select_cursor\").style.top = 0; | |
| } | |
| document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].onpointerdown = function(e) { | |
| document.getElementById(\"select_cursor\").style.borderColor = \"#808080\"; | |
| } | |
| document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].onpointerup = function(e) { | |
| document.getElementById(\"select_cursor\").style.borderColor = \"#ffffff\"; | |
| }' title='select' style='text-decoration:none;color:white;'>⬚ Select polygon</a>""") | |
| mouse = gr.Textbox(elem_id="mouse", value="""[]""", interactive=False) | |
| output_mask.apply(fn=draw_mask, inputs=[ksize, threshold, mouse, output_mask], outputs=[output_mask]) | |
| 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="Output Video", format="mp4") | |
| processed_zip = gr.File(label="Output Archive") | |
| result = gr.Model3D(label="3D Mesh", clear_color=[0.5, 0.5, 0.5, 0.0], camera_position=[0, 90, 0], interactive=True, elem_id="model3D") | |
| svg_in = gr.HTML(value="""<svg id='svg_in' height='32' width='256' viewBox='0 0 256 32' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' style='touch-action:none;background-color:#808080;' onpointerdown=' | |
| try{ | |
| if (document.getElementById(\"pl\").getAttribute(\"points\").length < 256) { | |
| var pts = \"\"; | |
| for (var i=0; i<256; i++) { | |
| pts += i+\",0 \"; | |
| } | |
| document.getElementById(\"pl\").setAttribute(\"points\", pts.slice(0,-1)); | |
| var xold = 0; | |
| var yold = 0; | |
| var x = 0; | |
| var y = 0; | |
| function lerp(y1, y2, mu) { return y1*(1-mu)+y2*mu; } | |
| this.onpointermove = function(event) { | |
| if (this.title != \"\") { | |
| x = parseInt(event.clientX - this.getBoundingClientRect().x); | |
| y = parseInt(event.clientY - this.getBoundingClientRect().y); | |
| if (x < 0) { x = 0; } else if (x > 255) { x = 255; } | |
| if (y < 0) { y = 0; } else if (y > 31) { y = 31; } | |
| var pl_a = document.getElementById(\"pl\").getAttribute(\"points\").split(\" \"); | |
| for (var i=Math.min(xold, x)+1; i<Math.max(xold, x); i++) { | |
| pl_a[i] = x+\",\"+parseInt(lerp( yold, y, (i-xold)/(x-xold) )); | |
| } | |
| pl_a[x] = x+\",\"+y; | |
| xold = x; | |
| yold = y; | |
| document.getElementById(\"pl\").setAttribute(\"points\", pl_a.join(\" \")); | |
| } | |
| } | |
| this.onpointerup = function(event) { | |
| var pl_a = document.getElementById(\"pl\").getAttribute(\"points\").replace(/\d+,/g, \"\").split(\" \"); | |
| for (var i=0; i<pl_a.length; i++) { | |
| pl_a[i] = parseInt(pl_a[i]) * 2 + 1; | |
| } | |
| document.getElementsByTagName(\"textarea\")[2].value = pl_a.join(\" \"); | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"input\", true, false); | |
| document.getElementsByTagName(\"textarea\")[2].dispatchEvent(evt); | |
| this.title = \"\"; | |
| } | |
| this.onpointerleave = function(event) { | |
| this.title = \"\"; | |
| } | |
| this.onpointerdown = function(event) { | |
| xold = parseInt(event.clientX - this.getBoundingClientRect().x); | |
| yold = parseInt(event.clientY - this.getBoundingClientRect().y); | |
| this.title = xold+\",\"+yold; | |
| } | |
| } | |
| }catch(e){alert(e);} | |
| '> | |
| <defs> | |
| <linearGradient id='lg' x1='0%' x2='100%' y1='0%' y2='0%'> | |
| <stop offset='0%' stop-color='white'/> | |
| <stop offset='100%' stop-color='black'/> | |
| </linearGradient> | |
| </defs> | |
| <polyline id='pl' points='-3,0 0,15 255,15 258,0' stroke='url(#lg)' fill='none' stroke-width='3' stroke-linejoin='round'/> | |
| </svg>""") | |
| average = gr.HTML(value="""<label for='average'>Average</label><input id='average' type='range' style='width:256px;height:1em;' value='1' min='1' max='15' step='2' onclick=' | |
| var pts_a = document.getElementsByTagName(\"textarea\")[1].value.split(\" \"); | |
| for (var i=0; i<256; i++) { | |
| var avg = 0; | |
| var div = this.value; | |
| for (var j = i-parseInt(this.value/2); j <= i+parseInt(this.value/2); j++) { | |
| if (pts_a[j]) { | |
| avg += parseInt(pts_a[j]); | |
| } else { | |
| div--; | |
| } | |
| } | |
| pts_a[i] = parseInt((avg / div - 1) / 2) * 2 + 1; | |
| } | |
| document.getElementsByTagName(\"textarea\")[1].value = pts_a.join(\" \"); | |
| for (var i=0; i<pts_a.length; i++) { | |
| pts_a[i] = i+\",\"+parseInt((pts_a[i] - 1) / 2); | |
| } | |
| document.getElementById(\"pl\").setAttribute(\"points\", pts_a.join(\" \")); | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"input\", true, false); | |
| document.getElementsByTagName(\"textarea\")[1].dispatchEvent(evt); | |
