Upload app.py

app.py

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+# from ultralytics import YOLO
+# import cv2
+# import gradio as gr
+# import numpy as np
+
+# # -------------------------
+# # Load models (once)
+# # -------------------------
+# det_model = YOLO(
+#     "models/detect/best_yolov8s.onnx"
+# )
+
+# cls_model = YOLO(
+#     "models/classify/Buck_classification_epoch_26_best.onnx",
+#     task="classify"
+# )
+
+# # -------------------------
+# # Inference function
+# # -------------------------
+# def predict(image):
+#     # Convert RGB (Gradio) → BGR (OpenCV)
+#     image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+#     det_results = det_model(image)
+
+#     for r in det_results:
+#         for box in r.boxes:
+#             x1, y1, x2, y2 = map(int, box.xyxy[0])
+
+#             crop = image[y1:y2, x1:x2]
+#             if crop.size == 0:
+#                 continue
+
+#             # Classification
+#             cls_results = cls_model(crop)
+#             probs = cls_results[0].probs
+
+#             cls_id = probs.top1
+#             cls_conf = probs.top1conf
+#             cls_name = cls_results[0].names[cls_id]
+
+#             # Labels
+#             label = f"Deer | {cls_name} ({cls_conf:.2f})"
+
+#             # Draw box + label
+#             cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+#             cv2.putText(
+#                 image,
+#                 label,
+#                 (x1, y1 - 10),
+#                 cv2.FONT_HERSHEY_SIMPLEX,
+#                 0.7,
+#                 (0, 255, 0),
+#                 2
+#             )
+
+#     # Convert back BGR → RGB
+#     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+#     return image
+
+
+# # -------------------------
+# # Gradio UI
+# # -------------------------
+# app = gr.Interface(
+#     fn=predict,
+#     inputs=gr.Image(type="numpy", label="Upload Deer Image"),
+#     outputs=gr.Image(type="numpy", label="Prediction"),
+#     title="Buck Tracker AI – Deer Detection & Classification",
+#     description="Upload a trail cameras image. The system detects deer and classifies Buck/Doe using a multi-stage YOLO pipeline."
+# )
+
+# # -------------------------
+# # Launch
+# # -------------------------
+# if __name__ == "__main__":
+#     app.launch()
+from ultralytics import YOLO
+import cv2
+import gradio as gr
+import numpy as np
+
+# -------------------------
+# Load detection model
+# -------------------------
+det_model = YOLO(r"models\buck_vs_doe_Detection_best.pt")
+
+# -------------------------
+# Inference function
+# -------------------------
+def predict(image):
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    results = det_model(image)
+
+    for r in results:
+        for box in r.boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            conf = float(box.conf[0])
+            cls_id = int(box.cls[0])
+
+            # ✅ Auto class name from Ultralytics
+            class_name = det_model.names[cls_id]
+
+            label = f"{class_name} ({conf:.2f})"
+
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(
+                image,
+                label,
+                (x1, y1 - 10),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.7,
+                (0, 255, 0),
+                2
+            )
+
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image
+
+
+# -------------------------
+# Gradio UI
+# -------------------------
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Image(type="numpy", label="Detection Result"),
+    title="Buck Tracker AI – Deer Detection",
+    description="YOLO-based deer detection with automatic class labels from the model."
+)
+
+# -------------------------
+# Launch
+# -------------------------
+if __name__ == "__main__":
+    app.launch()