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import gradio as gr | |
import cv2 | |
from PIL import Image | |
import numpy as np | |
from transformers import pipeline | |
import os | |
import torch | |
import torch.nn.functional as F | |
from torchvision import transforms | |
from torchvision.transforms import Compose | |
import trimesh | |
from geometry import create_triangles | |
import tempfile | |
from functools import partial | |
import spaces | |
from zipfile import ZipFile | |
import json | |
from depth_anything.dpt import DepthAnything | |
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet | |
from moviepy.editor import * | |
frame_selected = 0 | |
locations = [] | |
mesh = [] | |
def zip_files(files_in, files_out): | |
with ZipFile("depth_result.zip", "w") as zipObj: | |
for idx, file in enumerate(files_in): | |
zipObj.write(file, file.split("/")[-1]) | |
for idx, file in enumerate(files_out): | |
zipObj.write(file, file.split("/")[-1]) | |
return "depth_result.zip" | |
def create_video(frames, fps, type): | |
print("building video result") | |
clip = ImageSequenceClip(frames, fps=fps) | |
clip.write_videofile(type + "_result.mp4", fps=fps) | |
return type + "_result.mp4" | |
def predict_depth(model, image): | |
return model(image)["depth"] | |
def make_video(video_path, outdir='./vis_video_depth', encoder='vits'): | |
if encoder not in ["vitl","vitb","vits"]: | |
encoder = "vits" | |
mapper = {"vits":"small","vitb":"base","vitl":"large"} | |
# DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' | |
# model = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(DEVICE).eval() | |
# Define path for temporary processed frames | |
temp_frame_dir = tempfile.mkdtemp() | |
margin_width = 50 | |
to_tensor_transform = transforms.ToTensor() | |
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' | |
# depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{}14'.format(encoder)).to(DEVICE).eval() | |
depth_anything = pipeline(task = "depth-estimation", model=f"nielsr/depth-anything-{mapper[encoder]}") | |
# total_params = sum(param.numel() for param in depth_anything.parameters()) | |
# print('Total parameters: {:.2f}M'.format(total_params / 1e6)) | |
transform = Compose([ | |
Resize( | |
width=518, | |
height=518, | |
resize_target=False, | |
keep_aspect_ratio=True, | |
ensure_multiple_of=14, | |
resize_method='lower_bound', | |
image_interpolation_method=cv2.INTER_CUBIC, | |
), | |
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
PrepareForNet(), | |
]) | |
if os.path.isfile(video_path): | |
if video_path.endswith('txt'): | |
with open(video_path, 'r') as f: | |
lines = f.read().splitlines() | |
else: | |
filenames = [video_path] | |
else: | |
filenames = os.listdir(video_path) | |
filenames = [os.path.join(video_path, filename) for filename in filenames if not filename.startswith('.')] | |
filenames.sort() | |
# os.makedirs(outdir, exist_ok=True) | |
for k, filename in enumerate(filenames): | |
file_size = os.path.getsize(filename)/1024/1024 | |
if file_size > 128.0: | |
print(f'File size of {filename} larger than 128Mb, sorry!') | |
return filename | |
print('Progress {:}/{:},'.format(k+1, len(filenames)), 'Processing', filename) | |
raw_video = cv2.VideoCapture(filename) | |
frame_width, frame_height = int(raw_video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(raw_video.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
frame_rate = int(raw_video.get(cv2.CAP_PROP_FPS)) | |
if frame_rate < 1: | |
frame_rate = 1 | |
cframes = int(raw_video.get(cv2.CAP_PROP_FRAME_COUNT)) | |
print(f'frames: {cframes}, fps: {frame_rate}') | |
# output_width = frame_width * 2 + margin_width | |
#filename = os.path.basename(filename) | |
