Delete app.py

app.py

@@ -1,1431 +0,0 @@
-import gradio as gr
-import cv2
-from PIL import Image
-import numpy as np
-import os
-import torch
-import torch.nn.functional as F
-from torchvision import transforms
-from torchvision.transforms import Compose
-import tempfile
-from functools import partial
-import spaces
-from zipfile import ZipFile
-from vincenty import vincenty
-import json
-from collections import Counter
-import mediapy
-import ffmpy
-from apng import APNG
-
-#from depth_anything.dpt import DepthAnything
-#from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
-from huggingface_hub import hf_hub_download
-from depth_anything_v2.dpt import DepthAnythingV2
-
-DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
-model_configs = {
-    'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
-    'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
-    'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
-    'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
-}
-encoder2name = {
-    'vits': 'Small',
-    'vitb': 'Base',
-    'vitl': 'Large',
-    'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint
-}
-
-blurin = "1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1"
-edge = []
-gradient = None
-params = { "fnum":0 }
-pcolors = []
-frame_selected = 0
-frames = []
-backups = []
-depths = []
-masks = []
-locations = []
-mesh = []
-mesh_n = []
-scene = None
-
-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_alpha_video(frames, fps, type):
-    print("building composite video result")
-    imgs = []
-    for j, img in enumerate(frames):
-        imgs.append(cv2.imread(img).astype(np.uint8))
-
-    APNG.from_files(frames, delay=1000).save(type + "_result.png")
-    mediapy.write_video(type + "_result.webm", imgs, fps=fps, codec='vp9')
-    return type + "_result.webm"
-
-def create_video(frames, fps, type):
-    print("building video result")
-    imgs = []
-    for j, img in enumerate(frames):
-        imgs.append(cv2.cvtColor(cv2.imread(img).astype(np.uint8), cv2.COLOR_BGR2RGB))
-
-    mediapy.write_video(type + "_result.mp4", imgs, fps=fps)
-    return type + "_result.mp4"
-
-@torch.no_grad()
-#@spaces.GPU
-def predict_depth(image, model):
-    return model.infer_image(image)
-    
-#def predict_depth(model, image):
-#    return model(image)["depth"]
-
-def make_video(video_path, outdir='./vis_video_depth', encoder='vits', blur_data=blurin, o=1, b=32):
-    if encoder not in ["vitl","vitb","vits","vitg"]:
-        encoder = "vits"
-
-    model_name = encoder2name[encoder]
-    model = DepthAnythingV2(**model_configs[encoder])
-    filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model")
-    state_dict = torch.load(filepath, map_location="cpu")
-    model.load_state_dict(state_dict)
-    model = model.to(DEVICE).eval()
-
-    #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)
-    global masks
-    
-    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
-        n = 0
-        depth_frames = []
-        orig_frames = []
-        backup_frames = []
-        rgba_frames = []
-        thumbnail_old = []
-
-        while raw_video.isOpened():
-            ret, raw_frame = raw_video.read()
-            if not ret:
-                break
-            else:
-                print(count)
-
-            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)
-            #raw_frame_bg = cv2.medianBlur(raw_frame, 255)
-
-            #
-            depth = predict_depth(raw_frame[:, :, ::-1], model)
-            depth_gray = ((depth - depth.min()) / (depth.max() - depth.min()) * 255.0).astype(np.uint8)
-            #
-            
-            #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)
-
-            # 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_gray[0:int(depth_gray.shape[0]/8*7)-1, 0:depth_gray.shape[1]], 250, 255)
-            # change image to black where we found white
-            depth_gray[0:int(depth_gray.shape[0]/8*7)-1, 0:depth_gray.shape[1]][mask>0] = 0
-
-            mask = cv2.inRange(depth_gray[int(depth_gray.shape[0]/8*7):depth_gray.shape[0], 0:depth_gray.shape[1]], 192, 255)
-            depth_gray[int(depth_gray.shape[0]/8*7):depth_gray.shape[0], 0:depth_gray.shape[1]][mask>0] = 192
-
-            depth_color = cv2.cvtColor(depth_gray, cv2.COLOR_GRAY2BGR)
-            # 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)
-
-            #raw_frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2BGRA)
-            #raw_frame[:, :, 3] = 255
-
-            if cframes < 16:
-              thumbnail = cv2.cvtColor(cv2.resize(raw_frame, (16,32)), cv2.COLOR_BGR2GRAY).flatten()
-              if len(thumbnail_old) > 0:
-                  diff = thumbnail - thumbnail_old
-                  #print(diff)
-                  c = Counter(diff)
-                  value, cc = c.most_common()[0]
-                  if value == 0 and cc > int(16*32*0.8):
-                      count += 1
-                      continue
-              thumbnail_old = thumbnail
-
-            blur_frame = blur_image(raw_frame, depth_color, blur_data)
-            rgba_frame = cv2.cvtColor(cv2.cvtColor(blur_frame, cv2.COLOR_BGR2RGB), cv2.COLOR_RGB2RGBA)
-            rgba_frame[:,:,3] = depth_gray
-
-            cv2.imwrite(f"f{count}.png", rgba_frame)
-            rgba_frames.append(f"f{count}.png")
-            
-            cv2.imwrite(f"f{count}.jpg", blur_frame)
-            orig_frames.append(f"f{count}.jpg")
-
-            cv2.imwrite(f"f{count}_.jpg", blur_frame)
-            backup_frames.append(f"f{count}_.jpg")
-            
-            cv2.imwrite(f"f{count}_dmap.jpg", depth_color)
-            depth_frames.append(f"f{count}_dmap.jpg")
-
-            depth_gray = seg_frame(depth_gray, o, b) + 128
-            #print(depth_gray[depth_gray>128]-128)
-
-            cv2.imwrite(f"f{count}_mask.jpg", depth_gray)
-            masks.append(f"f{count}_mask.jpg")            
