uploading project files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+weights/best.pt filter=lfs diff=lfs merge=lfs -text

README.md

@@ -5,7 +5,7 @@ colorFrom: yellow
 colorTo: green
 sdk: gradio
 sdk_version: 4.40.0
-app_file: app.py
+app_file: gradio_with_map.py
 pinned: false
 license: apache-2.0
 ---

gradio_with_map.py

@@ -0,0 +1,220 @@
+import xml.etree.ElementTree as ET
+import gradio as gr
+import PIL.Image as Image
+import numpy as np
+import cv2
+from ultralytics import YOLOv10
+from exiftool import ExifToolHelper
+from geopy.distance import geodesic
+import folium
+import base64
+import supervision as sv
+import os
+
+# Constants for image dimensions
+IMAGE_WIDTH = 4000
+IMAGE_HEIGHT = 3000
+
+# Load YOLO model
+model = YOLOv10("weights/best.pt")
+
+# Define the directory for saving uploaded images
+UPLOAD_DIR = './uploads/'  # Or any other directory within your project
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+
+# Function to calculate ground distance from pixel distance
+def calculate_ground_distance(altitude, fov_deg, image_dimension, pixel_distance):
+    fov_rad = np.radians(fov_deg)
+    ground_distance = (2 * altitude * np.tan(fov_rad / 2)) * (pixel_distance / image_dimension)
+    return ground_distance
+
+# Function to get GPS coordinates from offsets
+def get_gps_coordinates(lat, lon, north_offset, east_offset):
+    new_location = geodesic(meters=north_offset).destination((lat, lon), 0)
+    new_location = geodesic(meters=east_offset).destination(new_location, 90)
+    return new_location.latitude, new_location.longitude
+
+def extract_xmp_metadata(xmp_data):
+    # Parse the XMP data as an XML tree
+    root = ET.fromstring(xmp_data)
+
+    # Define the namespace to use for querying elements
+    ns = {
+        'rdf': 'http://www.w3.org/1999/02/22-rdf-syntax-ns#',
+        'drone-dji': 'http://www.dji.com/drone-dji/1.0/'
+    }
+
+    # Find the rdf:Description element
+    rdf_description = root.find('.//rdf:Description', ns)
+
+    # Extract the desired values
+    relative_altitude = float(rdf_description.get('{http://www.dji.com/drone-dji/1.0/}RelativeAltitude', '0'))
+    gimbal_yaw_degree = float(rdf_description.get('{http://www.dji.com/drone-dji/1.0/}GimbalYawDegree', '0'))
+    gimbal_pitch_degree = float(rdf_description.get('{http://www.dji.com/drone-dji/1.0/}GimbalPitchDegree', '0'))
+
+    return relative_altitude, gimbal_yaw_degree, gimbal_pitch_degree
+
+def save_image_with_metadata(img, img_path):
+    # Convert PIL Image to a format that retains EXIF
+    img_format = img.format or 'JPEG'
+    
+    # Save image to a temporary file to preserve metadata
+    img.save(img_path, format=img_format)
+
+
+def predict_image(img, conf_threshold, iou_threshold):
+    # Define the file path within the uploads directory
+    img_path = os.path.join(UPLOAD_DIR, 'uploaded_image.jpg')
+
+    # Save the image
+    save_image_with_metadata(img, img_path)
+
+    # Extract XMP data
+    xmp_data = img.info.get("xmp")
+
+    if xmp_data:
+        relative_altitude, gimbal_yaw_degree, gimbal_pitch_degree = extract_xmp_metadata(xmp_data)
+        # for debugging 
+        print("Extracted XMP Metadata:")
+        print(f"Relative Altitude: {relative_altitude}")
+        print(f"Gimbal Yaw Degree: {gimbal_yaw_degree}")
+        print(f"Gimbal Pitch Degree: {gimbal_pitch_degree}")
+    else:
+        print("XMP data not found in the image.")
+    
+    # Extract EXIF data