| ' oninput=' | |
| this.parentNode.childNodes[2].innerText = this.value; | |
| '/><span>1</span>""") | |
| with gr.Accordion(label="Blur levels", open=False): | |
| blur_in = gr.Textbox(value="", label="Kernel size", show_label=False) | |
| with gr.Accordion(label="Locations", open=False): | |
| offset = gr.HTML(value="""<input type='text' id='kbrd' onkeydown=' | |
| if (BABYLON) { | |
| if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"click\", true, false); | |
| document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
| } | |
| event.preventDefault(); | |
| if (BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotationQuaternion) { | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotationQuaternion = null; | |
| } | |
| switch(event.key) { | |
| case \"w\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.y += 1; | |
| this.value = \"w ⬆ x\"; | |
| break; | |
| case \"x\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.y -= 1; | |
| this.value = \"w ⬇ x\"; | |
| break; | |
| case \"a\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z -= 1; | |
| this.value = \"a ⬅ d\"; | |
| break; | |
| case \"d\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z += 1; | |
| this.value = \"a ➡ d\"; | |
| break; | |
| case \"e\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x -= 1; | |
| this.value = \"z ↗ e\"; | |
| break; | |
| case \"z\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x += 1; | |
| this.value = \"z ↙ e\"; | |
| break; | |
| case \"s\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x = 0; | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.y = 0; | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z = 0; | |
| this.value = \"\"; | |
| break; | |
| case \"t\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.z += Math.PI/256; | |
| this.value = \"t 🔃 b\"; | |
| break; | |
| case \"b\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.z -= Math.PI/256; | |
| this.value = \"t 🔃 b\"; | |
| break; | |
| case \"f\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.y -= Math.PI/256; | |
| this.value = \"f 🔁 h\"; | |
| break; | |
| case \"h\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.y += Math.PI/256; | |
| this.value = \"f 🔁 h\"; | |
| break; | |
| case \"y\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.x -= Math.PI/256; | |
| this.value = \"v 🔄 y\"; | |
| break; | |
| case \"v\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.x += Math.PI/256; | |
| this.value = \"v 🔄 y\"; | |
| break; | |
| case \"g\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.x = 0; | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.y = 0; | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.z = 0; | |
| this.value = \"\"; | |
| break; | |
| case \"i\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.y *= 256/255; | |
| this.value = \"i ↕ ,\"; | |
| break; | |
| case \",\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.y /= 256/255; | |
| this.value = \"i ↕ ,\"; | |
| break; | |
| case \"j\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.z /= 256/255; | |
| this.value = \"j ↔ l\"; | |
| break; | |
| case \"l\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.z *= 256/255; | |
| this.value = \"j ↔ l\"; | |
| break; | |
| case \"o\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.x /= 256/255; | |
| this.value = \"m ⤢ o\"; | |
| break; | |
| case \"m\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.x *= 256/255; | |
| this.value = \"m ⤢ o\"; | |
| break; | |
| case \"k\": | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.x = 1; | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.y = 1; | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.z = 1; | |
| this.value = \"\"; | |
| break; | |
| default: | |
| this.value = \"\"; | |
| } | |
| } | |
| ' style='color:auto;background-color:transparent;border:1px solid lightgray;'/><pre id='keymap'> | |
| ` 1 2 3 4 5 6 7 8 9 0 - = | |
| W E T Y I O { } | |
| A-`S´-D F-`G´-H J-`K´-L ; ' | |
| Z´ X̀ V´ B̀ M´ `, . / | |
| <a id='move' href='#'>move</a> <a id='rotate' href='#'>rotate</a> <a id='scale' href='#'>scale</a> | |
| </pre>""") | |
| selected = gr.Number(elem_id="fnum", value=0, minimum=0, maximum=256, interactive=False) | |
| output_frame.select(fn=select_frame, inputs=[output_mask], outputs=[output_mask, selected]) | |
| example_coords = """[ | |
| {"latLng": { "lat": 50.07379596793083, "lng": 14.437146122950555 } }, | |
| {"latLng": { "lat": 50.073799567020004, "lng": 14.437146774240507 } }, | |
