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@@ -51,3 +51,4 @@ docs/_build/ | |
| .idea | ||
| /.spyproject | ||
| .spyproject | ||
| hailort.log | ||
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| #!/usr/bin/python3 | ||
| import time | ||
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| from picamera2 import Picamera2 | ||
| from picamera2.encoders import H264Encoder | ||
| from picamera2.outputs import FfmpegOutput | ||
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| picam2 = Picamera2() | ||
| video_config = picam2.create_video_configuration() | ||
| picam2.configure(video_config) | ||
| encoder = H264Encoder(10000000) | ||
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| # audio filter takes the left channel and copies it to the right channel | ||
| # below example copies c0 (left channel) to c1 (right channel) - convert mono to stereo | ||
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| # or add audio delay on left channel like this: audio_filter="pan=stereo|adelay=1500|0" | ||
| # source for more examples: https://ffmpeg.org/ffmpeg-filters.html#Examples-2 | ||
| output = FfmpegOutput( | ||
| 'ffmpeg_audio_filter_test.mp4', | ||
| audio=True, | ||
| audio_filter="pan=stereo|c0=c0|c1=c0" | ||
| ) | ||
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| picam2.start_recording(encoder, output) | ||
| time.sleep(10) | ||
| picam2.stop_recording() |
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| person | ||
| bicycle | ||
| car | ||
| motorcycle | ||
| airplane | ||
| bus | ||
| train | ||
| truck | ||
| boat | ||
| traffic light | ||
| fire hydrant | ||
| stop sign | ||
| parking meter | ||
| bench | ||
| bird | ||
| cat | ||
| dog | ||
| horse | ||
| sheep | ||
| cow | ||
| elephant | ||
| bear | ||
| zebra | ||
| giraffe | ||
| backpack | ||
| umbrella | ||
| handbag | ||
| tie | ||
| suitcase | ||
| frisbee | ||
| skis | ||
| snowboard | ||
| sports ball | ||
| kite | ||
| baseball bat | ||
| baseball glove | ||
| skateboard | ||
| surfboard | ||
| tennis racket | ||
| bottle | ||
| wine glass | ||
| cup | ||
| fork | ||
| knife | ||
| spoon | ||
| bowl | ||
| banana | ||
| apple | ||
| sandwich | ||
| orange | ||
| broccoli | ||
| carrot | ||
| hot dog | ||
| pizza | ||
| donut | ||
| cake | ||
| chair | ||
| couch | ||
| potted plant | ||
| bed | ||
| dining table | ||
| toilet | ||
| tv | ||
| laptop | ||
| mouse | ||
| remote | ||
| keyboard | ||
| cell phone | ||
| microwave | ||
| oven | ||
| toaster | ||
| sink | ||
| refrigerator | ||
| book | ||
| clock | ||
| vase | ||
| scissors | ||
| teddy bear | ||
| hair drier | ||
| toothbrush |
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| #!/usr/bin/env python3 | ||
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| """Example module for Hailo Detection.""" | ||
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| import argparse | ||
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| import cv2 | ||
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| from picamera2 import MappedArray, Picamera2, Preview | ||
| from picamera2.devices import Hailo | ||
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| def extract_detections(hailo_output, w, h, class_names, threshold=0.5): | ||
| """Extract detections from the HailoRT-postprocess output.""" | ||
| results = [] | ||
| for class_id, detections in enumerate(hailo_output): | ||
| for detection in detections: | ||
| score = detection[4] | ||
| if score >= threshold: | ||
| y0, x0, y1, x1 = detection[:4] | ||
| bbox = (int(x0 * w), int(y0 * h), int(x1 * w), int(y1 * h)) | ||
| results.append([class_names[class_id], bbox, score]) | ||
| return results | ||
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| def draw_objects(request): | ||
| current_detections = detections | ||
| if current_detections: | ||
| with MappedArray(request, "main") as m: | ||
| for class_name, bbox, score in current_detections: | ||
| x0, y0, x1, y1 = bbox | ||
| label = f"{class_name} %{int(score * 100)}" | ||
| cv2.rectangle(m.array, (x0, y0), (x1, y1), (0, 255, 0, 0), 2) | ||
| cv2.putText(m.array, label, (x0 + 5, y0 + 15), | ||
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0, 0), 1, cv2.LINE_AA) | ||
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| if __name__ == "__main__": | ||
| # Parse command-line arguments. | ||
| parser = argparse.ArgumentParser(description="Detection Example") | ||
| parser.add_argument("-m", "--model", help="Path for the HEF model.", | ||
| default="/usr/share/hailo-models/yolov8s_h8l.hef") | ||
| parser.add_argument("-l", "--labels", default="coco.txt", | ||
| help="Path to a text file containing labels.") | ||
| parser.add_argument("-s", "--score_thresh", type=float, default=0.5, | ||
| help="Score threshold, must be a float between 0 and 1.") | ||
| args = parser.parse_args() | ||
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| # Get the Hailo model, the input size it wants, and the size of our preview stream. | ||
| with Hailo(args.model) as hailo: | ||
| model_h, model_w, _ = hailo.get_input_shape() | ||
| video_w, video_h = 1280, 960 | ||
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| # Load class names from the labels file | ||
| with open(args.labels, 'r', encoding="utf-8") as f: | ||
| class_names = f.read().splitlines() | ||
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| # The list of detected objects to draw. | ||
| detections = None | ||
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| # Configure and start Picamera2. | ||
| with Picamera2() as picam2: | ||
| main = {'size': (video_w, video_h), 'format': 'XRGB8888'} | ||
| lores = {'size': (model_w, model_h), 'format': 'RGB888'} | ||
| controls = {'FrameRate': 30} | ||
| config = picam2.create_preview_configuration(main, lores=lores, controls=controls) | ||
| picam2.configure(config) | ||
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| picam2.start_preview(Preview.QTGL, x=0, y=0, width=video_w, height=video_h) | ||
| picam2.start() | ||
| picam2.pre_callback = draw_objects | ||
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| # Process each low resolution camera frame. | ||
| while True: | ||
| frame = picam2.capture_array('lores') | ||
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| # Run inference on the preprocessed frame | ||
| results = hailo.run(frame) | ||
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| # Extract detections from the inference results | ||
| detections = extract_detections(results, video_w, video_h, class_names, args.score_thresh) |
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