+    exif_data = img.info.get("exif")
+    try:
+        xmp_data = img.info.get("xmp")
+        #print(xmp_data)    
+    except:
+        print("error loading xmp data")    
+    #print(exif_data)
+
+    # Save the image with metadata
+    if exif_data:
+        img.save(img_path, exif=exif_data)  # Save the image with its EXIF data
+    else:
+        img.save(img_path)  # Save without EXIF data if not available
+    
+    # Convert PIL Image to OpenCV image
+    img_cv2 = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+
+    # Use ExifTool to extract metadata
+    metadata = {}
+    tag_list = [
+        "Composite:FOV",
+        "Composite:GPSLatitude",
+        "Composite:GPSLongitude",
+        "XMP:AbsoluteAltitude",
+        "XMP:RelativeAltitude",
+        "XMP:GimbalRollDegree",
+        "XMP:GimbalYawDegree",
+        "XMP:GimbalPitchDegree"
+    ]
+
+    rel_path = img_path.lstrip("./")
+    #print(rel_path)
+    with ExifToolHelper() as et:
+        for d in et.get_metadata(rel_path):
+            metadata.update({k: v for k, v in d.items() if k in tag_list})
+
+    # Extract necessary metadata
+    CAMERA_GPS = (metadata["Composite:GPSLatitude"], metadata["Composite:GPSLongitude"])
+    RELATIVE_ALTITUDE = float(relative_altitude)
+    GIMBAL_YAW_DEGREE = float(gimbal_yaw_degree)
+    FOV_HORIZONTAL = float(metadata["Composite:FOV"])
+    FOV_VERTICAL = FOV_HORIZONTAL * (IMAGE_HEIGHT / IMAGE_WIDTH)
+    #GIMBAL_PITCH_DEGREE = float(gimbal_pitch_degree)
+    
+    # Convert degrees to radians
+    yaw_rad = np.radians(GIMBAL_YAW_DEGREE)
+    #pitch_rad = np.radians(GIMBAL_PITCH_DEGREE)
+
+    # Perform prediction
+    results = model.predict(
+        source=img_cv2,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
+
+    detections = sv.Detections.from_ultralytics(results[0])
+    
+    # Annotate and display image
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+
+    # Process detections and calculate GPS coordinates
+    building_locations = []
+    for i, box in enumerate(detections.xyxy):  # Correct way to iterate through boxes
+        # Extract bounding box coordinates and class
+        #print(box)
+        x_min, y_min, x_max, y_max = box  # Access the first (and only) box
+        class_id = int(detections.class_id[i])  # Get class ID as an integer
+
+        x_center = (x_min + x_max) / 2
+        y_center = (y_min + y_max) / 2
+
+        pixel_distance_x = x_center - IMAGE_WIDTH / 2
+        pixel_distance_y = IMAGE_HEIGHT / 2 - y_center
+
+        ground_distance_x = calculate_ground_distance(RELATIVE_ALTITUDE, FOV_HORIZONTAL, IMAGE_WIDTH, pixel_distance_x)
+        ground_distance_y = calculate_ground_distance(RELATIVE_ALTITUDE, FOV_VERTICAL, IMAGE_HEIGHT, pixel_distance_y)
+
+        east_offset = ground_distance_x * np.cos(yaw_rad) - ground_distance_y * np.sin(yaw_rad)
+        north_offset = ground_distance_x * np.sin(yaw_rad) + ground_distance_y * np.cos(yaw_rad)
+
+        building_lat, building_lon = get_gps_coordinates(CAMERA_GPS[0], CAMERA_GPS[1], north_offset, east_offset)
+        building_locations.append((building_lat, building_lon, class_id))
+
+    # Create a Folium map centered at the camera's GPS position
+    map_center = CAMERA_GPS
+    m = folium.Map(
+        location=map_center,
+        zoom_start=18,
+        tiles='Esri.WorldImagery'
+    )
+
+    # Add markers for each detected building
+    for i, (building_lat, building_lon, class_id) in enumerate(building_locations):