-            count += 1
-
-        final_vid = create_video(orig_frames, frame_rate, "orig")
-        depth_vid = create_video(depth_frames, frame_rate, "depth")
-        alpha_vid = create_alpha_video(rgba_frames, frame_rate, "rgba")
-            
-        final_zip = zip_files(orig_frames, depth_frames)
-        raw_video.release()
-        # out.release()
-        cv2.destroyAllWindows()
-
-        global gradient
-        global frame_selected
-        global depths
-        global frames
-        global backups 
-        frames = orig_frames
-        backups = backup_frames
-        depths = depth_frames
-
-        if depth_color.shape[0] == 2048: #height
-            gradient = cv2.imread('./gradient_large.png').astype(np.uint8)
-        elif depth_color.shape[0] == 1024:
-            gradient = cv2.imread('./gradient.png').astype(np.uint8)
-        else:
-            gradient = cv2.imread('./gradient_small.png').astype(np.uint8)
-        
-        return alpha_vid, final_zip, frames, masks[frame_selected], depths, depth_vid #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]]
-    nl = [[0,0,0]]
-    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]
-
-            nx = np.cos(d_lon) * np.sin(d_lat)
-            ny = np.cos(d_lat)
-            nz = np.sin(d_lon) * np.sin(d_lat)
-            
-            # Convert to cartesian coordinates
-            x = radius * nx
-            y = radius * ny
-            z = radius * nz
-
-            pts = np.stack([x, y, z], axis=1)
-            uvs = np.stack([lon/np.pi/2, lat/np.pi], axis=1)
-            nls = np.stack([-nx, -ny, -nz], axis=1)
-            
-            pts3d = np.concatenate((pts3d, pts), axis=0)
-            uv = np.concatenate((uv, uvs), axis=0)
-            nl = np.concatenate((nl, nls), axis=0)
-            #print(f'i: {i}, j: {j}')
-            j = j+1
-        i = i+1
-        
-    return [pts3d, uv, nl]
-
-def rgb2gray(rgb):
-    return np.dot(rgb[...,:3], [0.333, 0.333, 0.333])
-
-def get_mesh(image, depth, blur_data, loadall):
-    global depths
-    global pcolors
-    global frame_selected
-    global mesh
-    global mesh_n
-    global scene
-    if loadall == False:
-        mesh = []
-        mesh_n = []
-    fnum = frame_selected
-
-    #print(image[fnum][0])
-    #print(depth["composite"])
-
-    depthc = cv2.imread(depths[frame_selected], cv2.IMREAD_UNCHANGED).astype(np.uint8)
-    blur_img = blur_image(cv2.imread(image[fnum][0], cv2.IMREAD_UNCHANGED).astype(np.uint8), depthc, blur_data)
-    gdepth = cv2.cvtColor(depthc, cv2.COLOR_RGB2GRAY) #rgb2gray(depthc)
-    
-    print('depth to gray - ok')
-    points = pano_depth_to_world_points(gdepth)
-    pts3d = points[0]
-    uv = points[1]
-    nl = points[2]
-    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
-    uvs = uv.reshape(-1, 2)
-    #print(uvs)
-    #verts = pts3d.reshape(-1, 3)
-    verts = [[0,0,0]]
-    normals = nl.reshape(-1, 3)
-    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
-    #verts, clrs
-
-    #pcd = o3d.geometry.TriangleMesh.create_tetrahedron()
-    #pcd.compute_vertex_normals()
-    #pcd.paint_uniform_color((1.0, 1.0, 1.0))
-    #mesh.append(pcd)
-    #print(mesh[len(mesh)-1])
-    if not str(fnum) in mesh_n:
-        mesh_n.append(str(fnum))
-    print('mesh - ok')
-
-    # Save as glb
-    #glb_file = tempfile.NamedTemporaryFile(suffix='.glb', delete=False)
-    #o3d.io.write_triangle_mesh(glb_file.name, pcd)
-    #print('file - ok')
-    return "./TriangleWithoutIndices.gltf", ",".join(mesh_n)
-    
-
-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
-
-    white = cv2.inRange(blur_frame, np.array([255,255,255]), np.array([255,255,255]))
-    blur_frame[white>0] = (254,254,254)
-    
-    return blur_frame
-
-def loadfile(f):
-    return f
-
-def show_json(txt):
-    data = json.loads(txt)
-    print(txt)
-    i=0
-    while i < len(data[2]):
-        data[2][i] = data[2][i]["image"]["path"]
-        data[4][i] = data[4][i]["path"]
-        i=i+1
-    return data[0]["video"]["path"], data[1]["path"], data[2], data[3]["background"]["path"], data[4], data[5]
-
-
-def seg_frame(newmask, b, d):
-
-    if newmask.shape[0] == 2048: #height
-        gd = cv2.imread('./gradient_large.png', cv2.IMREAD_GRAYSCALE).astype(np.uint8)
-    elif newmask.shape[0] == 1024:
-        gd = cv2.imread('./gradient.png', cv2.IMREAD_GRAYSCALE).astype(np.uint8)
-    else:
-        gd = cv2.imread('./gradient_small.png', cv2.IMREAD_GRAYSCALE).astype(np.uint8)
-   
-    newmask[np.absolute(newmask.astype(np.int16)-gd.astype(np.int16))<16] = 0
-    ret,newmask = cv2.threshold(newmask,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
-
-    #b = 1
-    #d = 32
-    element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * b + 1, 2 * b + 1), (b, b))
-    bd = cv2.erode(newmask, element)
-    element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * d + 1, 2 * d + 1), (d, d))
-    bg = cv2.dilate(newmask, element)
-    bg[bg.shape[0]-64:bg.shape[0],0:bg.shape[1]] = 0
-
-    mask = np.zeros(newmask.shape[:2],np.uint8)
-    # https://docs.opencv.org/4.x/d8/d83/tutorial_py_grabcut.html
-    # wherever it is marked white (sure foreground), change mask=1
-    # wherever it is marked black (sure background), change mask=0
-    mask[bg == 255] = 3
-    mask[bd == 255] = 1 #2: probable bg, 3: probable fg
-    
-    return mask
-
-
-def select_frame(d, evt: gr.SelectData):
-    global frame_selected
-    global depths
-    global masks
-    global edge
-    
-    if evt.index != frame_selected:
-        edge = []
-        frame_selected = evt.index
-        
-    return depths[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 bincount(a):
-    a2D = a.reshape(-1,a.shape[-1])
-    col_range = (256, 256, 256) # generically : a2D.max(0)+1
-    a1D = np.ravel_multi_index(a2D.T, col_range)
