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Create app.py
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app.py
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import os
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import uuid
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import pandas as pd
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import cv2
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from ultralytics import YOLO
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# Initialize the YOLO model
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model = YOLO('runs/segment/train/weights/best.pt')
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# Define directories
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image_dir = 'datasetsw/valid/ripe'
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output_dir = 'datasetsw/valid/test_ripe'
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# List all image files in the directory
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image_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.jpg', '.jpeg', '.png'))]
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# Initialize an empty list to store the results
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results_list = []
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# Process each image
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for image_file in image_files:
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results = model(image_file)
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annotated_frame = results[0].plot()
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total_objects = len(results[0].boxes)
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labels = results[0].boxes.cls.tolist()
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matang_count = labels.count(0)
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mentah_count = labels.count(1)
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# Generate a unique filename and save the annotated image
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unique_id = str(uuid.uuid4())
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combined_filename = f"{unique_id}.jpg"
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output_path = os.path.join(output_dir, combined_filename)
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cv2.imwrite(output_path, annotated_frame)
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# Append the results to the list
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results_list.append({
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'image_file': image_file,
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'total_objects': total_objects,
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'matang_count': matang_count,
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'mentah_count': mentah_count
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})
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# Convert the results list to a dataframe
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results_df = pd.DataFrame(results_list)
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# Save the dataframe to a CSV file
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results_df.to_csv(os.path.join(output_dir, 'detection_results.csv'), index=False)
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cv2.destroyAllWindows()
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