# output_path = os.path.join(outdir, filename[:filename.rfind('.')] + '_video_depth.mp4') | |
#with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmpfile: | |
# output_path = tmpfile.name | |
#out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"avc1"), frame_rate, (output_width, frame_height)) | |
#fourcc = cv2.VideoWriter_fourcc(*'mp4v') | |
#out = cv2.VideoWriter(output_path, fourcc, frame_rate, (output_width, frame_height)) | |
count=0 | |
depth_frames = [] | |
orig_frames = [] | |
while raw_video.isOpened(): | |
ret, raw_frame = raw_video.read() | |
if not ret: | |
break | |
frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2RGB) / 255.0 | |
frame_pil = Image.fromarray((frame * 255).astype(np.uint8)) | |
frame = transform({'image': frame})['image'] | |
frame = torch.from_numpy(frame).unsqueeze(0).to(DEVICE) | |
depth = to_tensor_transform(predict_depth(depth_anything, frame_pil)) | |
depth = F.interpolate(depth[None], (frame_height, frame_width), mode='bilinear', align_corners=False)[0, 0] | |
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 | |
depth = depth.cpu().numpy().astype(np.uint8) | |
depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_BONE) | |
depth_gray = cv2.cvtColor(depth_color, cv2.COLOR_RGBA2GRAY) | |
depth_color = cv2.cvtColor(depth_gray, cv2.COLOR_GRAY2BGR) | |
# Remove white border around map: | |
# define lower and upper limits of white | |
white_lo = np.array([250,250,250]) | |
white_hi = np.array([255,255,255]) | |
# mask image to only select white | |
mask = cv2.inRange(depth_color, white_lo, white_hi) | |
# change image to black where we found white | |
depth_color[mask>0] = (0,0,0) | |
# split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255 | |
# combined_frame = cv2.hconcat([raw_frame, split_region, depth_color]) | |
# out.write(combined_frame) | |
# frame_path = os.path.join(temp_frame_dir, f"frame_{count:05d}.png") | |
# cv2.imwrite(frame_path, combined_frame) | |
cv2.imwrite(f"f{count}.jpg", raw_frame) | |
orig_frames.append(f"f{count}.jpg") | |
cv2.imwrite(f"f{count}_dmap.jpg", depth_color) | |
depth_frames.append(f"f{count}_dmap.jpg") | |
count += 1 | |
final_vid = create_video(depth_frames, frame_rate, "depth") | |
final_zip = zip_files(orig_frames, depth_frames) | |
raw_video.release() | |
# out.release() | |
cv2.destroyAllWindows() | |
return final_vid, final_zip, orig_frames, depth_frames #output_path | |
def depth_edges_mask(depth): | |
"""Returns a mask of edges in the depth map. | |
Args: | |
depth: 2D numpy array of shape (H, W) with dtype float32. | |
Returns: | |
mask: 2D numpy array of shape (H, W) with dtype bool. | |
""" | |
# Compute the x and y gradients of the depth map. | |
depth_dx, depth_dy = np.gradient(depth) | |
# Compute the gradient magnitude. | |
depth_grad = np.sqrt(depth_dx ** 2 + depth_dy ** 2) | |
# Compute the edge mask. | |
mask = depth_grad > 0.05 | |
return mask | |
def pano_depth_to_world_points(depth): | |
""" | |
360 depth to world points | |
given 2D depth is an equirectangular projection of a spherical image | |
Treat depth as radius | |
longitude : -pi to pi | |
latitude : -pi/2 to pi/2 | |
""" | |
# Convert depth to radius | |
radius = (255 - depth.flatten()) | |
lon = np.linspace(0, np.pi*2, depth.shape[1]) | |
lat = np.linspace(0, np.pi, depth.shape[0]) | |
lon, lat = np.meshgrid(lon, lat) | |
lon = lon.flatten() | |
lat = lat.flatten() | |
pts3d = [[0,0,0]] | |
uv = [[0,0]] | |
for i in range(0, 1): #(0,2) | |
for j in range(0, 1): #(0,2) | |
#rnd_lon = (np.random.rand(depth.shape[0]*depth.shape[1]) - 0.5) / 8 | |
#rnd_lat = (np.random.rand(depth.shape[0]*depth.shape[1]) - 0.5) / 8 | |
