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DepthLens — Monocular Depth Estimation
Gradio app for HuggingFace Spaces deployment.
Estimates depth from a single image using MiDaS models.
Optimized for free-tier CPU inference.
"""
import os
import time
import gradio as gr
import numpy as np
from PIL import Image
from models import DepthEstimator
from utils import depth_to_colormap, create_side_by_side, create_overlay
from download_examples import download_examples
# ──────────────────────────────────────────────
# Download example images if missing
# ──────────────────────────────────────────────
download_examples()
# ──────────────────────────────────────────────
# Load model at startup (small for CPU speed)
# ──────────────────────────────────────────────
print("Starting DepthLens...")
estimator = DepthEstimator(model_size="small")
print("Ready!")
def predict(image, colormap, output_mode, overlay_alpha):
"""Run depth estimation and return result image + stats string."""
if image is None:
raise gr.Error("Please upload an image first.")
start = time.time()
image_rgb = Image.fromarray(image) if isinstance(image, np.ndarray) else image.convert("RGB")
depth = estimator.predict(image_rgb)
inference_time = time.time() - start
depth_colored = depth_to_colormap(depth, colormap.lower())
if output_mode == "Side-by-Side":
result = create_side_by_side(image_rgb, depth_colored)
elif output_mode == "Overlay":
result = create_overlay(image_rgb, depth_colored, alpha=overlay_alpha)
else:
result = depth_colored
w, h = image_rgb.size
stats = f"{w}×{h} · {inference_time:.2f}s inference · MiDaS Small"
return result, stats
# ──────────────────────────────────────────────
# Build example list (only include files that exist)
# ──────────────────────────────────────────────
example_list = []
for name in ["street.jpg", "landscape.jpg", "indoor.jpg"]:
path = os.path.join("examples", name)
if os.path.exists(path):
example_list.append([path])
# ──────────────────────────────────────────────
# Gradio Interface
# ──────────────────────────────────────────────
with gr.Blocks(
title="DepthLens — Monocular Depth Estimation",
theme=gr.themes.Base(
primary_hue="teal",
neutral_hue="slate",
),
) as demo:
gr.Markdown(
"""
# DepthLens — Monocular Depth Estimation
Upload any image to estimate per-pixel depth using MiDaS.
Warm colors = close, cool colors = far.
"""
)
with gr.Row():
with gr.Column(scale=1):
input_image = gr.Image(type="pil", label="Input Image")
colormap = gr.Dropdown(
choices=["Inferno", "Magma", "Viridis", "Plasma"],
value="Inferno",
label="Colormap",
)
output_mode = gr.Radio(
choices=["Depth Map", "Side-by-Side", "Overlay"],
value="Depth Map",
label="Output Mode",
)
overlay_alpha = gr.Slider(
minimum=0.2, maximum=0.8, value=0.5, step=0.1,
label="Overlay Opacity",
)
run_btn = gr.Button("Estimate Depth", variant="primary")
stats = gr.Textbox(label="Info", interactive=False)
with gr.Column(scale=1):
result_image = gr.Image(type="pil", label="Result")
run_btn.click(
fn=predict,
inputs=[input_image, colormap, output_mode, overlay_alpha],
outputs=[result_image, stats],
api_name="predict",
)
if example_list:
gr.Examples(
examples=example_list,
inputs=[input_image],
)
if __name__ == "__main__":
demo.launch()
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