import os
import cv2
from ultralytics import YOLO

# Input and output video paths
video_path = r'D:\AIM\lemon\Lemon Videos 1\Lemon Videos\test\7.mp4'
video_path_out = r'D:\AIM\lemon\Lemon Videos 1\Lemon Videos\test\7_out.mp4'
# video_path = r'D:\AIM\lemon\Lemon Videos 1\Lemon Videos\Bad Lemons\8.mp4'
# video_path_out = r'D:\AIM\lemon\Lemon Videos 1\Lemon Videos\Bad Lemons\8_out.mp4'

# Load the YOLO model
model = YOLO(r"D:\AIM\lemon\runs\detect\train4\weights\best.pt")  # Load the custom model

# Initialize VideoWriter to save the output video
cap = cv2.VideoCapture(video_path)
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(video_path_out, fourcc, fps, (width, height))

# Dictionary to track the state of each lemon
lemon_states = {}  # Format: {ID: "State"}

# Define class labels
class_labels = {
    0: "Bruised",
    1: "DefectiveLemon",
    2: "GoodLemon",
    3: "NotRipeLemon",
    4: "Rotten"
}

# Classes that require ID assignment
id_tracked_classes = ["DefectiveLemon", "GoodLemon", "NotRipeLemon"]

# Perform object tracking using BoT-SORT
results = model.track(source=video_path, conf=0.5, tracker='botsort.yaml', show=False)

for result in results:
    frame = result.orig_img  # Current frame
    detections = result.boxes  # Detection box information

    for box in detections:
        x1, y1, x2, y2 = map(int, box.xyxy[0])  # Detection box coordinates
        obj_id = int(box.id) if box.id is not None else -1  # Tracking object ID
        class_id = int(box.cls)  # Class ID
        score = box.conf  # Confidence score
        label = class_labels.get(class_id, "Unknown")  # Get class label

        # Update lemon state and output information for tracked boxes
        if obj_id != -1 and label in id_tracked_classes:
            if obj_id not in lemon_states:
                lemon_states[obj_id] = label
            else:
                # Once detected as "DefectiveLemon," the state remains "DefectiveLemon"
                if lemon_states[obj_id] != "DefectiveLemon":
                    lemon_states[obj_id] = label

            # Output ID, position, and label
            position = f"({x1}, {y1}, {x2}, {y2})"
            print(f"ID: {obj_id}, Position: {position}, Label: {lemon_states[obj_id]}")

        # Draw detection boxes and labels (including untracked ones)
        if obj_id != -1 and label in id_tracked_classes:
            display_text = f"ID {obj_id} | {lemon_states[obj_id]}"
        else:
            display_text = label  # For untracked labels, only show the class

        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
        cv2.putText(frame, display_text, (x1, y1 - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

    # Write the processed frame to the output video
    out.write(frame)

# Release resources
cap.release()
out.release()
print("Video processing completed, and the result has been saved.")