d_lon = lon + i/2 * np.pi*2 / depth.shape[1] | |
d_lat = lat + j/2 * np.pi / depth.shape[0] | |
# Convert to cartesian coordinates | |
x = radius * np.cos(d_lon) * np.sin(d_lat) | |
y = radius * np.cos(d_lat) | |
z = radius * np.sin(d_lon) * np.sin(d_lat) | |
pts = np.stack([x, y, z], axis=1) | |
uvs = np.stack([lon, lat], axis=1) | |
pts3d = np.concatenate((pts3d, pts), axis=0) | |
uv = np.concatenate((uv, uvs), axis=0) | |
#print(f'i: {i}, j: {j}') | |
j = j+1 | |
i = i+1 | |
return [pts3d, uv] | |
def rgb2gray(rgb): | |
return np.dot(rgb[...,:3], [0.333, 0.333, 0.333]) | |
def get_mesh(image, depth, blur_data, loadall): | |
global locations | |
global mesh | |
if loadall == False: | |
mesh = [] | |
fnum = frame_selected | |
blur_img = blur_image(image[fnum][0], depth[fnum][0], blur_data) | |
gdepth = rgb2gray(depth[fnum][0]) | |
print('depth to gray - ok') | |
points = pano_depth_to_world_points(gdepth) | |
pts3d = points[0] | |
uv = points[1] | |
print('radius from depth - ok') | |
# Create a trimesh mesh from the points | |
# Each pixel is connected to its 4 neighbors | |
# colors are the RGB values of the image | |
verts = pts3d.reshape(-1, 3) | |
#triangles = create_triangles(image.shape[0], image.shape[1]) | |
#print('triangles - ok') | |
rgba = cv2.cvtColor(blur_img, cv2.COLOR_RGB2RGBA) | |
colors = rgba.reshape(-1, 4) | |
clrs = [[128, 128, 128, 0]] | |
for i in range(0,1): #(0,4) | |
clrs = np.concatenate((clrs, colors), axis=0) | |
i = i+1 | |
#mesh = trimesh.Trimesh(vertices=verts, faces=triangles, vertex_colors=colors) | |
mesh.append(trimesh.PointCloud(verts, colors=clrs)) | |
#material = trimesh.visual.texture.SimpleMaterial(image=image) | |
#texture = trimesh.visual.TextureVisuals(uv=uv, image=image, material=material) | |
#mesh.visual = texture | |
scene = trimesh.Scene(mesh) | |
print('mesh - ok') | |
# Save as glb | |
glb_file = tempfile.NamedTemporaryFile(suffix='.glb', delete=False) | |
glb_path = glb_file.name | |
scene.export(glb_path) | |
print('file - ok') | |
return glb_path | |
def blur_image(image, depth, blur_data): | |
blur_a = blur_data.split() | |
print(f'blur data {blur_data}') | |
blur_frame = image.copy() | |
j = 0 | |
while j < 256: | |
i = 255 - j | |
blur_lo = np.array([i,i,i]) | |
blur_hi = np.array([i+1,i+1,i+1]) | |
blur_mask = cv2.inRange(depth, blur_lo, blur_hi) | |
print(f'kernel size {int(blur_a[j])}') | |
blur = cv2.GaussianBlur(image, (int(blur_a[j]), int(blur_a[j])), 0) | |
blur_frame[blur_mask>0] = blur[blur_mask>0] | |
j = j + 1 | |
return blur_frame | |
def loadurl(url): | |
return url | |
def select_frame(evt: gr.SelectData): | |
global frame_selected | |
if evt.index != frame_selected: | |
frame_selected = evt.index | |
return gr.Gallery(selected_index=evt.index, preview=True), frame_selected | |
css = """ | |
#img-display-container { | |
max-height: 100vh; | |
} | |
#img-display-input { | |
max-height: 80vh; | |
} | |
#img-display-output { | |
max-height: 80vh; | |
} | |
""" | |
title = "# Depth Anything Video Demo" | |
description = """Depth Anything on full video files. | |
Please refer to our [paper](https://arxiv.org/abs/2401.10891), [project page](https://depth-anything.github.io), or [github](https://github.com/LiheYoung/Depth-Anything) for more details. | |
Mesh rendering from [ZoeDepth](https://huggingface.co/spaces/shariqfarooq/ZoeDepth) ([github](https://github.com/isl-org/ZoeDepth/tree/main/ui)).""" | |
transform = Compose([ | |
Resize( | |
width=518, | |
height=518, | |
resize_target=False, | |
keep_aspect_ratio=True, | |
ensure_multiple_of=14, | |
resize_method='lower_bound', | |
image_interpolation_method=cv2.INTER_CUBIC, | |
