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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
from vincenty import vincenty
import json
#import DracoPy
from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
from moviepy.editor import *
edge = []
gradient = None
params = { "fnum":0, "l":16 }
dcolor = []
pcolors = []
frame_selected = 0
frames = []
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_video(frames, fps, type):
print("building video result")
clip = ImageSequenceClip(frames, fps=fps)
clip.write_videofile(type + "_result.mp4", fps=fps)
return type + "_result.mp4"
@torch.no_grad()
def predict_depth(model, image):
return model(image)["depth"]
#@spaces.GPU
def make_video(video_path, outdir='./vis_video_depth', encoder='vits'):
if encoder not in ["vitl","vitb","vits"]:
encoder = "vits"
mapper = {"vits":"small","vitb":"base","vitl":"large"}
# DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
# model = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(DEVICE).eval()
# Define path for temporary processed frames
temp_frame_dir = tempfile.mkdtemp()
margin_width = 50
to_tensor_transform = transforms.ToTensor()
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
# depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{}14'.format(encoder)).to(DEVICE).eval()
depth_anything = pipeline(task = "depth-estimation", model=f"nielsr/depth-anything-{mapper[encoder]}")
# total_params = sum(param.numel() for param in depth_anything.parameters())
# print('Total parameters: {:.2f}M'.format(total_params / 1e6))
transform = Compose([
Resize(
width=518,
height=518,
resize_target=False,
keep_aspect_ratio=True,
ensure_multiple_of=14,
resize_method='lower_bound',
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
])
if os.path.isfile(video_path):
if video_path.endswith('txt'):
with open(video_path, 'r') as f:
lines = f.read().splitlines()
else:
filenames = [video_path]
else:
filenames = os.listdir(video_path)
filenames = [os.path.join(video_path, filename) for filename in filenames if not filename.startswith('.')]
filenames.sort()
# os.makedirs(outdir, exist_ok=True)
for k, filename in enumerate(filenames):
file_size = os.path.getsize(filename)/1024/1024
if file_size > 128.0:
print(f'File size of {filename} larger than 128Mb, sorry!')
return filename
print('Progress {:}/{:},'.format(k+1, len(filenames)), 'Processing', filename)
raw_video = cv2.VideoCapture(filename)
frame_width, frame_height = int(raw_video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(raw_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
frame_rate = int(raw_video.get(cv2.CAP_PROP_FPS))
if frame_rate < 1:
frame_rate = 1
cframes = int(raw_video.get(cv2.CAP_PROP_FRAME_COUNT))
print(f'frames: {cframes}, fps: {frame_rate}')
# output_width = frame_width * 2 + margin_width
#filename = os.path.basename(filename)
# output_path = os.path.join(outdir, filename[:filename.rfind('.')] + '_video_depth.mp4')
#with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmpfile:
# output_path = tmpfile.name
#out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"avc1"), frame_rate, (output_width, frame_height))
#fourcc = cv2.VideoWriter_fourcc(*'mp4v')
#out = cv2.VideoWriter(output_path, fourcc, frame_rate, (output_width, frame_height))
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}.png", raw_frame)
orig_frames.append(f"f{count}.png")
cv2.imwrite(f"f{count}_dmap.png", depth_color)
depth_frames.append(f"f{count}_dmap.png")
masks.append(f"f{count}_dmap.png")
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 gradient