-    return list(reversed(np.unravel_index(np.bincount(a1D).argmax(), col_range)))
-
-def reset_mask(d):
-    global frame_selected
-    global frames
-    global backups
-    global masks
-    global depths
-    global edge
-
-    edge = []
-    backup = cv2.imread(backups[frame_selected]).astype(np.uint8)
-    cv2.imwrite(frames[frame_selected], backup)
-
-    d["layers"][0][0:d["layers"][0].shape[0], 0:d["layers"][0].shape[1]] = (0,0,0,0)
-
-    return gr.ImageEditor(value=d)
-
-
-def draw_mask(o, b, v, d, evt: gr.EventData):
-    global frames
-    global depths
-    global params
-    global frame_selected
-    global masks
-    global gradient
-    global edge
-    
-    points = json.loads(v)
-    pts = np.array(points, np.int32)
-    pts = pts.reshape((-1,1,2))
-
-    if len(edge) == 0 or params["fnum"] != frame_selected:
-      if params["fnum"] != frame_selected:
-          d["background"] = cv2.imread(depths[frame_selected]).astype(np.uint8)
-          params["fnum"] = frame_selected
-
-      bg = cv2.cvtColor(d["background"], cv2.COLOR_RGBA2GRAY)
-      bg[bg==255] = 0
-        
-      edge = bg.copy()
-    else:
-      bg = edge.copy()
-
-    x = points[len(points)-1][0]
-    y = points[len(points)-1][1]
-
-    mask = cv2.imread(masks[frame_selected], cv2.IMREAD_GRAYSCALE).astype(np.uint8)
-    mask[mask==128] = 0
-    print(mask[mask>0]-128)
-    d["layers"][0] = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGBA)
-
-    sel = cv2.floodFill(mask, None, (x, y), 1, 2, 2, (4 | cv2.FLOODFILL_FIXED_RANGE))[2] #(4 | cv2.FLOODFILL_FIXED_RANGE | cv2.FLOODFILL_MASK_ONLY | 255 << 8)
-    # 255 << 8 tells to fill with the value 255)
-    sel = sel[1:sel.shape[0]-1, 1:sel.shape[1]-1]
-
-    d["layers"][0][sel==0] = (0,0,0,0)
-
-
-    mask = cv2.cvtColor(d["layers"][0], cv2.COLOR_RGBA2GRAY)
-    mask[mask==0] = 128
-    print(mask[mask>128]-128)
-    mask, bgdModel, fgdModel = cv2.grabCut(cv2.cvtColor(d["background"], cv2.COLOR_RGBA2RGB), mask-128, None,None,None,15, cv2.GC_INIT_WITH_MASK)
-    mask = np.where((mask==2)|(mask==0),0,1).astype('uint8')
-
-    frame = cv2.imread(frames[frame_selected], cv2.IMREAD_UNCHANGED).astype(np.uint8)
-    frame[mask>0] = (255,255,255)
-    cv2.imwrite(frames[frame_selected], frame)
-    
-    switch_rows(False)
-    return gr.ImageEditor(value=d)
-
-
-load_model="""
-async(c, o, p, d, n, m, s)=>{
-  var intv = setInterval(function(){
-    if (document.getElementById("model3D").getElementsByTagName("canvas")[0]) {
-      try {
-      if (typeof BABYLON !== "undefined" && BABYLON.Engine && BABYLON.Engine.LastCreatedScene) {
-        BABYLON.Engine.LastCreatedScene.onAfterRenderObservable.add(function() { //onDataLoadedObservable
-
-          var then = new Date().getTime();
-          var now, delta;
-          const interval = 1000 / 25;
-          const tolerance = 0.1;
-          
-          BABYLON.Engine.LastCreatedScene.getEngine().stopRenderLoop();
-          BABYLON.Engine.LastCreatedScene.getEngine().runRenderLoop(function () {
-            now = new Date().getTime();
-            delta = now - then;
-            then = now - (delta % interval);
-            if (delta >= interval - tolerance) {
-                BABYLON.Engine.LastCreatedScene.render();
-            }
-          });
-        
-          BABYLON.Engine.LastCreatedScene.getEngine().setHardwareScalingLevel(1.0);
-          BABYLON.Engine.LastCreatedScene.clearColor = new BABYLON.Color4(255,255,255,255);
-          BABYLON.Engine.LastCreatedScene.ambientColor = new BABYLON.Color4(255,255,255,255);
-          //BABYLON.Engine.LastCreatedScene.autoClear = false;
-          //BABYLON.Engine.LastCreatedScene.autoClearDepthAndStencil = false;
-          /*for (var i=0; i<BABYLON.Engine.LastCreatedScene.getNodes().length; i++) {
-            if (BABYLON.Engine.LastCreatedScene.getNodes()[i].material) {
-              BABYLON.Engine.LastCreatedScene.getNodes()[i].material.pointSize = Math.ceil(Math.log2(Math.PI/document.getElementById("zoom").value));
-            }
-          }*/
-          BABYLON.Engine.LastCreatedScene.getAnimationRatio();
-        });
-        
-        if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) {
-          BABYLON.Engine.LastCreatedScene.activeCamera.metadata = {
-            pipeline: new BABYLON.DefaultRenderingPipeline("default", true, BABYLON.Engine.LastCreatedScene, [BABYLON.Engine.LastCreatedScene.activeCamera]) 
-          }
-        }
-        BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.samples = 4;
-        BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.contrast = 1.0;
-        BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure = 1.0;
-
-        //BABYLON.Engine.LastCreatedScene.activeCamera.detachControl(document.getElementById("model3D").getElementsByTagName("canvas")[0]);
-        BABYLON.Engine.LastCreatedScene.activeCamera.inertia = 0.0;
-        //pan
-        BABYLON.Engine.LastCreatedScene.activeCamera.panningInertia = 0.0;
-        BABYLON.Engine.LastCreatedScene.activeCamera.panningDistanceLimit = 16;
-        BABYLON.Engine.LastCreatedScene.activeCamera.panningSensibility = 16;
-        //zoom
-        BABYLON.Engine.LastCreatedScene.activeCamera.pinchDeltaPercentage = 1/256;
-        BABYLON.Engine.LastCreatedScene.activeCamera.wheelDeltaPercentage = 1/256;
-        BABYLON.Engine.LastCreatedScene.activeCamera.upperRadiusLimit = (1.57-0.157)*16;
-        BABYLON.Engine.LastCreatedScene.activeCamera.lowerRadiusLimit = 0.0;
-        //BABYLON.Engine.LastCreatedScene.activeCamera.attachControl(document.getElementById("model3D").getElementsByTagName("canvas")[0], false);
-        
-        BABYLON.Engine.LastCreatedScene.activeCamera.fov = document.getElementById("zoom").value;
-
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].style.filter = "blur(" + Math.ceil(Math.log2(Math.PI/document.getElementById("zoom").value))/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();}
