Upload app.py

app.py

@@ -0,0 +1,109 @@
+from ultralytics import YOLO
+import cv2
+import gradio as gr
+from PIL import Image
+from huggingface_hub import snapshot_download
+import os
+import numpy as np
+
+# Model path and loading function
+model_path = "/Users/LeeTM/Downloads/best_int8_openvino_model"
+
+def load_model(repo_id):
+    print("Loading model, please wait...")
+    download_dir = snapshot_download(repo_id)
+    path = os.path.join(download_dir, "best_int8_openvino_model")
+    detection_model = YOLO(path, task='detect')
+    return detection_model
+
+def process_image(pilimg):
+    # Default values for confidence and IOU 
+    conf = 0.7  # Confidence threshold
+    iou = 0.7  # IOU threshold
+
+    # Apply the YOLOv8 model for object detection
+    result = detection_model.predict(pilimg, conf=conf, iou=iou)
+
+    # Plot the results (bounding boxes) on the image
+    img_bgr = result[0].plot()  # This will draw the bounding boxes
+    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # Convert to RGB-order PIL image
+
+    # Collect labels and confidences for the detected objects
+    labels = [detection_model.names[int(det.cls)] for det in result[0].boxes]
+    confidences = [f"{float(det.conf):.2f}" for det in result[0].boxes]
+
+    detection_details = "\n".join([f"{label}: {conf}" for label, conf in zip(labels, confidences)])
+
+    return out_pilimg, detection_details
+
+def process_video(video_path):
+    # Default values for confidence and IOU 
+    conf = 0.7  # Confidence threshold
+    iou = 0.7  # IOU threshold
+
+    cap = cv2.VideoCapture(video_path)
+    
+    # Get video details
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+    fps = int(cap.get(5))
+
+    # Output video settings
+    out_path = "output_video.mp4"
+    out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        # Convert the frame to a PIL image (YOLO uses RGB format)
+        pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+        # Apply the YOLOv8 model for object detection
+        result = detection_model.predict(pil_img, conf=conf, iou=iou)
+
+        # Plot the results (bounding boxes) on the frame
+        img_bgr = result[0].plot()  # This will draw the bounding boxes
+        frame_bgr = cv2.cvtColor(np.array(img_bgr), cv2.COLOR_RGB2BGR)
+
+        # Write the processed frame to the output video
+        out.write(frame_bgr)
+
+    cap.release()
+    out.release()
+
+    return None, out_path  # Return None for image, and video path for the video output
+
+# Model Repository
+REPO_ID = "crimson78/pokemon_ash_pikachu_yolov8"
+detection_model = load_model(REPO_ID)
+
+# Gradio Interface
+def detect_objects(input_file):
+    # Check if the input is a video or an image
+    if input_file.endswith(('.mp4', '.mov', '.avi', '.mkv')):
+        # Process as video and return None for image
+        return process_video(input_file)
+    else:
+        # Process as image and return video as None
+        pil_img = Image.open(input_file)
+        return process_image(pil_img)
+
+# Gradio Interface
+gui = gr.Interface(
+    fn=detect_objects,
+    inputs=[
+        gr.File(label="Upload an Image or Video", type="filepath"),  # File input (both image and video supported)
+    ],
+    outputs=[
+        gr.Image(type="pil", label="Detection Results"),  # Image output for images
+        gr.Video(label="Detection Results")  # Video output for videos
+    ],
+    title="YOLO Object Detection",
+    description="Upload an image or video, and the model will automatically detect objects using the YOLO model. The confidence and IOU thresholds are fixed and cannot be adjusted.",
+    flagging_mode='never'  # Use flagging_mode as never
+)
+
+# Launch the Gradio app with a public link
+gui.launch()