| {"latLng": { "lat": 50.07377647505558, "lng": 14.437161000659017 } }, | |
| {"latLng": { "lat": 50.07379496839027, "lng": 14.437148958238538 } }, | |
| {"latLng": { "lat": 50.073823157821664, "lng": 14.437124189538856 } } | |
| ]""" | |
| coords = gr.JSON(elem_id="coords", value=example_coords, label="Precise coordinates", show_label=False) | |
| html = gr.HTML(value="""<label for='zoom'>Zoom</label><input id='zoom' type='range' style='width:256px;height:1em;' value='0.8' min='0.157' max='1.57' step='0.001' oninput=' | |
| if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"click\", true, false); | |
| document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
| } | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].material.pointSize = Math.ceil(Math.log2(Math.PI/this.value)); | |
| BABYLON.Engine.LastCreatedScene.activeCamera.fov = this.value; | |
| this.parentNode.childNodes[2].innerText = BABYLON.Engine.LastCreatedScene.activeCamera.fov; | |
| document.getElementById(\"model3D\").getElementsByTagName(\"canvas\")[0].style.filter = \"blur(\" + BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].material.pointSize/2.0*Math.sqrt(2.0) + \"px)\"; | |
| '/><span>0.8</span>""") | |
| camera = gr.HTML(value="""<a href='#' id='reset_cam' onclick=' | |
| if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata = { | |
| screenshot: true, | |
| pipeline: new BABYLON.DefaultRenderingPipeline(\"default\", true, BABYLON.Engine.LastCreatedScene, [BABYLON.Engine.LastCreatedScene.activeCamera]) | |
| } | |
| } | |
| BABYLON.Engine.LastCreatedScene.activeCamera.radius = 0; | |
| BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].material.pointSize = Math.ceil(Math.log2(Math.PI/document.getElementById(\"zoom\").value)); | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.samples = 4; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.fov = document.getElementById(\"zoom\").value; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.contrast = document.getElementById(\"contrast\").value; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure = document.getElementById(\"exposure\").value; | |
| document.getElementById(\"model3D\").getElementsByTagName(\"canvas\")[0].style.filter = \"blur(\" + BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].material.pointSize/2.0*Math.sqrt(2.0) + \"px)\"; | |
| document.getElementById(\"model3D\").getElementsByTagName(\"canvas\")[0].oncontextmenu = function(e){e.preventDefault();} | |
| document.getElementById(\"model3D\").getElementsByTagName(\"canvas\")[0].ondrag = function(e){e.preventDefault();} | |
| if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager) { | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager = new BABYLON.GizmoManager(BABYLON.Engine.LastCreatedScene, 16); | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.positionGizmoEnabled = true; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.rotationGizmoEnabled = false; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.scaleGizmoEnabled = false; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.boundingBoxGizmoEnabled = false; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.usePointerToAttachGizmos = false; | |
| document.getElementById(\"move\").onclick = function(event) { | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.positionGizmoEnabled = true; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.rotationGizmoEnabled = false; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.scaleGizmoEnabled = false; | |
| } | |
| document.getElementById(\"rotate\").onclick = function(event) { | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.positionGizmoEnabled = false; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.rotationGizmoEnabled = true; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.scaleGizmoEnabled = false; | |
| } | |
| document.getElementById(\"scale\").onclick = function(event) { | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.positionGizmoEnabled = false; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.rotationGizmoEnabled = false; | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.scaleGizmoEnabled = true; | |
| } | |
| } | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.attachToMesh(BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1]); | |
| '>reset camera</a>""") | |
| contrast = gr.HTML(value="""<label for='contrast'>Contrast</label><input id='contrast' type='range' style='width:256px;height:1em;' value='2.0' min='0' max='2' step='0.001' oninput=' | |
