Updating the working version of the code

Author: javanese-programmer

capture.py

@@ -0,0 +1,74 @@
+"""Script to capture images from object detection"""
+
+import argparse
+from utils import video
+from detection import deeplearning
+
+def parse_argument():
+    """Parse arguments for command line"""
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        '--multipleObject',
+        help='Whether to detect single or multiple types of objects.',
+        required=False,
+        default=False)
+    parser.add_argument(
+        '--trueClass',
+        help='Name of the object class to be detected if it is a single type.',
+        required=False,
+        type=str,
+        default='yellow_duck')
+    parser.add_argument(
+        '--collectAll',
+        help='Whether to collect all images or only the True Positive one.',
+        required=False,
+        default=False)
+    parser.add_argument(
+        '--model',
+        help='Path of the object detection model.',
+        required=False,
+        default='./model/frogducky2.tflite')
+    parser.add_argument(
+        '--frameWidth',
+        help='Width of frame to capture from camera.',
+        required=False,
+        type=int,
+        default=640)
+    parser.add_argument(
+        '--frameHeight',
+        help='Height of frame to capture from camera.',
+        required=False,
+        type=int,
+        default=480)
+    parser.add_argument(
+        '--numThreads',
+        help='Number of CPU threads to run the model.',
+        required=False,
+        type=int,
+        default=4)
+    parser.add_argument(
+        '--enableEdgeTPU',
+        help='Whether to run the model on EdgeTPU.',
+        action='store_true',
+        required=False,
+        default=False)
+    parser.add_argument(
+        '--videoFilename',
+        help='Name of the output video file with .mp4 extension',
+        required=False,
+        default='Deteksi_Objek.mp4')
+    return parser.parse_args()
+
+def main():
+    """Main function to run detection"""
+    args = parse_argument()
+    detector = deeplearning.ConveyorDetector(bool(args.multipleObject), args.model,
+                                          args.frameWidth, args.frameHeight,
+                                          int(args.numThreads),bool(args.enableEdgeTPU))
+    detector.capture(bool(args.collectAll), str(args.videoFilename), str(args.trueClass))
+    video.move_video(str(args.videoFilename))
+
+
+if __name__ == '__main__':
+    main()