), | |
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
PrepareForNet(), | |
]) | |
# @torch.no_grad() | |
# def predict_depth(model, image): | |
# return model(image) | |
with gr.Blocks(css=css) as demo: | |
gr.Markdown(title) | |
gr.Markdown(description) | |
gr.Markdown("### Video Depth Prediction demo") | |
with gr.Row(): | |
with gr.Column(): | |
input_url = gr.Textbox(value="./examples/streetview.mp4", label="URL") | |
input_video = gr.Video(label="Input Video", format="mp4") | |
input_url.change(fn=loadurl, inputs=[input_url], outputs=[input_video]) | |
output_frame = gr.Gallery(label="Frame", type='numpy', preview=True, columns=8192) | |
output_depth = gr.Gallery(label="Depth", type='numpy', preview=True, columns=8192, interactive=False) | |
submit = gr.Button("Submit") | |
with gr.Column(): | |
model_type = gr.Dropdown([("small", "vits"), ("base", "vitb"), ("large", "vitl")], type="value", value="vits", label='Model Type') | |
processed_video = gr.Video(label="Output Video", format="mp4") | |
processed_zip = gr.File(label="Output Archive") | |
result = gr.Model3D(label="3D Mesh", clear_color=[0.5, 0.5, 0.5, 0.0], camera_position=[0, 90, 0], interactive=True, elem_id="model3D") | |
svg_in = gr.HTML(value="""<svg id='svg_in' height='32' width='256' viewBox='0 0 256 32' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' style='touch-action:none;background-color:#808080;' onpointerdown=' | |
try{ | |
if (document.getElementById(\"pl\").getAttribute(\"points\").length < 256) { | |
var pts = \"\"; | |
for (var i=0; i<256; i++) { | |
pts += i+\",0 \"; | |
} | |
document.getElementById(\"pl\").setAttribute(\"points\", pts.slice(0,-1)); | |
var xold = 0; | |
var yold = 0; | |
var x = 0; | |
var y = 0; | |
function lerp(y1, y2, mu) { return y1*(1-mu)+y2*mu; } | |
this.onpointermove = function(event) { | |
if (this.title != \"\") { | |
x = parseInt(event.clientX - this.getBoundingClientRect().x); | |
y = parseInt(event.clientY - this.getBoundingClientRect().y); | |
if (x < 0) { x = 0; } else if (x > 255) { x = 255; } | |
if (y < 0) { y = 0; } else if (y > 31) { y = 31; } | |
var pl_a = document.getElementById(\"pl\").getAttribute(\"points\").split(\" \"); | |
for (var i=Math.min(xold, x)+1; i<Math.max(xold, x); i++) { | |
pl_a[i] = x+\",\"+parseInt(lerp( yold, y, (i-xold)/(x-xold) )); | |
} | |
pl_a[x] = x+\",\"+y; | |
xold = x; | |
yold = y; | |
document.getElementById(\"pl\").setAttribute(\"points\", pl_a.join(\" \")); | |
} | |
} | |
this.onpointerup = function(event) { | |
var pl_a = document.getElementById(\"pl\").getAttribute(\"points\").replace(/\d+,/g, \"\").split(\" \"); | |
for (var i=0; i<pl_a.length; i++) { | |
pl_a[i] = parseInt(pl_a[i]) * 2 + 1; | |
} | |
document.getElementsByTagName(\"textarea\")[1].value = pl_a.join(\" \"); | |
var evt = document.createEvent(\"Event\"); | |
evt.initEvent(\"input\", true, false); | |
document.getElementsByTagName(\"textarea\")[1].dispatchEvent(evt); | |
this.title = \"\"; | |
} | |
this.onpointerleave = function(event) { | |
this.title = \"\"; | |
} | |
this.onpointerdown = function(event) { | |
xold = parseInt(event.clientX - this.getBoundingClientRect().x); | |
yold = parseInt(event.clientY - this.getBoundingClientRect().y); | |
this.title = xold+\",\"+yold; | |
} | |
} | |
}catch(e){alert(e);} | |
'> | |
<defs> | |
<linearGradient id='lg' x1='0%' x2='100%' y1='0%' y2='0%'> | |
<stop offset='0%' stop-color='white'/> | |
<stop offset='100%' stop-color='black'/> | |
</linearGradient> | |
</defs> | |
<polyline id='pl' points='-3,0 0,15 255,15 258,0' stroke='url(#lg)' fill='none' stroke-width='3' stroke-linejoin='round'/> | |
</svg>""") | |