global frame_selected
global depths
global frames
frames = orig_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 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
"""
depth[depth.shape[0]-1:depth.shape[0], 0:depth.shape[1]] = 255
mask = cv2.inRange(depth, 192, 255)
depth[mask>0] = 255
mask = cv2.inRange(depth[int(depth.shape[0]/8*6.5):depth.shape[0]-1, 0:depth.shape[1]], 160, 254)
depth[int(depth.shape[0]/8*6.5):depth.shape[0]-1, 0:depth.shape[1]][mask>0] = 160
# 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 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.cvtColor(depth["background"], cv2.COLOR_RGBA2RGB)
blur_img = blur_image(image[fnum][0], 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
#cverts = []
#i=0
#j=0
#while i<len(pcolors):
#cverts.append([])
#while j<len(clrs):
#if (clrs[j] == pcolors[i]).all():
#cverts[i].append(verts[j])
#j=j+1
mesh.append(trimesh.PointCloud(verts, colors=clrs))
print(mesh[len(mesh)-1])
mesh[len(mesh)-1].merge_vertices()
#i=i+1
mesh_n.append(str(fnum))
#triangles = create_triangles(rgba.shape[0], rgba.shape[1])
#rgba_pil = Image.fromarray(rgba.astype(np.uint8))
#material = trimesh.visual.texture.SimpleMaterial(image=rgba_pil) #, ambient=[0,0,0,0], diffuse=[0,0,0,0], specular=[0,0,0,0], glossiness=1.0)
#visuals = trimesh.visual.TextureVisuals(uv=uvs, image=rgba_pil, material=material)
#mesh.append(trimesh.Trimesh(vertices=verts, faces=triangles, vertex_normals=normals, visual=visuals, validate=True, process=False))
#print('triangles - ok')
#ln = []
#cr = []
#k = 1
#while k < len(verts):
# ln.append(k)
# cr.append(clrs[k])
# k=k+1
#print(len(ln))
#print(len(cr))
#mesh.append(trimesh.path.path.Path3D(entities=[trimesh.path.entities.Line(ln)], vertices=verts, colors=[[0,0,255,255]]))
#mesh.append(trimesh.PointCloud(verts, colors=clrs))
#mesh[len(mesh)-1].merge_vertices()
#mesh_n.append(str(fnum))
#binary = DracoPy.encode(verts, colors=clrs.astype(np.uint8))
#theta = locations[fnum]["heading"] / 180 * np.pi
#phi = locations[fnum]["pitch"] / 180 * np.pi
#center = [0, 0, 0]
#r_matrix = trimesh.transformations.rotation_matrix(-phi, [0, 0, 1], center)
#mesh[len(mesh)-1].apply_transform(r_matrix)
#r_matrix = trimesh.transformations.rotation_matrix(theta, [0, 1, 0], center)
#mesh[len(mesh)-1].apply_transform(r_matrix)
#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)
#f = open(glb_file.name, "wb")
#f.write(binary)
#f.close()
scene.export(glb_file.name)
print('file - ok')
return glb_file.name, glb_file.name, ",".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
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"]
i=i+1
return data[0]["video"]["path"], data[1]["path"], data[2], data[3]["background"]["path"], data[4]
def select_frame(v, evt: gr.SelectData):
global dcolor
global frame_selected
global masks
global edge
if evt.index != frame_selected:
masks[frame_selected] = v
frame_selected = evt.index
edge = []
if len(dcolor) == 0:
bg = [127, 127, 127, 255]
else:
bg = "[" + str(dcolor[frame_selected])[1:-1] + ", 255]"
return masks[frame_selected], frame_selected, bg
def switch_rows(v):
global frames
global depths
if v == True:
print(depths[0])
return depths
else:
print(frames[0])
return frames
def optimize(v, d):
global pcolors
global dcolor
global frame_selected
global frames
global depths
if v == True:
ddepth = cv2.CV_16S
kernel_size = 3
l = 16
dcolor = []
for k, f in enumerate(frames):
frame = cv2.imread(frames[k]).astype(np.uint8)
# convert to np.float32
f = np.float32(frame.reshape((-1,3)))
