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import cv2
import numpy as np
import gradio as gr
from ultralytics import YOLO
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
from PIL import Image
import torch # Added torch import for depth estimation
# Load YOLO model for tree detection
# Replace with your model path (local or Hugging Face Hub)
yolo_model = YOLO("./data/best.pt") # Update with your YOLO model path
# Load depth estimation model and processor from Hugging Face
processor = AutoImageProcessor.from_pretrained("Intel/dpt-large")
depth_model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-large")
# Function to process image and estimate tree heights
def process_image(image, focal_length_mm=3.6, sensor_height_mm=4.8, depth_scale=100):
"""
Process an input image to detect trees and estimate their heights.
Args:
image: PIL Image from Gradio
focal_length_mm: Camera focal length in millimeters (default: 3.6)
sensor_height_mm: Camera sensor height in millimeters (default: 4.8)
depth_scale: Scaling factor to convert depth map to centimeters (default: 100)
Returns:
Annotated image and JSON with tree heights
"""
# Convert PIL image to OpenCV format (BGR)
image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
image_height = image_cv.shape[0] # Image height in pixels
# Step 1: Run YOLO to detect trees
results = yolo_model(image_cv)
boxes = results[0].boxes.xyxy.cpu().numpy() # Bounding boxes [x_min, y_min, x_max, y_max]
# Step 2: Prepare image for depth estimation
# Convert OpenCV image (BGR) to PIL for transformers
image_pil = Image.fromarray(cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB))
# Preprocess image for depth model
inputs = processor(images=image_pil, return_tensors="pt")
# Step 3: Run depth estimation
with torch.no_grad():
outputs = depth_model(**inputs)
predicted_depth = outputs.predicted_depth
# Resize depth map to match input image size
depth_map = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=(image_cv.shape[0], image_cv.shape[1]),
mode="bicubic",
align_corners=False,
).squeeze().cpu().numpy()
# Step 4: Process each detected tree
output = []
for box in boxes:
x_min, y_min, x_max, y_max = map(int, box)
h_pixel = y_max - y_min # Bounding box height in pixels
# Extract depth for the tree’s bounding box
depth_region = depth_map[y_min:y_max, x_min:x_max]
avg_depth = np.mean(depth_region) # Average depth (relative units)
# Convert depth to centimeters using scaling factor
distance_cm = avg_depth * depth_scale # Tune depth_scale based on testing
# Calculate tree height in centimeters
# Formula: H = (h_pixel * D * sensor_height) / (focal_length * image_height)
tree_height_cm = (h_pixel * distance_cm * sensor_height_mm) / (focal_length_mm * image_height)
tree_height_cm = round(tree_height_cm, 2) # Round to 2 decimal places
output.append({
"box": (x_min, y_min, x_max, y_max),
"height_cm": tree_height_cm
})
# Step 5: Draw results on the image
for item in output:
x_min, y_min, x_max, y_max = item["box"]
# Draw bounding box
cv2.rectangle(image_cv, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
# Add height text
cv2.putText(image_cv, f"Height: {item['height_cm']} cm", (x_min, y_min - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
# Convert back to RGB for Gradio
image_rgb = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
return image_rgb, output
# Create Gradio interface
iface = gr.Interface(
fn=process_image,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Number(label="Focal Length (mm)", value=3.6),
gr.Number(label="Sensor Height (mm)", value=4.8),
gr.Number(label="Depth Scale Factor", value=100)
],
outputs=[
gr.Image(label="Detected Trees with Heights"),
gr.JSON(label="Tree Heights (cm)")
],
title="Tree Detection and Height Estimation",
description="Upload an image to detect trees and estimate their heights in centimeters. Adjust camera parameters and depth scale as needed."
)
# Launch the interface locally
iface.launch(server_name="0.0.0.0", server_port=7860) |