average = gr.HTML(value="""<label for='average'>Average</label><input id='average' type='range' style='width:256px;height:1em;' value='1' min='1' max='15' step='2' onclick=' | |
var pts_a = document.getElementsByTagName(\"textarea\")[1].value.split(\" \"); | |
for (var i=0; i<256; i++) { | |
var avg = 0; | |
var div = this.value; | |
for (var j = i-parseInt(this.value/2); j <= i+parseInt(this.value/2); j++) { | |
if (pts_a[j]) { | |
avg += parseInt(pts_a[j]); | |
} else { | |
div--; | |
} | |
} | |
pts_a[i] = parseInt((avg / div - 1) / 2) * 2 + 1; | |
} | |
document.getElementsByTagName(\"textarea\")[1].value = pts_a.join(\" \"); | |
for (var i=0; i<pts_a.length; i++) { | |
pts_a[i] = i+\",\"+parseInt((pts_a[i] - 1) / 2); | |
} | |
document.getElementById(\"pl\").setAttribute(\"points\", pts_a.join(\" \")); | |
var evt = document.createEvent(\"Event\"); | |
evt.initEvent(\"input\", true, false); | |
document.getElementsByTagName(\"textarea\")[1].dispatchEvent(evt); | |
' oninput=' | |
this.parentNode.childNodes[2].innerText = this.value; | |
'/><span>1</span>""") | |
with gr.Accordion(label="Blur levels", open=False): | |
blur_in = gr.Textbox(value="", label="Kernel size", show_label=False) | |
with gr.Accordion(label="Locations", open=False): | |
offset = gr.HTML(value="""<input type='text' id='kbrd' onkeydown=' | |
if (BABYLON) { | |
if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
var evt = document.createEvent(\"Event\"); | |
evt.initEvent(\"click\", true, false); | |
document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
} | |
event.preventDefault(); | |
if (BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotationQuaternion) { | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotationQuaternion = null; | |
} | |
switch(event.key) { | |
case \"w\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.y += 1; | |
this.value = \"w ⬆ x\"; | |
break; | |
case \"x\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.y -= 1; | |
this.value = \"w ⬇ x\"; | |
break; | |
case \"a\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z -= 1; | |
this.value = \"a ⬅ d\"; | |
break; | |
case \"d\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z += 1; | |
this.value = \"a ➡ d\"; | |
break; | |
case \"e\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x -= 1; | |
this.value = \"z ↗ e\"; | |
break; | |
case \"z\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x += 1; | |
this.value = \"z ↙ e\"; | |
break; | |
case \"s\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.x = 0; | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.y = 0; | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].position.z = 0; | |
this.value = \"\"; | |
break; | |
case \"t\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.z += Math.PI/256; | |
this.value = \"t 🔃 b\"; | |
break; | |
case \"b\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.z -= Math.PI/256; | |
this.value = \"t 🔃 b\"; | |
break; | |
case \"f\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.y -= Math.PI/256; | |
this.value = \"f 🔁 h\"; | |
break; | |
case \"h\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.y += Math.PI/256; | |
this.value = \"f 🔁 h\"; | |
break; | |
case \"y\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.x -= Math.PI/256; | |
this.value = \"v 🔄 y\"; | |
break; | |
case \"v\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.x += Math.PI/256; | |
this.value = \"v 🔄 y\"; | |
break; | |
case \"g\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.x = 0; | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.y = 0; | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].rotation.z = 0; | |
this.value = \"\"; | |
break; | |