# define criteria, number of clusters(K) and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 4, 1.0)
ret,label,center=cv2.kmeans(f,l,None,criteria,4,cv2.KMEANS_RANDOM_CENTERS)
# Now convert back into uint8, and make original image
center = np.uint8(center)
res = center[label.flatten()]
frame = res.reshape((frame.shape))
depth = cv2.imread(depths[k]).astype(np.uint8)
mask = cv2.cvtColor(depth, cv2.COLOR_RGB2GRAY)
dcolor.append(bincount(frame[mask==0]))
print(dcolor[k])
clrs = Image.fromarray(frame.astype(np.uint8)).convert('RGB').getcolors()
i=0
while i<len(clrs):
clrs[i] = list(clrs[i][1])
clrs[i].append(255)
i=i+1
print(clrs)
pcolors = clrs
#mask = cv2.convertScaleAbs(cv2.Laplacian(cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY), ddepth, ksize=kernel_size))
#mask[mask>0] = 255
#frame[mask==0] = (0, 0, 0)
cv2.imwrite(frames[k], frame)
#depth[mask==0] = (255,255,255)
mask = cv2.inRange(frame, np.array([dcolor[k][0]-8, dcolor[k][1]-8, dcolor[k][2]-8]), np.array([dcolor[k][0]+8, dcolor[k][1]+8, dcolor[k][2]+8]))
depth[mask>0] = (255,255,255)
depth[depth.shape[0]-1:depth.shape[0], 0:depth.shape[1]] = (160, 160, 160)
depth[0:1, 0:depth.shape[1]] = (0, 0, 0)
cv2.imwrite(depths[k], depth)
if d == False:
return frames, "[" + str(dcolor[frame_selected])[1:-1] + ", 255]"
else:
return depths, "[" + str(dcolor[frame_selected])[1:-1] + ", 255]"
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():
global frame_selected
global masks
global depths
global edge
edge = []
masks[frame_selected] = cv2.imread(depths[frame_selected]).astype(np.uint8)
cv2.imwrite(f"f{frame_selected}_dmap.png", masks[frame_selected])
return masks[frame_selected], f"f{frame_selected}_dmap.png"
def apply_mask(d, b):
global frame_selected
global masks
global depths
global edge
edge = []
mask = cv2.cvtColor(d["layers"][0], cv2.COLOR_RGBA2GRAY)
dilation = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * b + 1, 2 * b + 1), (b, b))
mask = cv2.dilate(mask, dilation)
b = b*2+1
dilation = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * b + 1, 2 * b + 1), (b, b))
dmask = cv2.dilate(mask, dilation)
d["background"][mask==255] = (255,255,255,255)
mask = cv2.inRange(d["background"], np.array([255,255,255,255]), np.array([255,255,255,255]))
for k, mk in enumerate(masks):
if k != frame_selected and k < len(depths):
masks[k] = cv2.imread(depths[k]).astype(np.uint8)
masks[k][dmask<255] = (255,255,255)
masks[frame_selected] = d["background"]
cv2.imwrite(f"f{frame_selected}_dmap.png", masks[frame_selected])
return masks[frame_selected], f"f{frame_selected}_dmap.png"
def draw_mask(l, t, v, d, evt: gr.EventData):
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))
scale = 1
delta = 0
ddepth = cv2.CV_16S
if len(edge) == 0 or params["fnum"] != frame_selected or params["l"] != l:
if len(edge) > 0:
d["background"] = cv2.imread(masks[frame_selected]).astype(np.uint8)
bg = cv2.cvtColor(d["background"], cv2.COLOR_RGBA2GRAY)
diff = (bg-cv2.cvtColor(gradient, cv2.COLOR_RGBA2GRAY)).astype(np.uint8)
mask = cv2.inRange(diff, -t, t)
#kernel = np.ones((c,c),np.float32)/(c*c)
#mask = cv2.filter2D(mask,-1,kernel)
grad = cv2.convertScaleAbs(cv2.Sobel(mask, ddepth, 1, 1, ksize=15-(t*2), scale=scale, delta=delta, borderType=cv2.BORDER_DEFAULT))
mask = mask + cv2.inRange(grad, 1, 255)
indices = np.arange(0,256) # List of all colors
divider = np.linspace(0,255,l+1)[1] # we get a divider
quantiz = np.int0(np.linspace(0,255,l)) # we get quantization colors
color_levels = np.clip(np.int0(indices/divider),0,l-1) # color levels 0,1,2..