| if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"click\", true, false); | |
| document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
| } | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.contrast = this.value; | |
| this.parentNode.childNodes[2].innerText = BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.contrast; | |
| '/><span>2.0</span>""") | |
| exposure = gr.HTML(value="""<label for='exposure'>Exposure</label><input id='exposure' type='range' style='width:256px;height:1em;' value='0.5' min='0' max='2' step='0.001' oninput=' | |
| if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"click\", true, false); | |
| document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
| } | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure = this.value; | |
| this.parentNode.childNodes[2].innerText = BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure; | |
| '/><span>0.5</span>""") | |
| canvas = gr.HTML(value="""<a href='#' onclick=' | |
| if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
| var evt = document.createEvent(\"Event\"); | |
| evt.initEvent(\"click\", true, false); | |
| document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
| } | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.screenshot = true; | |
| BABYLON.Engine.LastCreatedScene.getEngine().onEndFrameObservable.add(function() { | |
| if (BABYLON.Engine.LastCreatedScene.activeCamera.metadata.screenshot === true) { | |
| BABYLON.Engine.LastCreatedScene.activeCamera.metadata.screenshot = false; | |
| try { | |
| BABYLON.Tools.CreateScreenshotUsingRenderTarget(BABYLON.Engine.LastCreatedScene.getEngine(), BABYLON.Engine.LastCreatedScene.activeCamera, | |
| { precision: 1.0 }, (durl) => { | |
| var cnvs = document.getElementById(\"model3D\").getElementsByTagName(\"canvas\")[0]; //.getContext(\"webgl2\"); | |
| var svgd = `<svg id=\"svg_out\" viewBox=\"0 0 ` + cnvs.width + ` ` + cnvs.height + `\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> | |
| <defs> | |
| <filter id=\"blur\" x=\"0\" y=\"0\" xmlns=\"http://www.w3.org/2000/svg\"> | |
| <feGaussianBlur in=\"SourceGraphic\" stdDeviation=\"` + BABYLON.Engine.LastCreatedScene.getNodes()[1].material.pointSize/2.0*Math.sqrt(2.0) + `\" /> | |
| </filter> | |
| </defs> | |
| <image filter=\"url(#blur)\" id=\"svg_img\" x=\"0\" y=\"0\" width=\"` + cnvs.width + `\" height=\"` + cnvs.height + `\" xlink:href=\"` + durl + `\"/> | |
| </svg>`; | |
| document.getElementById(\"cnv_out\").width = cnvs.width; | |
| document.getElementById(\"cnv_out\").height = cnvs.height; | |
| document.getElementById(\"img_out\").src = \"data:image/svg+xml;base64,\" + btoa(svgd); | |
| } | |
| ); | |
| } catch(e) { alert(e); } | |
| // https://forum.babylonjs.com/t/best-way-to-save-to-jpeg-snapshots-of-scene/17663/11 | |
| } | |
| }); | |
| '/>snapshot</a><br/><img src='' id='img_out' onload=' | |
| var ctxt = document.getElementById(\"cnv_out\").getContext(\"2d\"); | |
| ctxt.drawImage(this, 0, 0); | |
| '/><br/> | |
| <canvas id='cnv_out'/>""") | |
| load_all = gr.Checkbox(label="Load all") | |
| render = gr.Button("Render") | |
| def on_submit(uploaded_video,model_type,coordinates): | |
| global locations | |
| locations = [] | |
| avg = [0, 0] | |
| if not coordinates: | |
| locations = json.loads(example_coords) | |
| for k, location in enumerate(locations): | |
| locations[k] = location["latLng"] | |
| avg[0] = avg[0] + locations[k]["lat"] | |
| avg[1] = avg[1] + locations[k]["lng"] | |
| else: | |
| locations = json.loads(coordinates) | |
| for k, location in enumerate(locations): | |
| locations[k] = location["location"]["latLng"] | |
| avg[0] = avg[0] + locations[k]["lat"] | |
| avg[1] = avg[1] + locations[k]["lng"] | |
| avg[0] = avg[0] / len(locations) | |
| avg[1] = avg[1] / len(locations) | |
| for k, location in enumerate(locations): | |
| locations[k]["lat"] = location["lat"] - avg[0] | |
| locations[k]["lng"] = location["lng"] - avg[1] | |
| print(locations) | |
| # 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 + (locations,) | |
| submit.click(on_submit, inputs=[input_video, model_type, coords], outputs=[processed_video, processed_zip, output_frame, output_mask, coords]) | |
| render.click(partial(get_mesh), inputs=[output_frame, output_mask, blur_in, load_all], 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_mask, coords], fn=on_submit, cache_examples=True) | |
| if __name__ == '__main__': | |
| demo.queue().launch() |