case \"i\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.y *= 256/255; | |
this.value = \"i ↕ ,\"; | |
break; | |
case \",\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.y /= 256/255; | |
this.value = \"i ↕ ,\"; | |
break; | |
case \"j\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.z /= 256/255; | |
this.value = \"j ↔ l\"; | |
break; | |
case \"l\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.z *= 256/255; | |
this.value = \"j ↔ l\"; | |
break; | |
case \"o\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.x /= 256/255; | |
this.value = \"m ⤢ o\"; | |
break; | |
case \"m\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.x *= 256/255; | |
this.value = \"m ⤢ o\"; | |
break; | |
case \"k\": | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.x = 1; | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.y = 1; | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].scaling.z = 1; | |
this.value = \"\"; | |
break; | |
default: | |
this.value = \"\"; | |
} | |
} | |
' style='color:auto;background-color:transparent;border:1px solid lightgray;'/><pre id='keymap'> | |
` 1 2 3 4 5 6 7 8 9 0 - = | |
W E T Y I O { } | |
A-`S´-D F-`G´-H J-`K´-L ; ' | |
Z´ X̀ V´ B̀ M´ `, . / | |
<a id='move' href='#'>move</a> <a id='rotate' href='#'>rotate</a> <a id='scale' href='#'>scale</a> | |
</pre>""") | |
selected = gr.Number(elem_id="fnum", value=0, minimum=0, maximum=256, interactive=False) | |
output_frame.select(fn=select_frame, inputs=None, outputs=[output_depth, selected], show_progress='hidden') | |
output_depth.select(fn=select_frame, inputs=None, outputs=[output_frame, selected], show_progress='hidden') | |
example_coords = """[ | |
{"latLng": { "lat": 50.07379596793083, "lng": 14.437146122950555 } }, | |
{"latLng": { "lat": 50.073799567020004, "lng": 14.437146774240507 } }, | |
{"latLng": { "lat": 50.07377647505558, "lng": 14.437161000659017 } }, | |
{"latLng": { "lat": 50.07379496839027, "lng": 14.437148958238538 } }, | |
{"latLng": { "lat": 50.073823157821664, "lng": 14.437124189538856 } } | |
]""" | |
coords = gr.JSON(elem_id="coords", value=example_coords, label="Precise coordinates", show_label=False) | |
html = gr.HTML(value="""<label for='zoom'>Zoom</label><input id='zoom' type='range' style='width:256px;height:1em;' value='0.8' min='0.157' max='1.57' step='0.001' oninput=' | |
if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
var evt = document.createEvent(\"Event\"); | |
evt.initEvent(\"click\", true, false); | |
document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
} | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].material.pointSize = Math.ceil(Math.log2(Math.PI/this.value)); | |
BABYLON.Engine.LastCreatedScene.activeCamera.fov = this.value; | |
this.parentNode.childNodes[2].innerText = BABYLON.Engine.LastCreatedScene.activeCamera.fov; | |
document.getElementById(\"model3D\").getElementsByTagName(\"canvas\")[0].style.filter = \"blur(\" + BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].material.pointSize/2.0*Math.sqrt(2.0) + \"px)\"; | |
'/><span>0.8</span>""") | |
camera = gr.HTML(value="""<a href='#' id='reset_cam' onclick=' | |
if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata = { | |
screenshot: true, | |
pipeline: new BABYLON.DefaultRenderingPipeline(\"default\", true, BABYLON.Engine.LastCreatedScene, [BABYLON.Engine.LastCreatedScene.activeCamera]) | |
} | |
} | |
BABYLON.Engine.LastCreatedScene.activeCamera.radius = 0; | |
BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].material.pointSize = Math.ceil(Math.log2(Math.PI/document.getElementById(\"zoom\").value)); | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.samples = 4; | |
