Delete app.py

app.py

@@ -1,213 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Judol Gradio YOLO11.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1oiuTAi-cys1ydtUhSDJSRdeA02mAmZQH
-"""
-
-
-
-import cv2
-from ultralytics import YOLO
-import gradio as gr
-import imageio
-
-
-model = YOLO('https://huggingface.co/JrEasy/Judol-Detection-YOLO11/resolve/main/best.pt')
-
-
-confidence_threshold = 0.6
-
-class_names = {
-    0: "BK8",
-    1: "Gate of Olympus",
-    2: "Princess",
-    3: "Starlight Princess",
-    4: "Zeus",
-}
-
-class_colors = {
-    0: (0, 255, 0),       # Green for BK8
-    1: (255, 0, 0),       # Blue for Gate of Olympus
-    2: (0, 0, 255),       # Red for Princess
-    3: (255, 255, 0),     # Cyan for Starlight Princess
-    4: (255, 0, 255),     # Magenta for Zeus
-}
-
-def format_time_ranges(timestamps, classes):
-
-    if not timestamps:
-        return ""
-
-
-    class_timestamps = {}
-
-    for timestamp, class_id in zip(timestamps, classes):
-        class_name = class_names.get(class_id, 'Unknown')
-        if class_name not in class_timestamps:
-            class_timestamps[class_name] = []
-        class_timestamps[class_name].append(timestamp)
-
-
-    formatted_ranges = []
-
-    for class_name, timestamps in class_timestamps.items():
-        timestamps = sorted(timestamps)
-        ranges = []
-        start = timestamps[0]
-        for i in range(1, len(timestamps)):
-            if timestamps[i] - timestamps[i - 1] <= 1:
-                continue
-            else:
-                ranges.append(f"{int(start)}-{int(timestamps[i - 1])}")
-                start = timestamps[i]
-
-        ranges.append(f"{int(start)}-{int(timestamps[-1])}")
-
-        formatted_ranges.append(f"{class_name} = {', '.join(ranges)}")
-
-    return ", ".join(formatted_ranges)
-
-import os
-
-def process_video(input_video):
-    cap = cv2.VideoCapture(input_video)
-    if not cap.isOpened():
-        print("Error: Could not open input video.")
-        return None, []
-
-    fps = cap.get(cv2.CAP_PROP_FPS)
-
-    # Define the output video path in the current directory
-    output_video_path = os.path.join(os.getcwd(), "processed_video.mp4")
-
-    writer = imageio.get_writer(output_video_path, fps=fps, codec="h264")
-
-    frame_count = 0
-    timestamps = []
-    classes_detected = []
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        timestamp = frame_count / fps
-        frame_count += 1
-
-        # Resize the frame to 640x640 before passing to the model
-        resized_frame = cv2.resize(frame, (640, 640))
-
-        gray_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
-        input_frame = cv2.merge([gray_frame, gray_frame, gray_frame])
-
-        results = model.predict(input_frame)
-
-        for result in results:
-            for box in result.boxes:
-                if box.conf[0] >= confidence_threshold:
-                    x1, y1, x2, y2 = map(int, box.xyxy[0])
-                    class_id = int(box.cls[0])
-                    class_name = class_names.get(class_id, f"Class {class_id}")
-                    color = class_colors.get(class_id, (0, 255, 0))
-                    cv2.rectangle(resized_frame, (x1, y1), (x2, y2), color, 2)
-                    text = f'{class_name}, Conf: {box.conf[0]:.2f}'
-                    text_position = (x1, y1 - 10 if y1 > 20 else y1 + 20)
-                    cv2.putText(resized_frame, text, text_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-                    timestamps.append(timestamp)
-                    classes_detected.append(class_id)
-
-        writer.append_data(cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB))
-
-    cap.release()
-    writer.close()
-
-    formatted_time_ranges = format_time_ranges(timestamps, classes_detected)
-
-    print(f"Processed video saved at: {output_video_path}")
-
-    return output_video_path, formatted_time_ranges
-
-
-
-
-def process_image(input_image):
-    # Convert image from RGB to BGR for OpenCV processing
-    bgr_frame = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
-
-    # Resize the frame to 640x640 before passing to the model
-    resized_frame = cv2.resize(bgr_frame, (640, 640))
-
-    # Convert to grayscale and create a 3-channel grayscale image
-    gray_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
-    input_frame = cv2.merge([gray_frame, gray_frame, gray_frame])
-
-    
-    results = model.predict(input_frame)
-
-    detections_log = []  
-    classes_detected = []  
-
-    for result in results:
-        for box in result.boxes:
-            if box.conf[0] >= confidence_threshold:  
-                x1, y1, x2, y2 = map(int, box.xyxy[0]) 
-                class_id = int(box.cls[0]) 
-                class_name = class_names.get(class_id, f"Class {class_id}")
-                color = class_colors.get(class_id, (0, 255, 0))  # Default green color
-
-               
-                cv2.rectangle(resized_frame, (x1, y1), (x2, y2), color, 2)
-                text = f'{class_name}, Conf: {box.conf[0]:.2f}'
-                text_position = (x1, y1 - 10 if y1 > 20 else y1 + 20)
-                cv2.putText(resized_frame, text, text_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-               
-                detections_log.append({
-                    "class": class_name,
-                    "confidence": box.conf[0]
-                })
-                classes_detected.append(class_id)
-
-    # Count occurrences of each class detected
-    class_count = {class_names.get(cls, f"Class {cls}"): classes_detected.count(cls) for cls in set(classes_detected)}
-
-    # Format the detections as 'Class = Count' pairs
-    formatted_log = ", ".join([f"{class_name} = {count}" for class_name, count in class_count.items()])
-
-    # Convert the output frame back to RGB
-    output_image = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB)
-    return output_image, formatted_log
-
-
-with gr.Blocks() as app:
-    gr.Markdown("## Judol Detection using YOLOv11")
-
-    with gr.Tab("Video Detection"):
-        with gr.Row():
-            input_video = gr.Video(label="Upload a video")
-            output_video = gr.Video(label="Processed Video")
-            detections_log = gr.Textbox(label="Detections Log", lines=10)
-
-        input_video.change(
-            fn=lambda input_video: process_video(input_video) if input_video else ("", []),
-            inputs=input_video,
-            outputs=[output_video, detections_log],
-        )
-
-    with gr.Tab("Image Detection"):
-        with gr.Row():
-            input_image = gr.Image(label="Upload an image")
-            output_image = gr.Image(label="Processed Image")
-            image_detections_log = gr.Textbox(label="Detections Log", lines=10)
-
-        input_image.change(
-            fn=process_image,
-            inputs=input_image,
-            outputs=[output_image, image_detections_log],
-        )
-        
-app.launch()