palette = quantiz[color_levels]
#for i in range(l):
# bg[(bg >= i*255/l) & (bg < (i+1)*255/l)] = i*255/(l-1)
bg = cv2.convertScaleAbs(palette[bg]).astype(np.uint8) # Converting image back to uint
bg[mask>0] = 0
params["fnum"] = frame_selected
params["l"] = l
d["layers"][0] = cv2.cvtColor(bg, cv2.COLOR_GRAY2RGBA)
edge = bg.copy()
else:
bg = edge.copy()
x = points[len(points)-1][0]
y = points[len(points)-1][1]
mask = cv2.floodFill(bg, None, (x, y), 1, 0, int(t*256/l), (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)
mask = mask[1:mask.shape[0]-1, 1:mask.shape[1]-1]
d["layers"][0][mask>0] = (255,255,255,255)
#d["background"][mask>0] = (255,255,255,255)
return gr.ImageEditor(value=d)
load_model="""
async(f, c, o, b, p, d, n)=>{
if (document.getElementById("iframe3D")) {
document.getElementById("iframe3D").src = "index.htm?file=" + f.path;
document.getElementById("iframe3D").onload = function() {
document.getElementById("iframe3D").contentDocument.getElementById("coords").value = c;
document.getElementById("iframe3D").contentDocument.getElementById("order").value = o;
document.getElementById("iframe3D").contentDocument.getElementById("bgcolor").value = b;
document.getElementById("iframe3D").contentDocument.getElementById("bgimage").value = p[n].image.url;
document.getElementById("iframe3D").contentDocument.getElementById("bgdepth").value = d.background.url;
}
toggleDisplay("model");
}
}
"""
on_click="""
async(p, d, n)=>{
var intv = setInterval(function(){
if (document.getElementById("iframe3D")===null || typeof document.getElementById("iframe3D")==="undefined") {
if (BABYLON && BABYLON.Engine.LastCreatedScene) {
BABYLON.Engine.LastCreatedScene.onAfterRenderObservable.add(function() { //onDataLoadedObservable
var bg = JSON.parse(document.getElementById("bgcolor").getElementsByTagName("textarea")[0].value);
BABYLON.Engine.LastCreatedScene.getEngine().setHardwareScalingLevel(1.0);
for (var i=0; i<bg.length; i++) {
bg[i] /= 255;
}
BABYLON.Engine.LastCreatedScene.clearColor = new BABYLON.Color4(bg[0], bg[1], bg[2], bg[3]);
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();
//BABYLON.Engine.LastCreatedScene.activeCamera.inertia = 0.0;
});
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 = 2.0;
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure = 0.5;
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();}
var dome = new BABYLON.PhotoDome("dome", p[n].image.url,
{
resolution: 16,
size: 512
}, BABYLON.Engine.LastCreatedScene);
for(i = 0; i < BABYLON.Engine.LastCreatedScene.getMeshByName("dome_mesh")._children.length; i++) {
let mesh = BABYLON.Engine.LastCreatedScene.getMeshByName("dome_mesh")._children[i];
alert(mesh.name);
mesh.dispose(false, true);
}
BABYLON.Engine.LastCreatedScene.getMeshByName("dome_mesh").applyDisplacementMap(d.background.url, 0, 255, function(m){try{alert(BABYLON.Engine.Version);}catch(e){alert(e);}}, null, null, true, function(e){alert(e);});
try {
BABYLON.GLTF2Export.GLBAsync(BABYLON.Engine.LastCreatedScene, "fileName").then((glb) => {
document.getElementById("file3D").getElementsByTagName("a")[0].download = Object.keys(glb.glTFFiles)[0];
document.getElementById("file3D").getElementsByTagName("a")[0].innerText = Object.keys(glb.glTFFiles)[0];
document.getElementById("file3D").getElementsByTagName("a")[0].href = glb.glTFFiles[Object.keys(glb.glTFFiles)[0]];
});
} catch (e){alert(e);}
clearInterval(intv);
}
} else if (BABYLON) {
BABYLON = null;
document.getElementById("model3D").getElementsByTagName("canvas")[0].remove();
clearInterval(intv);
}
}, 40);
}
"""
js = """
async()=>{
console.log('Hi');
}
"""
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, js=js) as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown("### Video Depth Prediction demo")
with gr.Row():
with gr.Column():
input_json = gr.Textbox(elem_id="json_in", value="{}", label="JSON")
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")
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])
optimize_switch = gr.Checkbox(label="Optimize")