-
-        document.getElementById("model3D").appendChild(document.getElementById("compass_box"));
-        window.coords = JSON.parse(document.getElementById("coords").getElementsByTagName("textarea")[0].value);
-        window.counter = 0;
-
-        if (o.indexOf(""+n) < 0) {
-          if (o != "") { o += ","; }
-          o += n;
-        }
-        //alert(o);
-        var o_ = o.split(",");
-        var q = BABYLON.Engine.LastCreatedScene.meshes;
-        for(i = 0; i < q.length; i++) {
-          let mesh = q[i];
-          mesh.dispose(false, true);
-        }
-        var dome = [];
-        /*for (var j=0; j<o_.length; j++) {
-          o_[j] = parseInt(o_[j]);
-          dome[j] = new BABYLON.PhotoDome("dome"+j, p[o_[j]].image.url, 
-          {
-            resolution: 16,
-            size: 512
-          }, BABYLON.Engine.LastCreatedScene);
-          var q = BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-2]._children;
-          for(i = 0; i < q.length; i++) {
-            let mesh = q[i];
-            mesh.dispose(false, true);
-          }
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].name = "dome"+j;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].scaling.z = -1;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].alphaIndex = o_.length-j;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].visibility = 0.9999;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].material.diffuseTexture.hasAlpha = true;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].material.useAlphaFromDiffuseTexture = true;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].applyDisplacementMap(m[o_[j]].url, 0, 255, function(m){try{alert(BABYLON.Engine.Version);}catch(e){alert(e);}}, null, null, true, function(e){alert(e);});
-          
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].rotationQuaternion = null;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].position.z = coords[o_[j]].lat;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].position.x = coords[o_[j]].lng;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].rotation.y = coords[o_[j]].heading / 180 * Math.PI;
-          BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].rotation.z = -coords[o_[j]].pitch / 180 * Math.PI;
-        }*/
-
-        if (s == false) {
-          v_url = document.getElementById("output_video").getElementsByTagName("video")[0].src;
-        } else {
-          v_url = document.getElementById("depth_video").getElementsByTagName("video")[0].src;
-        }
-        window.videoDome = new BABYLON.VideoDome(
-            "videoDome", [v_url],
-            {
-                resolution: 16,
-                size: 512,
-                clickToPlay: false,
-            }, BABYLON.Engine.LastCreatedScene
-        );
-        var q = BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-2]._children;
-        for (i = 0; i < q.length; i++) {
-            let mesh = q[i];
-            mesh.dispose(false, true);
-        }
-        BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].rotationQuaternion = null;
-        //BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].position.z = coords[counter].lat;
-        //BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].position.x = coords[counter].lng;
-        BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].rotation.y = coords[counter].heading / 180 * Math.PI;
-        BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].rotation.z = -coords[counter].pitch / 180 * Math.PI;
-        
-        BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].scaling.z = -1;
-        BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].material.diffuseTexture.hasAlpha = true;
-        BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].material.useAlphaFromDiffuseTexture = true;
-        //BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].material.emissiveTexture = videoDome.videoTexture;
-        //BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].material.emissiveTexture.hasAlpha = true;
-        //BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].material.useAlphaFromEmissiveTexture = true;
-        BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].alphaIndex = 1;
-        BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1].visibility = 0.9999;
-
-        window.md = false;
-        window.rd = false;
-        window.compass = document.getElementById("compass");
-        window.x = 0;
-        window.y = 0;
-        window.xold = 0;
-        window.yold = 0;
-        window.buffer = null;
-        window.bufferCanvas = document.createElement("canvas");
-        window.ctx = bufferCanvas.getContext("2d", { willReadFrequently: true });
-        window.video = document.getElementById("depth_video").getElementsByTagName("video")[0];
-        window.parallax = 0;
-        window.xdir = new BABYLON.Vector3(1, 0, 0);
-        window.rdir = new BABYLON.Vector3(0, 0, 0);
-        window.videoDomeMesh = BABYLON.Engine.LastCreatedScene.meshes[BABYLON.Engine.LastCreatedScene.meshes.length-1];
-
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('pointermove', function(evt) {
-        if (md === true) {
-          rdir = BABYLON.Engine.LastCreatedScene.activeCamera.getDirection(xdir);
-          videoDomeMesh.position.x = parallax * rdir.x;
-          videoDomeMesh.position.z = parallax * rdir.z;
-          
-          try {
-            compass.style.transform = "rotateX(" + (BABYLON.Engine.LastCreatedScene.activeCamera.beta-Math.PI/2) + "rad) rotateZ(" + BABYLON.Engine.LastCreatedScene.activeCamera.alpha + "rad)";
-          } catch(e) {alert(e);}
-        }
-        if (rd === true) {
-          x = parseInt(evt.clientX - evt.target.getBoundingClientRect().x);
-          y = parseInt(evt.clientY - evt.target.getBoundingClientRect().y);
