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Merge pull request #2873 from kwagyeman/kwabena/add_yolo_lc
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scripts/examples: Add YOLO LC person tracking example.
This commit is contained in:
commit
7cbdb927da
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"alignment": 16,
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"optimize": "Performance"
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_lc_192.tflite",
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"alignment": 16,
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"optimize": "Performance"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/blazeface_front_128.tflite",
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"alignment": 16,
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"optimize": "Performance"
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_lc_192.tflite",
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"alignment": 16,
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"optimize": "Performance"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/blazeface_front_128.tflite",
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"alignment": 16,
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"optimize": "Performance"
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_lc_192.tflite",
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"alignment": 16,
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"optimize": "Performance"
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/blazeface_front_128.tflite",
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"alignment": 16,
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"optimize": "Performance"
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_lc_192.tflite",
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"alignment": 16,
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"optimize": "Performance"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/blazeface_front_128.tflite",
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"alignment": 16,
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"optimize": "Performance"
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_lc_192.tflite",
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"alignment": 16,
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"optimize": "Performance"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/blazeface_front_128.tflite",
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"alignment": 16,
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"optimize": "Performance"
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_lc_192.tflite",
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"alignment": 16,
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"optimize": "Performance"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_v2_224_small.tflite",
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"alignment": 32,
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"profile": "default"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_lc_192.tflite",
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"alignment": 32,
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"profile": "default"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_v2_224_small.tflite",
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"alignment": 16,
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"optimize": "Performance"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/yolo_lc_192.tflite",
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"alignment": 16,
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"optimize": "Performance"
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},
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{
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"type": "tflite",
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"path": "{TOP}/lib/models/blazeface_front_128.tflite",
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BIN
lib/models/yolo_lc_192.tflite
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BIN
lib/models/yolo_lc_192.tflite
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Binary file not shown.
1
lib/models/yolo_lc_192.txt
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1
lib/models/yolo_lc_192.txt
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person
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# This work is licensed under the MIT license.
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# Copyright (c) 2013-2025 OpenMV LLC. All rights reserved.
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# https://github.com/openmv/openmv/blob/master/LICENSE
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#
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# TensorFlow Lite YOLO LC Person Detector Example
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#
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# YOLO LC is a variant of YOLOV2 that is fast enough to run on OpenMV Cams without NPUs.
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import csi
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import time
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import ml
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from ml.postprocessing.darknet import YoloLC
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# Initialize the sensor.
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csi0 = csi.CSI()
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csi0.reset()
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csi0.pixformat(csi.RGB565)
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csi0.framesize(csi.VGA)
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csi0.window((400, 400))
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# Load built-in person detection model
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model = ml.Model("/rom/yolo_lc_192.tflite", postprocess=YoloLC(threshold=0.4))
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print(model)
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# Visualization parameters.
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n = len(model.labels)
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model_class_colors = [(int(255 * i // n), int(255 * (n - i - 1) // n), 255) for i in range(n)]
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clock = time.clock()
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while True:
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clock.tick()
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img = csi0.snapshot()
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# boxes is a list of list per class of ((x, y, w, h), score) tuples
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boxes = model.predict([img])
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# Draw bounding boxes around the detected objects
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for i, class_detections in enumerate(boxes):
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rects = [r for r, score in class_detections]
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labels = [model.labels[i] for j in range(len(rects))]
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colors = [model_class_colors[i] for j in range(len(rects))]
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ml.utils.draw_predictions(img, rects, labels, colors, format=None)
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print(clock.fps(), "fps")
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