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import gradio as gr
import torch
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import torch.nn.functional as F
import torchvision.transforms as transforms
import depth_only_parameters as params
from models.depth_only_model import PVSDNet
from models.depth_only_lite_model import PVSDNet_Lite
import helperFunctions as helper
import socket
from huggingface_hub import hf_hub_download
import joblib
REPO_ID = "3ZadeSSG/PVSDNet-Depth-Only"
print("Downloading/Loading checkpoints from Hugging Face Hub...")
params.MODEL_Small_Location = hf_hub_download(
repo_id=REPO_ID,
filename="depth_only_lite_model.pth"
)
params.MODEL_Large_Location = hf_hub_download(
repo_id=REPO_ID,
filename="depth_only_model.pth"
)
print(f"Large Model loaded at: {params.MODEL_Large_Location}")
print(f"Lite Model loaded at: {params.MODEL_Small_Location}")
def get_valid_resolutions(width, height):
"""Dynamically determines valid resolutions based on input size.
- Caps the highest resolution at 1024px to avoid unnecessary high-res computations.
- Uses 6 resolutions for large images to improve multi-scale fusion quality.
- Uses 4 resolutions for smaller images (< 512px width or height).
"""
def make_divisible(n, base=16):
return max(base, int(round(n / base) * base))
max_resolution = 1024
high_w, high_h = make_divisible(min(width, max_resolution)), make_divisible(min(height, max_resolution))
# Calculate more intermediate steps for better fusion
r80_w, r80_h = make_divisible(int(high_w // 1.25)), make_divisible(int(high_h // 1.25))
r66_w, r66_h = make_divisible(int(high_w // 1.5)), make_divisible(int(high_h // 1.5))
r50_w, r50_h = make_divisible(int(high_w // 2)), make_divisible(int(high_h // 2))
r40_w, r40_h = make_divisible(int(high_w // 2.5)), make_divisible(int(high_h // 2.5))
r33_w, r33_h = make_divisible(max(256, int(high_w // 3))), make_divisible(max(256, int(high_h // 3)))
if width < 512 or height < 512:
return [(high_w, high_h), (r80_w, r80_h), (r66_w, r66_h), (r50_w, r50_h)]
else:
return [
(high_w, high_h),
(r80_w, r80_h),
(r66_w, r66_h),
(r50_w, r50_h),
(r40_w, r40_h),
(r33_w, r33_h)
]
def get_transforms(resolutions):
return [transforms.Compose([transforms.Resize((h, w)), transforms.ToTensor()]) for w, h in resolutions]
def get_prediction(image, transform, model):
img_input = image.convert('RGB')
img_input = transform(img_input).unsqueeze(0).to(params.DEVICE)
depth_out = model(img_input).detach().squeeze(0).to("cpu")
return depth_out
def predict_single_image(image, model_type):
if image is None:
return None, None
# Select model class and checkpoint
if model_type == "Lite":
model_class = PVSDNet_Lite
checkpoint = params.MODEL_Small_Location
else: # Default to "Large"
model_class = PVSDNet
checkpoint = params.MODEL_Large_Location
model = model_class(total_image_input=params.params_number_input)
model = helper.load_Checkpoint(checkpoint, model, load_cpu=True)
model.to(params.DEVICE)
model.eval()
original_width, original_height = image.size
resolutions = get_valid_resolutions(original_width, original_height)
print(f"Resolutions: {resolutions} for Model Type: {model_type}")
transforms_list = get_transforms(resolutions)
depth_maps = [get_prediction(image, t, model) for t in transforms_list]
depth_maps_resized = [
F.interpolate(depth[None], (original_height, original_width), mode='bilinear', align_corners=False)[0, 0]
for depth in depth_maps
]
depth_final = sum(depth_maps_resized) / len(depth_maps_resized)
depth_image = (depth_final - depth_final.min()) / (depth_final.max() - depth_final.min())
img_out = depth_image.numpy()
img_out_colored = plt.get_cmap('inferno')(img_out / np.max(img_out))[:, :, :3]
img_out_colored = (img_out_colored * 255).astype(np.uint8)
gray_scale_img_out = (depth_image.numpy() * 255).astype(np.uint8)
return Image.fromarray(img_out_colored), Image.fromarray(gray_scale_img_out)
with gr.Blocks(title="PVSDNet-Depth-Only Model", theme="default") as demo:
gr.Markdown(
"""
## PVSDNet-Depth-Only ZeroShot Relative Depth Estimation Model
* Upload an image and get its depth estimation with multi-scale fusion.
* Images use 2 - 6 resolutions for multi-scale fusion.