BABYLON.Engine.LastCreatedScene.activeCamera.fov = document.getElementById(\"zoom\").value; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.contrast = document.getElementById(\"contrast\").value; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure = document.getElementById(\"exposure\").value; | |
document.getElementById(\"model3D\").getElementsByTagName(\"canvas\")[0].style.filter = \"blur(\" + BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1].material.pointSize/2.0*Math.sqrt(2.0) + \"px)\"; | |
if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager) { | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager = new BABYLON.GizmoManager(BABYLON.Engine.LastCreatedScene, 8); | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.positionGizmoEnabled = true; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.rotationGizmoEnabled = false; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.scaleGizmoEnabled = false; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.boundingBoxGizmoEnabled = false; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.usePointerToAttachGizmos = false; | |
document.getElementById(\"move\").onclick = function(event) { | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.positionGizmoEnabled = true; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.rotationGizmoEnabled = false; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.scaleGizmoEnabled = false; | |
} | |
document.getElementById(\"rotate\").onclick = function(event) { | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.positionGizmoEnabled = false; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.rotationGizmoEnabled = true; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.scaleGizmoEnabled = false; | |
} | |
document.getElementById(\"scale\").onclick = function(event) { | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.positionGizmoEnabled = false; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.rotationGizmoEnabled = false; | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.scaleGizmoEnabled = true; | |
} | |
} | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.gizmoManager.attachToMesh(BABYLON.Engine.LastCreatedScene.getNodes()[parseInt(document.getElementById(\"fnum\").getElementsByTagName(\"input\")[0].value)+1]); | |
'>reset camera</a>""") | |
contrast = gr.HTML(value="""<label for='contrast'>Contrast</label><input id='contrast' type='range' style='width:256px;height:1em;' value='2.0' min='0' max='2' step='0.001' oninput=' | |
if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
var evt = document.createEvent(\"Event\"); | |
evt.initEvent(\"click\", true, false); | |
document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
} | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.contrast = this.value; | |
this.parentNode.childNodes[2].innerText = BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.contrast; | |
'/><span>2.0</span>""") | |
exposure = gr.HTML(value="""<label for='exposure'>Exposure</label><input id='exposure' type='range' style='width:256px;height:1em;' value='0.5' min='0' max='2' step='0.001' oninput=' | |
if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
var evt = document.createEvent(\"Event\"); | |
evt.initEvent(\"click\", true, false); | |
document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
} | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure = this.value; | |
this.parentNode.childNodes[2].innerText = BABYLON.Engine.LastCreatedScene.activeCamera.metadata.pipeline.imageProcessing.exposure; | |
'/><span>0.5</span>""") | |
canvas = gr.HTML(value="""<a href='#' onclick=' | |
if (!BABYLON.Engine.LastCreatedScene.activeCamera.metadata) { | |