-          
-          if (Math.abs(BABYLON.Engine.LastCreatedScene.activeCamera.radius) > (1.57-0.157)*16) {
-            BABYLON.Engine.LastCreatedScene.activeCamera.radius = (1.57-0.157)*16;
-          } else {
-            BABYLON.Engine.LastCreatedScene.activeCamera.fov = BABYLON.Engine.LastCreatedScene.activeCamera.radius/16 + 0.157;
-          }
-          document.getElementById('zoom').value = BABYLON.Engine.LastCreatedScene.activeCamera.fov;
-          document.getElementById('zoom').parentNode.childNodes[2].innerText = document.getElementById('zoom').value;
-          
-          xold=x;
-          yold=y;
-        }
-        });
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('pointerdown', function() {
-          md = true;
-        });
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('pointerup', function() {
-          md = false;
-          rd = false;
-        });
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('pointercancel', function() {
-          md = false;
-          rd = false;
-        });
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('pointerleave', function() {
-          md = false;
-          rd = false;
-        });
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('pointerout', function() {
-          md = false;
-          rd = false;
-        });
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('contextmenu', function() {
-          rd = true;
-        });
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('gesturestart', function() {
-          rd = true;
-        });
-        document.getElementById("model3D").getElementsByTagName("canvas")[0].addEventListener('gestureend', function() {
-          rd = false;
-        });
-        
-
-        function requestMap() {
-        try {
-          ctx.drawImage(video, 0, 0, video.videoWidth, video.videoHeight);
-          videoDome.videoTexture.video.pause();
-          video.pause();
-          if (buffer) {
-            counter = parseInt(video.currentTime);
-            if (!coords[counter]) {counter = coords.length-1;}
-            applyDisplacementMapFromBuffer(videoDomeMesh, buffer, video.videoWidth, video.videoHeight, 0, -1, null, null, true);
-          }
-          buffer = ctx.getImageData(0, 0, video.videoWidth, video.videoHeight).data;
-          applyDisplacementMapFromBuffer(videoDomeMesh, buffer, video.videoWidth, video.videoHeight, 0, 1, null, null, true);
-        } catch(e) {alert(e)}
-        }
-        window.requestMap = requestMap;
-
-        videoDome.videoTexture.video.oncanplaythrough = function () {
-          document.getElementById('seek').innerHTML = '';
-          for (var i=0; i<videoDome.videoTexture.video.duration; i++) {
-            document.getElementById('seek').innerHTML += '<a href="#" style="position:absolute;left:'+(56+coords[i].lng/2)+'px;top:'+(56-coords[i].lat/2)+'px;" onclick="seek('+i+');">-'+i+'-</a> ';
-          }
-          bufferCanvas.width = video.videoWidth;
-          bufferCanvas.height = video.videoHeight;
-          
-          videoPlay();
-        };
-
-        //var debugLayer = BABYLON.Engine.LastCreatedScene.debugLayer.show();
-        
-        if (document.getElementById("model")) {
-          document.getElementById("model").appendChild(document.getElementById("model3D"));
-          toggleDisplay("model");
-        }
-        
-        clearInterval(intv);
-      }
-      } catch(e) {alert(e);}
-    }
-  }, 40);
-}
-"""
-
-js = """
-async()=>{
-console.log('Hi');
-
-const chart = document.getElementById('chart');
-const blur_in = document.getElementById('blur_in').getElementsByTagName('textarea')[0];
-var md = false;
-var xold = 128;
-var yold = 32;
-var a = new Array(256);
-var l;
-
-for (var i=0; i<256; i++) {
-  const hr = document.createElement('hr');
-  hr.style.backgroundColor = 'hsl(0,0%,' + (100-i/256*100) + '%)';
-  chart.appendChild(hr);
-}
-
-function resetLine() {
-  a.fill(1);
-  for (var i=0; i<256; i++) {
-    chart.childNodes[i].style.height = a[i] + 'px';
-    chart.childNodes[i].style.marginTop = '32px';
-  }
-}
-resetLine();
-window.resetLine = resetLine;
-
-function pointerDown(x, y) {
-  md = true;
-  xold = parseInt(x - chart.getBoundingClientRect().x);
-  yold = parseInt(y - chart.getBoundingClientRect().y);
-  chart.title = xold + ',' + yold;
-}
-window.pointerDown = pointerDown;
-
-function pointerUp() {
-  md = false;
-  var evt = document.createEvent('Event');
-  evt.initEvent('input', true, false);
-  blur_in.dispatchEvent(evt);
-  chart.title = '';
-}
-window.pointerUp = pointerUp;
-
-function lerp(y1, y2, mu) { return y1*(1-mu)+y2*mu; }
-
-function drawLine(x, y) {
-  x = parseInt(x - chart.getBoundingClientRect().x);
-  y = parseInt(y - chart.getBoundingClientRect().y);
-  if (md === true && y >= 0 && y < 64 && x >= 0 && x < 256) {
-    if (y < 32) {
-      a[x] = Math.abs(32-y)*2 + 1;
-      chart.childNodes[x].style.height = a[x] + 'px';
-      chart.childNodes[x].style.marginTop = y + 'px';
-
-      for (var i=Math.min(xold, x)+1; i<Math.max(xold, x); i++) {
-        l = parseInt(lerp( yold, y, (i-xold)/(x-xold) ));
-
-        if (l < 32) {
-          a[i] = Math.abs(32-l)*2 + 1;
-          chart.childNodes[i].style.height = a[i] + 'px';
-          chart.childNodes[i].style.marginTop = l + 'px';
-        } else if (l < 64) {
-          a[i] = Math.abs(l-32)*2 + 1;
-          chart.childNodes[i].style.height = a[i] + 'px';
-          chart.childNodes[i].style.marginTop = (64-l) + 'px';
-        }
-      }
-    } else if (y < 64) {
-      a[x] = Math.abs(y-32)*2 + 1;
-      chart.childNodes[x].style.height = a[x] + 'px';
-      chart.childNodes[x].style.marginTop = (64-y) + 'px';
-
-      for (var i=Math.min(xold, x)+1; i<Math.max(xold, x); i++) {
-        l = parseInt(lerp( yold, y, (i-xold)/(x-xold) ));
-
-        if (l < 32) {
-          a[i] = Math.abs(32-l)*2 + 1;