+        building_status = 'Damaged' if class_id == 1 else 'Undamaged'
+
+        folium.Marker(
+            location=(building_lat, building_lon),
+            popup=f'Building {i+1}: {building_status}',
+            icon=folium.Icon(color='red' if class_id == 1 else 'green', icon='home')
+        ).add_to(m)
+
+    # Save map to HTML and convert to display in Gradio
+    m.save('temp_map.html')
+    with open('temp_map.html', 'r') as f:
+        folium_map_html = f.read()
+    
+    encoded_html = base64.b64encode(folium_map_html.encode()).decode('utf-8')
+    data_url = f"data:text/html;base64,{encoded_html}"
+
+    return im, f'<iframe src="{data_url}" width="100%" height="600" style="border:none;"></iframe>'
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
+    ],
+    outputs=[
+        gr.Image(type="pil", label="Annotated Image"),
+        gr.HTML(label="Map"),
+    ],
+    title="Custom trained Yolov10 Model on Rescuenet Dataset",
+    description="Upload images for inference and view detected building locations on map.",
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -0,0 +1,114 @@
+aiofiles==23.2.1
+altair==5.3.0
+annotated-types==0.7.0
+anyio==4.4.0
+attrs==23.2.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+branca==0.7.2
+certifi==2024.6.2
+chardet==4.0.0
+click==8.1.7
+comet-ml==3.44.3
+configobj==5.0.8
+contourpy==1.2.1
+cycler==0.12.1
+defusedxml==0.7.1
+dnspython==2.6.1
+docopt==0.6.2
+dulwich==0.22.1
+email_validator==2.2.0
+everett==3.1.0
+exceptiongroup==1.2.1
+ExifRead==3.0.0
+fastapi==0.111.0
+fastapi-cli==0.0.4
+fastjsonschema==2.20.0
+ffmpy==0.3.2
+filetype==1.2.0
+folium==0.17.0
+fonttools==4.53.0
+fsspec==2024.6.0
+geographiclib==2.0
+geopy==2.4.1
+gradio==4.31.5
+gradio_client==0.16.4
+h11==0.14.0
+httpcore==1.0.5
+httptools==0.6.1
+httpx==0.27.0
+huggingface-hub==0.23.2
+importlib_metadata==8.0.0
+importlib_resources==6.4.0
+ipython==8.12.3
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+jupyterlab_pygments==0.3.0
+kiwisolver==1.4.5
+markdown-it-py==3.0.0
+matplotlib==3.9.0
+mdurl==0.1.2
+mistune==3.0.2
+nbclient==0.10.0
+nbconvert==7.16.4
+nbformat==5.10.4
+onnx==1.14.0
+onnxruntime==1.15.1
+onnxsim==0.4.36
+opencv-python==4.9.0.80
+opencv-python-headless==4.10.0.84
+orjson==3.10.5
+pandas==2.2.2
+pandocfilters==1.5.1
+piexif==1.1.3
+pillow==10.3.0
+pipreqs==0.5.0
+protobuf==4.25.3
+py-cpuinfo==9.0.0
+pycocotools==2.0.7
+pydantic==2.7.4
+pydantic_core==2.18.4
+pydub==0.25.1
+PyExifTool==0.5.6
+pyparsing==3.1.2
+python-box==6.1.0
+python-dotenv==1.0.1
+python-multipart==0.0.9
+pytz==2024.1
+referencing==0.35.1
+requests-toolbelt==1.0.0
+rich==13.7.1
+roboflow==1.1.36
+rpds-py==0.18.1
+ruff==0.4.10
+safetensors==0.4.3
+scipy==1.13.0
+seaborn==0.13.2
+semantic-version==2.10.0
+sentry-sdk==2.12.0
+shellingham==1.5.4
+simplejson==3.19.2
+sniffio==1.3.1
+soupsieve==2.5
+starlette==0.37.2
+supervision==0.22.0
+tinycss2==1.3.0
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.0.1
+torchvision==0.15.2
+tqdm==4.66.4
+typer==0.12.3
+tzdata==2024.1
+ujson==5.10.0
+ultralytics @ git+https://github.com/THU-MIG/yolov10.git@aad320dd80b56694e590c950b25060a134966496
+uvicorn==0.30.1
+uvloop==0.19.0
+watchfiles==0.22.0
+webencodings==0.5.1
+websockets==11.0.3
+wrapt==1.16.0
+wurlitzer==3.1.1
+xyzservices==2024.6.0
+yarg==0.1.9
+zipp==3.19.2

weights/best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23a3ca2bc5fb2555d94a6c9ae7c888b0c88232f3baebd5fb5edfdb9ec4f7dbf5
+size 16527660