**Note:** Huggingface demo is running on CPU so inference speeds will be slow.
### Head to our [Project Page](https://realistic3d-miun.github.io/PVSDNet/) for more details about the models.
""")
with gr.Row():
with gr.Column():
img_input = gr.Image(type="pil", label="RGB Image", height=384)
with gr.Accordion("Advanced Settings", open=False):
model_type_dropdown = gr.Dropdown(["Large", "Lite"], label="Model Type", value="Large")
generate_btn = gr.Button("Estimate Depth", variant="primary")
with gr.Column():
output_color = gr.Image(type="pil", label="Depth Map (Color)", height=384)
output_gray = gr.Image(type="pil", label="Depth Map (Grayscale)", height=384)
generate_btn.click(
fn=predict_single_image,
inputs=[img_input, model_type_dropdown],
outputs=[output_color, output_gray]
)
gr.Markdown("### Example Samples")
with gr.Column():
with gr.Row():
with gr.Column(scale=2): gr.Markdown("**Example Image (Click to load)**")
with gr.Column(scale=1): gr.Markdown("**Resolution**")
with gr.Column(scale=2): gr.Markdown("**Fusion Resolutions**")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
diode_preview = gr.Image("./samples/DIODE/00022_00195_outdoor_010_030.png", label="DIODE", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("1024 x 768")
with gr.Column(scale=2):
gr.Markdown("1024x768, 816x608, 688x512, 512x384, 416x304, 336x256")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
eth3d_preview = gr.Image("./samples/ETH3D/DSC_0243.JPG", label="ETH3D", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("6048 x 4032")
with gr.Column(scale=2):
gr.Markdown("1024x1024, 816x816, 688x688, 512x512, 416x416, 336x336")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
sintel_preview = gr.Image("./samples/Sintel/frame_0028_temple.png", label="Sintel", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("1024 x 436")
with gr.Column(scale=2):
gr.Markdown("1024x432, 816x352, 688x288, 512x224")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
kitti_preview = gr.Image("./samples/KITTI/2011_10_03_drive_0047_sync_image_0000000383_image_02.png", label="KITTI", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("1216 x 532")
with gr.Column(scale=2):
gr.Markdown("1024x352, 816x288, 688x240, 512x176")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
wild_1_preview = gr.Image("./samples/Wild/toy.jpeg", label="Wild Image 1", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("3019 x 3018")
with gr.Column(scale=2):
gr.Markdown("1024x1024, 816x816, 688x688, 512x512, 416x416, 336x336")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
wild_2_preview = gr.Image("./samples/Wild/hamburg.jpeg", label="Wild Image 2", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("1536 x 1920")
with gr.Column(scale=2):
gr.Markdown("1024x1024, 816x816, 688x688, 512x512, 416x416, 336x336")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
wild_3_preview = gr.Image("./samples/Wild/north_hill.jpeg", label="Wild Image 3", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("2320 x 2321")
with gr.Column(scale=2):
gr.Markdown("1024x1024, 816x816, 688x688, 512x512, 416x416, 336x336")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
wild_4_preview = gr.Image("./samples/Wild/EH.jpeg", label="Wild Image 4", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("1920 x 1080")
with gr.Column(scale=2):
gr.Markdown("1024x1024, 816x816, 688x688, 512x512, 416x416, 336x336")
with gr.Row(variant="panel"):
with gr.Column(scale=2):
wild_5_preview = gr.Image("./samples/Wild/train_station.jpeg", label="Wild Image 5", height=120, interactive=False, show_label=True)
with gr.Column(scale=1):
gr.Markdown("1066 x 1060")
with gr.Column(scale=2):
gr.Markdown("1024x1024, 816x816, 688x688, 512x512, 416x416, 336x336")
# Define click events to load images
eth3d_preview.select(fn=lambda: Image.open("./samples/ETH3D/DSC_0243.JPG"), outputs=img_input)
sintel_preview.select(fn=lambda: Image.open("./samples/Sintel/frame_0028_temple.png"), outputs=img_input)
kitti_preview.select(fn=lambda: Image.open("./samples/KITTI/2011_10_03_drive_0047_sync_image_0000000383_image_02.png"), outputs=img_input)
diode_preview.select(fn=lambda: Image.open("./samples/DIODE/00022_00195_outdoor_010_030.png"), outputs=img_input)
wild_1_preview.select(fn=lambda: Image.open("./samples/Wild/toy.jpeg"), outputs=img_input)
wild_2_preview.select(fn=lambda: Image.open("./samples/Wild/hamburg.jpeg"), outputs=img_input)
wild_3_preview.select(fn=lambda: Image.open("./samples/Wild/north_hill.jpeg"), outputs=img_input)
wild_4_preview.select(fn=lambda: Image.open("./samples/Wild/EH.jpeg"), outputs=img_input)
wild_5_preview.select(fn=lambda: Image.open("./samples/Wild/train_station.jpeg"), outputs=img_input)
demo.launch()
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