-          chart.childNodes[i].style.height = a[i] + 'px';
-          chart.childNodes[i].style.marginTop = l + 'px';
-        } else if (l < 64) {
-          a[i] = Math.abs(l-32)*2 + 1;
-          chart.childNodes[i].style.height = a[i] + 'px';
-          chart.childNodes[i].style.marginTop = (64-l) + 'px';
-        }
-      }
-    }
-    blur_in.value = a.join(' ');
-    xold = x;
-    yold = y;
-    chart.title = xold + ',' + yold;
-  }
-}
-window.drawLine = drawLine;
-
-
-window.screenshot = false;
-
-function snapshot() {
-if (BABYLON) {
-  screenshot = true;
-  BABYLON.Engine.LastCreatedScene.getEngine().onEndFrameObservable.add(function() {
-    if (screenshot === true) {
-    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="1" />
-                            </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
-                }
-          });
-}
-}
-window.snapshot = snapshot;
-
-
-window.recorder = null;
-
-function record_video() {
-  try {
-  if (BABYLON.VideoRecorder.IsSupported(BABYLON.Engine.LastCreatedScene.getEngine()) && (recorder == null || !recorder.isRecording) ) {
-    if (recorder == null) {
-        recorder = new BABYLON.VideoRecorder(BABYLON.Engine.LastCreatedScene.getEngine(), { mimeType:'video/mp4', fps:25, /*audioTracks: mediaStreamDestination.stream.getAudioTracks()*/ });
-    }
-    recorder.startRecording('video.mp4', 60*60);
-  }
-  } catch(e) {alert(e);}
-}
-window.record_video = record_video;
-
-function stop_recording() {
-  if (recorder.isRecording) {
-    recorder.stopRecording();
-  }
-}
-window.stop_recording = stop_recording;
-
-function seek(t) {
-  videoDome.videoTexture.video.currentTime = t;
-  if (videoDome.videoTexture.video.currentTime > videoDome.videoTexture.video.duration) {
-    videoDome.videoTexture.video.currentTime = videoDome.videoTexture.video.duration;
-  } else if (videoDome.videoTexture.video.currentTime < 0) {
-    videoDome.videoTexture.video.currentTime = 0;
-  }
-  video.currentTime = t;
-  if (video.currentTime > video.duration) {
-    video.currentTime = video.duration;
-  } else if (video.currentTime < 0) {
-    video.currentTime = 0;
-  }
-  requestMap();
-}
-window.seek = seek;
-
-function videoPlay() {
-  videoDome.videoTexture.video.oncanplaythrough = null;
-  video.oncanplaythrough = null;
-
-  videoDome.videoTexture.video.loop = true;
-  video.loop = true;
-  videoDome.videoTexture.video.play();
-  video.play();
-}
-window.videoPlay = videoPlay;
-
-
-    function applyDisplacementMapFromBuffer(
-        mesh,
-        buffer,
-        heightMapWidth,
-        heightMapHeight,
-        minHeight,
-        maxHeight,
-        uvOffset,
-        uvScale,
-        forceUpdate
-    ) {
-      try {
-        if (!mesh.isVerticesDataPresent(BABYLON.VertexBuffer.NormalKind)) {
-            let positions = mesh.getVerticesData(BABYLON.VertexBuffer.PositionKind);
-            let normals = [];
-            BABYLON.VertexData.ComputeNormals(positions, mesh.getIndices(), normals, {useRightHandedSystem: true});
-            mesh.setVerticesData(BABYLON.VertexBuffer.NormalKind, normals);
-        }
-        const positions = mesh.getVerticesData(BABYLON.VertexBuffer.PositionKind, true, true);
-        const normals = mesh.getVerticesData(BABYLON.VertexBuffer.NormalKind);
-        const uvs = mesh.getVerticesData(BABYLON.VertexBuffer.UVKind);
-
-        let position = BABYLON.Vector3.Zero();
-        const normal = BABYLON.Vector3.Zero();
-        const uv = BABYLON.Vector2.Zero();
-
-        uvOffset = uvOffset || BABYLON.Vector2.Zero();
-        uvScale = uvScale || new BABYLON.Vector2(1, 1);
-
-        for (let index = 0; index < positions.length; index += 3) {
-            BABYLON.Vector3.FromArrayToRef(positions, index, position);
-            BABYLON.Vector3.FromArrayToRef(normals, index, normal);
-            BABYLON.Vector2.FromArrayToRef(uvs, (index / 3) * 2, uv);
-
-            // Compute height
-            const u = (Math.abs(uv.x * uvScale.x + (uvOffset.x % 1)) * (heightMapWidth - 1)) % heightMapWidth | 0;
-            const v = (Math.abs(uv.y * uvScale.y + (uvOffset.y % 1)) * (heightMapHeight - 1)) % heightMapHeight | 0;
-
-            const pos = (u + v * heightMapWidth) * 4;
-            const r = buffer[pos] / 255.0;
-            const g = buffer[pos + 1] / 255.0;
-            const b = buffer[pos + 2] / 255.0;
-            const a = buffer[pos + 3] / 255.0;
-
-            const gradient = r * 0.33 + g * 0.33 + b * 0.33;
-            //const gradient = a;
-
-            normal.normalize();
-            normal.scaleInPlace(minHeight + (maxHeight - minHeight) * gradient);
-            position = position.add(normal);
-
-            position.toArray(positions, index);
-        }
-        mesh.setVerticesData(BABYLON.VertexBuffer.PositionKind, positions);
-
-        return mesh;
-    } catch(e) {alert(e)}
-    }
-    window.applyDisplacementMapFromBuffer = applyDisplacementMapFromBuffer;
-
-
-var intv_ = setInterval(function(){
-if (document.getElementById("image_edit") && document.getElementById("image_edit").getElementsByTagName("canvas")) {
-  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("image_edit").getElementsByTagName("canvas")[0].onfocus = function(e) {
-    document.getElementById("mouse").getElementsByTagName("textarea")[0].value = "[]";
-  }
-  document.getElementById("image_edit").getElementsByTagName("canvas")[0].onblur = function(e) {
-    document.getElementById("mouse").getElementsByTagName("textarea")[0].value = "[]";
-  }
-  clearInterval(intv_);
-}
-}, 40);
-
-}
-"""
-
-css = """
-#img-display-container {
-    max-height: 100vh;
-    }
-#img-display-input {
-    max-height: 80vh;
-    }
-#img-display-output {
-    max-height: 80vh;
-    }
-"""
-
-head = """
-"""
-
-title = "# Depth Anything V2 Video"
-description = """**Depth Anything V2** on full video files, intended for Google Street View panorama slideshows.   
