# This work is licensed under the MIT license. # Copyright (c) 2013-2025 OpenMV LLC. All rights reserved. # https://github.com/openmv/openmv/blob/master/LICENSE # # TensorFlow Lite YOLO V5 Example # # This example runs a YOLO V5 object detection model. # Please see OpenMV IDE's model zoo for example yolo v5 models. # # You can train your own custom YOLOV5 models using Edge Impulse: # https://github.com/edgeimpulse/ml-block-yolov5 # # NOTE: This exaxmple requires an OpenMV Cam with an NPU like the AE3 or N6 to run real-time. import csi import time import ml from ml.postprocessing.ultralytics import YoloV5 # Initialize the sensor. csi0 = csi.CSI() csi0.reset() csi0.pixformat(csi.RGB565) csi0.framesize(csi.VGA) csi0.window((400, 400)) # Load YOLO V5 model from ROM FS. model = ml.Model("/rom/", postprocess=YoloV5(threshold=0.4)) print(model) # Visualization parameters. n = len(model.labels) model_class_colors = [(int(255 * i // n), int(255 * (n - i - 1) // n), 255) for i in range(n)] clock = time.clock() while True: clock.tick() img = csi0.snapshot() # boxes is a list of list per class of ((x, y, w, h), score) tuples boxes = model.predict([img]) # Draw bounding boxes around the detected objects for i, class_detections in enumerate(boxes): rects = [r for r, score in class_detections] labels = [model.labels[i] for j in range(len(rects))] colors = [model_class_colors[i] for j in range(len(rects))] ml.utils.draw_predictions(img, rects, labels, colors, format=None) print(clock.fps(), "fps")