bgcolor = gr.Textbox(elem_id="bgcolor", value="[127, 127, 127, 255]", label="Background color", interactive=False)
optimize_switch.input(fn=optimize, inputs=[optimize_switch, output_switch], outputs=[output_frame, bgcolor])
output_mask = gr.ImageEditor(layers=False, sources=('upload', '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.Row():
selector = gr.HTML(value="""
<a href='#' id='selector' onclick='if (this.style.fontWeight!=\"bold\") {
this.style.fontWeight=\"bold\";
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].onpointerdown = function(e) {
document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].style.borderColor = \"#a0a0a0\";
}
document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].onpointerup = function(e) {
document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].style.borderColor = \"#ffffff\";
}
} else {
this.style.fontWeight=\"normal\";
document.getElementById(\"image_edit\").getElementsByTagName(\"canvas\")[0].onclick = null;
}' title='Select point' style='text-decoration:none;color:white;'>⊹ Select point</a> <a href='#' id='clear_select' onclick='
document.getElementById(\"mouse\").getElementsByTagName(\"textarea\")[0].value = \"[]\";
' title='Clear selection' style='text-decoration:none;color:white;'>✕ Clear</a>""")
apply = gr.Button("Apply", size='sm')
reset = gr.Button("Reset", size='sm')
depth_file = gr.File(label="Edited depth")
with gr.Accordion(label="Edge", open=False):
levels = gr.Slider(label="Color levels", value=16, maximum=32, minimum=2, step=1)
tolerance = gr.Slider(label="Tolerance", value=1, maximum=7, minimum=0, step=1)
bsize = gr.Slider(label="Border size", value=15, maximum=256, minimum=1, step=2)
mouse = gr.Textbox(elem_id="mouse", value="""[]""", interactive=False)
mouse.input(fn=draw_mask, show_progress="minimal", inputs=[levels, tolerance, mouse, output_mask], outputs=[output_mask])
apply.click(fn=apply_mask, inputs=[output_mask, bsize], outputs=[output_mask, depth_file])
reset.click(fn=reset_mask, inputs=None, outputs=[output_mask, depth_file])
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], zoom_speed=2.0, pan_speed=2.0, interactive=True, elem_id="model3D") #, display_mode="point_cloud"
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\").slice(0,5) == \"-1,0 \") {
document.getElementById(\"pl\").setAttribute(\"points\", document.getElementById(\"pl\").getAttribute(\"points\").slice(5));
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' stroke='url(#lg)' fill='none' stroke-width='3' stroke-linejoin='round' points='-1,0 0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 10,0 11,0 12,0 13,0 14,0 15,0 16,0 17,0 18,0 19,0 20,0 21,0 22,0 23,0 24,0 25,0 26,0 27,0 28,0 29,0 30,0 31,0 32,0 33,0 34,0 35,0 36,0 37,0 38,0 39,0 40,0 41,0 42,0 43,0 44,0 45,0 46,0 47,0 48,0 49,0 50,0 51,0 52,0 53,0 54,0 55,0 56,0 57,0 58,0 59,0 60,0 61,0 62,0 63,0 64,0 65,0 66,0 67,0 68,0 69,0 70,0 71,0 72,0 73,0 74,0 75,0 76,0 77,0 78,0 79,0 80,0 81,0 82,0 83,0 84,0 85,0 86,0 87,0 88,0 89,0 90,0 91,0 92,0 93,0 94,0 95,0 96,0 97,0 98,0 99,0 100,0 101,0 102,0 103,0 104,0 105,0 106,0 107,0 108,0 109,0 110,0 111,0 112,0 113,0 114,0 115,0 116,0 117,0 118,0 119,0 120,0 121,0 122,0 123,0 124,0 125,0 126,0 127,0 128,0 129,0 130,0 131,0 132,0 133,0 134,0 135,0 136,0 137,0 138,0 139,0 140,0 141,0 142,0 143,0 144,0 145,0 146,0 147,0 148,0 149,0 150,0 151,0 152,0 153,0 154,0 155,0 156,0 157,0 158,0 159,0 160,0 161,0 162,0 163,0 164,0 165,0 166,0 167,0 168,0 169,0 170,0 171,0 172,0 173,0 174,0 175,0 176,0 177,0 178,0 179,0 180,0 181,0 182,0 183,0 184,0 185,0 186,0 187,0 188,0 189,0 190,0 191,0 192,0 193,0 194,0 195,0 196,0 197,0 198,0 199,0 200,0 201,0 202,0 203,0 204,0 205,0 206,0 207,0 208,0 209,0 210,0 211,0 212,0 213,0 214,0 215,0 216,0 217,0 218,0 219,0 220,0 221,0 222,0 223,0 224,0 225,0 226,0 227,0 228,0 229,0 230,0 231,0 232,0 233,0 234,0 235,0 236,0 237,0 238,0 239,0 240,0 241,0 242,0 243,0 244,0 245,0 246,0 247,0 248,0 249,0 250,0 251,0 252,0 253,0 254,0 255,0'/>