-Please refer to the [paper](https://arxiv.org/abs/2406.09414), [project page](https://depth-anything-v2.github.io), and [github](https://github.com/DepthAnything/Depth-Anything-V2) 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, js=js, head=head) as demo:
-    gr.Markdown(title)
-    gr.Markdown(description)
-    gr.Markdown("### Video Depth Prediction demo")
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Group():
-              input_json = gr.Textbox(elem_id="json_in", value="{}", label="JSON", interactive=False)
-              input_url = gr.Textbox(elem_id="url_in", value="./examples/streetview.mp4", label="URL")
-              input_video = gr.Video(label="Input Video", format="mp4")
-              input_url.input(fn=loadfile, inputs=[input_url], outputs=[input_video])
-              submit = gr.Button("Submit")
-            with gr.Group():
-              output_frame = gr.Gallery(label="Frames", preview=True, columns=8192, interactive=False)
-              output_switch = gr.Checkbox(label="Show depths")
-              output_switch.input(fn=switch_rows, inputs=[output_switch], outputs=[output_frame])
-              selected = gr.Number(label="Selected frame", visible=False, elem_id="fnum", value=0, minimum=0, maximum=256, interactive=False)
-              with gr.Accordion(label="Depths", open=False):
-                output_depth = gr.Files(label="Depth files", interactive=False)
-            with gr.Group():
-              output_mask = gr.ImageEditor(layers=False, sources=('clipboard'), show_download_button=True, type="numpy", interactive=True, transforms=(None,), eraser=gr.Eraser(), brush=gr.Brush(default_size=0, colors=['black', '#505050', '#a0a0a0', 'white']), elem_id="image_edit")
-              with gr.Accordion(label="Border", open=False):
-                boffset = gr.Slider(label="Inner", value=1, maximum=256, minimum=0, step=1)
-                bsize = gr.Slider(label="Outer", value=32, maximum=256, minimum=0, step=1)
-                mouse = gr.Textbox(label="Mouse x,y", elem_id="mouse", value="""[]""", interactive=False)
-              reset = gr.Button("Reset", size='sm')
-              mouse.input(fn=draw_mask, show_progress="minimal", inputs=[boffset, bsize, mouse, output_mask], outputs=[output_mask])
-              reset.click(fn=reset_mask, inputs=[output_mask], outputs=[output_mask])
-
-        with gr.Column():
-          model_type = gr.Dropdown([("small", "vits"), ("base", "vitb"), ("large", "vitl"), ("giant", "vitg")], type="value", value="vits", label='Model Type')
-          processed_video = gr.Video(label="Output Video", format="mp4", elem_id="output_video", interactive=False)
-          processed_zip = gr.File(label="Output Archive", interactive=False)
-          depth_video = gr.Video(label="Depth Video", format="mp4", elem_id="depth_video", interactive=False, visible=True)
-          result = gr.Model3D(label="3D Mesh", clear_color=[0.5, 0.5, 0.5, 0.0], camera_position=[0, 90, 512], zoom_speed=2.0, pan_speed=2.0, interactive=True, elem_id="model3D")
-          with gr.Accordion(label="Embed in website", open=False):
-            embed_model = gr.Textbox(elem_id="embed_model", label="Include this wherever the model is to appear on the page", interactive=False, value="""
-              
-            """)
-            
-          with gr.Tab("Blur"):
-            chart_c = gr.HTML(elem_id="chart_c", value="""<div id='chart' onpointermove='window.drawLine(event.clientX, event.clientY);' onpointerdown='window.pointerDown(event.clientX, event.clientY);' onpointerup='window.pointerUp();' onpointerleave='window.pointerUp();' onpointercancel='window.pointerUp();' onclick='window.resetLine();'></div>
-            <style>
-  * {
-    user-select: none;
-  }
-  html, body {
-    user-select: none;
-  }
-  #model3D canvas {
-    user-select: none;
-  }
-  #chart hr {
-    width: 1px;
-    height: 1px;
-    clear: none;
-    border: 0;
-    padding:0;
-    display: inline-block;
-    position: relative;
-    vertical-align: top;
-    margin-top:32px;
-  }
-  #chart {
-    padding:0;
-    margin:0;
-    width:256px;
-    height:64px;
-    background-color:#808080;
-    touch-action: none;
-  }
-    #compass_box {
-      position:absolute;
-	  top:2em;
-	  right:3px;
-	  border:1px dashed gray;
-	  border-radius: 50%;
-	  width:1.5em;
-	  height:1.5em;
-	  padding:0;
-	  margin:0;
-	}
-	#compass {
-	  position:absolute;
-	  transform:rotate(0deg);
-	  border:1px solid black;
-	  border-radius: 50%;
-	  width:100%;
-	  height:100%;
-	  padding:0;
-	  margin:0;
-	  line-height:1em;
-	  letter-spacing:0;
-	}
-    #compass b {
-      margin-top:-1px;
-    }
-            </style>
-            """)
-            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.getElementById(\"blur_in\").getElementsByTagName(\"textarea\")[0].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 if (div > 1) {
-                    div--;
-                  }
-                }
-                pts_a[i] = Math.round((avg / div - 1) / 2) * 2 + 1;
-
-                document.getElementById(\"chart\").childNodes[i].style.height = pts_a[i] + \"px\";
-                document.getElementById(\"chart\").childNodes[i].style.marginTop = (64-pts_a[i])/2 + \"px\";
-              }
-              document.getElementById(\"blur_in\").getElementsByTagName(\"textarea\")[0].value = pts_a.join(\" \");
-
-              var evt = document.createEvent(\"Event\");
-              evt.initEvent(\"input\", true, false);
-              document.getElementById(\"blur_in\").getElementsByTagName(\"textarea\")[0].dispatchEvent(evt);
-            ' oninput='
-              this.parentNode.childNodes[2].innerText = this.value;
-            ' onchange='this.click();'/><span>1</span>""")
-            with gr.Accordion(label="Levels", open=False):
-              blur_in = gr.Textbox(elem_id="blur_in", label="Kernel size", show_label=False, interactive=False, value=blurin)
-          with gr.Group():
-            with gr.Accordion(label="Locations", open=False):
-              output_frame.select(fn=select_frame, inputs=[output_mask], outputs=[output_mask, selected])
-              example_coords = """[
-                  {"lat": 50.07379596793083, "lng": 14.437146122950555, "heading": 152.70303, "pitch": 2.607833999999997}, 
-                  {"lat": 50.073799567020004, "lng": 14.437146774240507, "heading": 151.12973, "pitch": 2.8672300000000064}, 
-                  {"lat": 50.07377647505558, "lng": 14.437161000659017, "heading": 151.41025, "pitch": 3.4802200000000028}, 
-                  {"lat": 50.07379496839027, "lng": 14.437148958238538, "heading": 151.93391, "pitch": 2.843050000000005}, 
-                  {"lat": 50.073823157821664, "lng": 14.437124189538856, "heading": 152.95769, "pitch": 4.233024999999998}
-                ]"""
-              coords = gr.Textbox(elem_id="coords", value=example_coords, label="Coordinates", interactive=False)
-              mesh_order = gr.Textbox(elem_id="order", value="", label="Order", interactive=False)
