Spaces:
Running
Running
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" | |
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() |