</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(label="Kernel size", show_label=False, value="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")
with gr.Accordion(label="Locations", open=False):
offset = gr.HTML(value="""<input type='text' id='kbrd' onpointerdown='this.style.color = \"white\";' onpointerup='this.style.color = \"auto\";' onpointermove='
try {
if (this.style.color!=\"auto\" && 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;
}
const dir = BABYLON.Engine.LastCreatedScene.activeCamera.getForwardRay().direction;
dir.y = 0; dir.normalize();
const angle = BABYLON.Vector3.GetAngleBetweenVectors(dir, BABYLON.Vector3.Forward(), BABYLON.Vector3.Up());
const x = event.clientX-this.getBoundingClientRect().x-128;
const y = event.clientY-this.getBoundingClientRect().y-64;
const angle_ = Math.atan2(y, x);
const r = Math.sqrt(Math.pow(y,2) + Math.pow(x,2));
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z = r * Math.sin(-angle_-angle);
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x = r * Math.cos(-angle_-angle);
}
} catch(e) {alert(e)}
' 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;
}
try {
const dir = BABYLON.Engine.LastCreatedScene.activeCamera.getForwardRay().direction;
dir.y = 0; dir.normalize();
const angle = BABYLON.Vector3.GetAngleBetweenVectors(dir, BABYLON.Vector3.Forward(), BABYLON.Vector3.Up());
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\":
const x = -1; const y = 0;
const angle_ = Math.atan2(y, x);
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z += Math.sin(-angle_-angle);
this.value = \"a ⬅ d\";
break;
case \"d\":
const x = 1; const y = 0;
const angle_ = Math.atan2(y, x);
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z += Math.sin(-angle_-angle);
this.value = \"a ➡ d\";
break;
case \"e\":
const x = 0; const y = -1;
const angle_ = Math.atan2(y, x);
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x += Math.cos(-angle_-angle);
this.value = \"z ↗ e\";
break;
case \"z\":
const x = 0; const y = 1;
const angle_ = Math.atan2(y, x);
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x += Math.cos(-angle_-angle);
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 = \"\";
}
} catch(e) {alert(e)}
}
' style='height:128px;width:256px;user-select:none;touch-action:none;color:auto;background-color:transparent;border:1px solid gray;'/>
<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, bgcolor])
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)
result_file = gr.File(elem_id="file3D", label="3D file (.glb)")
result_file.change(fn=None, inputs=[result_file, coords, mesh_order, bgcolor, output_frame, output_mask, selected], outputs=None, js=load_model)
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(\" + 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();}
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")
input_json.input(show_json, inputs=[input_json], outputs=[processed_video, processed_zip, output_frame, output_mask, coords])
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):
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):
heading = locations[k]["tiles"]["originHeading"]
pitch = locations[k]["tiles"]["originPitch"]
locations[k] = locations[k]["location"]["latLng"]
locations[k]["heading"] = heading
locations[k]["pitch"] = pitch
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):
lat = vincenty((location["lat"], 0), (avg[0], 0)) * 1000
lng = vincenty((0, location["lng"]), (0, avg[1])) * 1000
locations[k]["lat"] = lat / 2.5 * 95 * np.sign(location["lat"]-avg[0])
locations[k]["lng"] = lng / 2.5 * 95 * np.sign(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 + (json.dumps(locations),)
submit.click(on_submit, inputs=[input_video, model_type, coords], outputs=[processed_video, processed_zip, output_frame, output_mask, coords])
render.click(None, inputs=[output_frame, output_mask, selected], outputs=None, js=on_click)
render.click(partial(get_mesh), inputs=[output_frame, output_mask, blur_in, load_all], outputs=[result, result_file, mesh_order])
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()