Upload 3 files

app.py

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+import os
+import cv2
+import random
+import matplotlib.pyplot as plt
+from ultralytics import YOLO
+import gradio as gr
+
+# Load the trained YOLO model
+MODEL_PATH = "best.pt"  # Replace with your model's path
+model = YOLO(MODEL_PATH)
+
+def random_color():
+    """Generate a random color for bounding boxes."""
+    return (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+
+def detect_and_visualize(image_path):
+    """
+    Perform object detection and visualize the results.
+
+    Args:
+        image_path (str): Path to the input image.
+
+    Returns:
+        Annotated image as an array.
+        Detection details as a dictionary.
+    """
+    # Perform object detection
+    results = model(image_path)
+
+    # Read the input image
+    image = cv2.imread(image_path)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert to RGB
+
+    detections = []
+    for result in results:
+        boxes = result.boxes.xyxy.cpu().numpy()  # Bounding box coordinates
+        confidences = result.boxes.conf.cpu().numpy()  # Confidence scores
+        class_ids = result.boxes.cls.cpu().numpy().astype(int)  # Class IDs
+
+        for box, confidence, class_id in zip(boxes, confidences, class_ids):
+            x_min, y_min, x_max, y_max = map(int, box)
+            class_name = model.names[class_id]
+
+            # Draw bounding box and label
+            color = random_color()
+            cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, 2)
+            label = f"{class_name} {confidence:.2f}"
+            cv2.putText(image, label, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+            # Append detection details
+            detections.append({
+                "label": class_name,
+                "confidence": float(confidence),
+                "bounding_box": {
+                    "x1": x_min,
+                    "y1": y_min,
+                    "x2": x_max,
+                    "y2": y_max
+                }
+            })
+
+    # Optionally save the annotated image
+    output_path = "output/annotated_image.jpg"
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    cv2.imwrite(output_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
+    print(f"Annotated image saved to {output_path}")
+
+    return cv2.cvtColor(image, cv2.COLOR_RGB2BGR), detections
+
+# Gradio interface
+def gradio_interface(image):
+    """
+    Gradio-compatible wrapper for object detection.
+
+    Args:
+        image (numpy.array): Input image.
+
+    Returns:
+        Annotated image and detection details.
+    """
+    temp_input_path = "temp_input.jpg"
+    cv2.imwrite(temp_input_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))  # Save temp file for YOLO
+    annotated_image, detections = detect_and_visualize(temp_input_path)
+    os.remove(temp_input_path)  # Clean up temp file
+    return annotated_image, detections
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=[
+        gr.Image(type="numpy", label="Annotated Image"),
+        gr.JSON(label="Detection Details")
+    ],
+    title="YOLO Object Detection",
+    description="Upload an image to detect objects and view annotated results along with detailed detection data."
+)
+
+if __name__ == "__main__":
+    interface.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:832aa82977bb37e2cef48b59118b64a705aefb476bcaa7fd94c9f9fe3ef84b91
+size 116598226

requirements.txt

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+gradio
+ultralytics
+opencv-python
+matplotlib