-            load_all = gr.Checkbox(label="Load all")
-
-          with gr.Group():
-            camera = gr.HTML(value="""<div style='width:128px;height:128px;border:1px dotted gray;padding:0;margin:0;float:left;clear:none;' id='seek'></div>
-            <span style='max-width:50%;float:right;clear:none;text-align:right;'>
-            <a href='#' id='reset_cam' style='float:right;clear:none;color:white' 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.activeCamera.alpha = 0;
-              BABYLON.Engine.LastCreatedScene.activeCamera.beta = Math.PI / 2;
-    
-              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(\" + Math.ceil(Math.log2(Math.PI/document.getElementById(\"zoom\").value))/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();}
-            '>Reset camera</a><br/>
-            <span><label for='zoom' style='width:8em'>Zoom</label><input id='zoom' type='range' style='width:128px;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.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></span><br/>
-            <span><label for='pan' style='width:8em'>Pan</label><input id='pan' type='range' style='width:128px;height:1em;' value='0' min='-16' max='16' 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);
-              }
-              parallax = this.value;
-              rdir = BABYLON.Engine.LastCreatedScene.activeCamera.getDirection(xdir);
-              videoDomeMesh.position.x = parallax * rdir.x;
-              videoDomeMesh.position.z = parallax * rdir.z;
-              this.parentNode.childNodes[2].innerText = parallax;
-            '/><span>0.0</span></span><br/>
-            <span><label for='contrast' style='width:8em'>Contrast</label><input id='contrast' type='range' style='width:128px;height:1em;' value='1.0' 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>1.0</span></span><br/>
-            <span><label for='exposure' style='width:8em'>Exposure</label><input id='exposure' type='range' style='width:128px;height:1em;' value='1.0' 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.exposure = this.value;
-              this.parentNode.childNodes[2].innerText = BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure;
-            '/><span>1.0</span></span><br/>
-              <a href='#' onclick='snapshot();'>Screenshot</a>
-              <a href='#' onclick='record_video();'>Record</a>
-              <a href='#' onclick='stop_recording();'>Stop rec.</a>
-              <a href='#' onclick='videoPlay();'>Play</a></span>""")
-            snapshot = gr.HTML(value="""<img src='' id='img_out' onload='var ctxt = document.getElementById(\"cnv_out\").getContext(\"2d\");ctxt.drawImage(this, 0, 0);'/><br/>
-              <canvas id='cnv_out'></canvas>
-              <div id='compass_box'><div id='compass'><a id='fullscreen' onclick='
-                const model3D = document.getElementById(\"model3D\");
-                if (model3D.parentNode.tagName != \"BODY\") {
-                  window.modelContainer = model3D.parentNode.id;
-                  document.body.appendChild(model3D);
-                  model3D.style.position = \"fixed\";
-                  model3D.style.left = \"0\";
-                  model3D.style.top = \"0\";
-                  model3D.style.zIndex = \"100\";
-                  document.getElementById(\"compass_box\").style.zIndex = \"101\";
-                } else {
-                  document.getElementById(window.modelContainer).appendChild(model3D);
-                  model3D.style.position = \"relative\";
-                  model3D.style.left = \"0\";
-                  model3D.style.top = \"0\";
-                  model3D.style.zIndex = \"initial\";
-                  document.getElementById(\"compass_box\").style.zIndex = \"initial\";
-                }'><b style='color:blue;'>◅</b>𝍠<b style='color:red;'>▻</b></a></div>
-              </div>
-            """)
-            render = gr.Button("Render")
-            input_json.input(show_json, inputs=[input_json], outputs=[processed_video, processed_zip, output_frame, output_mask, output_depth, coords])
-
-    
-    def on_submit(uploaded_video,model_type,blur_in,boffset,bsize,coordinates):
-        global locations
-        locations = []
-        avg = [0, 0]
-        
-        locations = json.loads(coordinates)
-        for k, location in enumerate(locations):
-            if "tiles" in locations[k]:
-                locations[k]["heading"] = locations[k]["tiles"]["originHeading"]
-                locations[k]["pitch"] = locations[k]["tiles"]["originPitch"]
-            elif not "heading" in locations[k] or not "pitch" in locations[k]:
-                locations[k]["heading"] = 0.0
-                locations[k]["pitch"] = 0.0
-
-            if "location" in locations[k]:
-                locations[k] = locations[k]["location"]["latLng"]
-            elif not "lat" in locations[k] or not "lng" in locations[k]:
-                locations[k]["lat"] = 0.0
-                locations[k]["lng"] = 0.0
-                
-            avg[0] = avg[0] + locations[k]["lat"]
-            avg[1] = avg[1] + locations[k]["lng"]
-                
-        if len(locations) > 0:
-            avg[0] = avg[0] / len(locations)
-            avg[1] = avg[1] / len(locations)
-            
-        for k, location in enumerate(locations):
-            lat = vincenty((location["lat"], 0), (avg[0], 0)) * 1000
-            lng = vincenty((0, location["lng"]), (0, avg[1])) * 1000
-            locations[k]["lat"] = float(lat / 2.5 * 111 * np.sign(location["lat"]-avg[0]))
-            locations[k]["lng"] = float(lng / 2.5 * 111 * np.sign(location["lng"]-avg[1]))
-        print(locations)
-        # 2.5m is height of camera on google street view car, 
-        # distance from center of sphere to pavement roughly 255 - 144 = 111 units
-            
-        # Process the video and get the path of the output video
-        output_video_path = make_video(uploaded_video,encoder=model_type,blur_data=blurin,o=boffset,b=bsize)
-
-        return output_video_path + (json.dumps(locations),)
-
-    submit.click(on_submit, inputs=[input_video, model_type, blur_in, boffset, bsize, coords], outputs=[processed_video, processed_zip, output_frame, output_mask, output_depth, depth_video, coords])
-    render.click(None, inputs=[coords, mesh_order, output_frame, output_mask, selected, output_depth, output_switch], outputs=None, js=load_model)
-    render.click(partial(get_mesh), inputs=[output_frame, output_mask, blur_in, load_all], outputs=[result, mesh_order])
-
-    example_files = [["./examples/streetview.mp4", "vits", blurin, 1, 32, example_coords]]
-    examples = gr.Examples(examples=example_files, fn=on_submit, cache_examples=True, inputs=[input_video, model_type, blur_in, boffset, bsize, coords], outputs=[processed_video, processed_zip, output_frame, output_mask, output_depth, depth_video, coords])
-    
-
-if __name__ == '__main__':
-    demo.queue().launch()