init

app.py

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+import gradio as gr
+from ultralytics import YOLO
+import os
+from PIL import Image
+import cv2
+import numpy as np
+import uuid
+
+class TreeDetectionModel:
+    def __init__(self, model_path):
+        """
+        Initialize the YOLOv8 model for tree detection.
+        Args:
+            model_path (str): Path to the trained YOLOv8 model (.pt or .onnx).
+        """
+        self.model = YOLO(model_path)
+
+    def detect(self, image_path):
+        """
+        Perform inference on an image.
+        Args:
+            image_path (str): Path to the input image.
+        Returns:
+            dict: Detection results including bounding boxes, class labels, and confidence scores.
+        """
+        results = self.model(image_path)
+        detections = []
+
+        for result in results:
+            for box in result.boxes:
+                detections.append({
+                    'class': result.names[int(box.cls)],
+                    'confidence': float(box.conf),
+                    'bbox': box.xyxy.tolist()[0]
+                })
+
+        return detections
+    
+    def extract_geotag(self, image_path):
+        """
+        Extract GPS coordinates from the image's EXIF data.
+        Args:
+            image_path (str): Path to the input image.
+        Returns:
+            dict: Geotag information (latitude, longitude, altitude).
+        """
+        img = Image.open(image_path)
+        exif_data = img._getexif()
+        if exif_data:
+            gps_info = exif_data.get(34853, {})
+            return {
+                'lat': gps_info.get(2),
+                'lon': gps_info.get(4),
+                'alt': gps_info.get(6)
+            }
+        return None
+
+
+# Load the trained YOLOv8 model
+model_path = "/home/kalie/work/projects/Community-Tree/ai/yolov8_model/best.pt"
+tree_detector = TreeDetectionModel(model_path)
+
+def detect_trees(image):
+    """
+    Perform tree detection on the uploaded image and display results.
+    Args:
+        image (PIL.Image): Input image.
+    Returns:
+        tuple: (annotated_image, detections_table, geotag_info)
+    """
+    # Convert PIL image to OpenCV format
+    image_cv = np.array(image)
+    image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)
+
+    # Save the image temporarily
+    temp_image_path = "temp_image.jpg"
+    cv2.imwrite(temp_image_path, image_cv)
+
+    # Perform detection
+    detections = tree_detector.detect(temp_image_path)
+
+    # Draw bounding boxes on the image
+    for detection in detections:
+        x1, y1, x2, y2 = map(int, detection['bbox'])
+        cv2.rectangle(image_cv, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        label = f"{detection['class']} ({detection['confidence']:.2f})"
+        cv2.putText(image_cv, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+    # Convert back to PIL format for Gradio
+    annotated_image = Image.fromarray(cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB))
+
+    # Prepare detections table
+    detections_table = [
+        [detection['class'], detection['confidence'], detection['bbox']]
+        for detection in detections
+    ]
+
+    # Extract geotag
+    geotag = tree_detector.extract_geotag(temp_image_path)
+    geotag_info = f"Latitude: {geotag['lat']}\nLongitude: {geotag['lon']}\nAltitude: {geotag['alt']}" if geotag else "No geotag found."
+
+    return annotated_image, detections_table, geotag_info
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌳 Tree Detection App")
+    gr.Markdown("Upload an image to detect trees and view results.")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(label="Upload Image", type="pil")
+            submit_button = gr.Button("Detect Trees")
+        with gr.Column():
+            image_output = gr.Image(label="Annotated Image")
+            detections_output = gr.Dataframe(
+                headers=["Class", "Confidence", "Bounding Box"],
+                label="Detection Results"
+            )
+            geotag_output = gr.Textbox(label="Geotag Information")
+
+    submit_button.click(
+        detect_trees,
+        inputs=image_input,
+        outputs=[image_output, detections_output, geotag_output]
+    )
+
+# Run the app
+demo.launch()

requirements.txt

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+aiofiles==23.2.1
+aniso8601==10.0.0
+annotated-types==0.7.0
+anyio==4.8.0
+blinker==1.9.0
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+contourpy==1.3.1
+cycler==0.12.1
+exceptiongroup==1.2.2
+fastapi==0.115.8
+ffmpy==0.5.0
+filelock==3.17.0
+Flask==3.1.0
+Flask-Cors==5.0.0
+Flask-JWT-Extended==4.7.1
+Flask-RESTful==0.3.10
+fonttools==4.55.8
+fsspec==2025.2.0
+gradio==5.14.0
+gradio_client==1.7.0
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.28.1
+idna==3.10
+itsdangerous==2.2.0
+Jinja2==3.1.5
+kiwisolver==1.4.8
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.10.0
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.4.2
+numpy==2.1.1
+nvidia-cublas-cu12==12.4.5.8
+nvidia-cuda-cupti-cu12==12.4.127
+nvidia-cuda-nvrtc-cu12==12.4.127
+nvidia-cuda-runtime-cu12==12.4.127
+nvidia-cudnn-cu12==9.1.0.70
+nvidia-cufft-cu12==11.2.1.3
+nvidia-curand-cu12==10.3.5.147
+nvidia-cusolver-cu12==11.6.1.9
+nvidia-cusparse-cu12==12.3.1.170
+nvidia-cusparselt-cu12==0.6.2
+nvidia-nccl-cu12==2.21.5
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.4.127
+opencv-python==4.11.0.86
+orjson==3.10.15
+packaging==24.2
+pandas==2.2.3
+pillow==11.1.0
+psutil==6.1.1
+py-cpuinfo==9.0.0
+pydantic==2.10.6
+pydantic_core==2.27.2
+pydub==0.25.1
+Pygments==2.19.1
+PyJWT==2.10.1
+pyparsing==3.2.1
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.20
+pytz==2024.2
+PyYAML==6.0.2
+requests==2.32.3
+rich==13.9.4
+ruff==0.9.4
+safehttpx==0.1.6
+scipy==1.15.1
+seaborn==0.13.2
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+starlette==0.45.3
+sympy==1.13.1
+tomlkit==0.13.2
+torch==2.6.0
+torchvision==0.21.0
+tqdm==4.67.1
+triton==3.2.0
+typer==0.15.1
+typing_extensions==4.12.2
+tzdata==2025.1
+ultralytics==8.3.70
+ultralytics-thop==2.0.14
+urllib3==2.3.0
+uvicorn==0.34.0
+websockets==14.2
+Werkzeug==3.1.3

yolov8_model/best_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a6058d690917cdbef87cb4684f3d45f4b91b3a68e23cb59edbd5a63d6df912c
+size 52010507