var evt = document.createEvent(\"Event\"); | |
evt.initEvent(\"click\", true, false); | |
document.getElementById(\"reset_cam\").dispatchEvent(evt); | |
} | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.screenshot = true; | |
BABYLON.Engine.LastCreatedScene.getEngine().onEndFrameObservable.add(function() { | |
if (BABYLON.Engine.LastCreatedScene.activeCamera.metadata.screenshot === true) { | |
BABYLON.Engine.LastCreatedScene.activeCamera.metadata.screenshot = false; | |
try { | |
BABYLON.Tools.CreateScreenshotUsingRenderTarget(BABYLON.Engine.LastCreatedScene.getEngine(), BABYLON.Engine.LastCreatedScene.activeCamera, | |
{ precision: 1.0 }, (durl) => { | |
var cnvs = document.getElementById(\"model3D\").getElementsByTagName(\"canvas\")[0]; //.getContext(\"webgl2\"); | |
var svgd = `<svg id=\"svg_out\" viewBox=\"0 0 ` + cnvs.width + ` ` + cnvs.height + `\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> | |
<defs> | |
<filter id=\"blur\" x=\"0\" y=\"0\" xmlns=\"http://www.w3.org/2000/svg\"> | |
<feGaussianBlur in=\"SourceGraphic\" stdDeviation=\"` + BABYLON.Engine.LastCreatedScene.getNodes()[1].material.pointSize/2.0*Math.sqrt(2.0) + `\" /> | |
</filter> | |
</defs> | |
<image filter=\"url(#blur)\" id=\"svg_img\" x=\"0\" y=\"0\" width=\"` + cnvs.width + `\" height=\"` + cnvs.height + `\" xlink:href=\"` + durl + `\"/> | |
</svg>`; | |
document.getElementById(\"cnv_out\").width = cnvs.width; | |
document.getElementById(\"cnv_out\").height = cnvs.height; | |
document.getElementById(\"img_out\").src = \"data:image/svg+xml;base64,\" + btoa(svgd); | |
} | |
); | |
} catch(e) { alert(e); } | |
// https://forum.babylonjs.com/t/best-way-to-save-to-jpeg-snapshots-of-scene/17663/11 | |
} | |
}); | |
'/>snapshot</a><br/><img src='' id='img_out' onload=' | |
var ctxt = document.getElementById(\"cnv_out\").getContext(\"2d\"); | |
ctxt.drawImage(this, 0, 0); | |
'/><br/> | |
<canvas id='cnv_out'/>""") | |
load_all = gr.Checkbox(label="Load all") | |
render = gr.Button("Render") | |
def on_submit(uploaded_video,model_type,coordinates): | |
global locations | |
locations = [] | |
avg = [0, 0] | |
if not coordinates: | |
locations = json.loads(example_coords) | |
for k, location in enumerate(locations): | |
locations[k] = location["latLng"] | |
avg[0] = avg[0] + locations[k]["lat"] | |
avg[1] = avg[1] + locations[k]["lng"] | |
else: | |
locations = json.loads(coordinates) | |
for k, location in enumerate(locations): | |
locations[k] = location["location"]["latLng"] | |
avg[0] = avg[0] + locations[k]["lat"] | |
avg[1] = avg[1] + locations[k]["lng"] | |
avg[0] = avg[0] / len(locations) | |
avg[1] = avg[1] / len(locations) | |
for k, location in enumerate(locations): | |
locations[k]["lat"] = location["lat"] - avg[0] | |
locations[k]["lng"] = location["lng"] - avg[1] | |
print(locations) | |
# Process the video and get the path of the output video | |
output_video_path = make_video(uploaded_video,encoder=model_type) | |
return output_video_path + (locations,) | |
submit.click(on_submit, inputs=[input_video, model_type, coords], outputs=[processed_video, processed_zip, output_frame, output_depth, coords]) | |
render.click(partial(get_mesh), inputs=[output_frame, output_depth, blur_in, load_all], outputs=[result]) | |
example_files = os.listdir('examples') | |
example_files.sort() | |
example_files = [os.path.join('examples', filename) for filename in example_files] | |
examples = gr.Examples(examples=example_files, inputs=[input_video], outputs=[processed_video, processed_zip, output_frame, output_depth, coords], fn=on_submit, cache_examples=True) | |
if __name__ == '__main__': | |
demo.queue().launch() |