53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import os
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import pandas as pd
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from ultralytics import YOLO
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import cv2
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# Input and output video paths
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video_path = r'D:\AIM\pecan\GH014359.mp4'
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video_path_out = r'D:\AIM\pecan\GH014359_out.mp4'
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cap = cv2.VideoCapture(video_path)
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ret, frame = cap.read()
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H, W, _ = frame.shape
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out = cv2.VideoWriter(video_path_out, cv2.VideoWriter_fourcc(*'MP4V'), int(cap.get(cv2.CAP_PROP_FPS)), (W, H))
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# Load the YOLO model
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model = YOLO(r"D:\AIM\pecan\runs\detect\train2\weights\best.pt") # Load a custom model
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threshold = 0.5
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detected_cracked = False # Initialize a flag for detecting cracked pecans
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while ret:
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# Perform detection on the current frame
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results = model(frame)[0]
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for result in results.boxes.data.tolist():
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x1, y1, x2, y2, score, class_id = result
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if score > threshold:
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# Draw bounding boxes and labels
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cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
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label = results.names[int(class_id)].upper()
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cv2.putText(frame, f"{label} {score:.2f}", (int(x1), int(y1 - 10)),
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cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
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# Check for the "cracked" label
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if label == "CRACKED":
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detected_cracked = True
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# Write the processed frame to the output video
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out.write(frame)
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ret, frame = cap.read()
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# Determine the final label based on detections
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final_label = "CRACKED" if detected_cracked else "GOOD"
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# Print the final label
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print(f"Final Label: {final_label}")
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# Release video resources
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cap.release()
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out.release()
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cv2.destroyAllWindows()
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