# TensorFlow Lite Object Detection Example # # This examples uses the builtin FOMO model to detect faces. import sensor import time import tf import math sensor.reset() # Reset and initialize the sensor. sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) sensor.set_windowing((240, 240)) # Set 240x240 window. sensor.skip_frames(time=2000) # Let the camera adjust. min_confidence = 0.4 # Load built-in FOMO face detection model labels, net = tf.load_builtin_model("fomo_face_detection") # Alternatively, models can be loaded from the filesystem storage. # net = tf.load('', load_to_fb=True) # labels = [line.rstrip('\n') for line in open("labels.txt")] colors = [ # Add more colors if you are detecting more than 7 types of classes at once. (255, 0, 0), (0, 255, 0), (255, 255, 0), (0, 0, 255), (255, 0, 255), (0, 255, 255), (255, 255, 255), ] clock = time.clock() while True: clock.tick() img = sensor.snapshot() # detect() returns all objects found in the image (splitted out per class already) # we skip class index 0, as that is the background, and then draw circles of the center # of our objects for i, detection_list in enumerate( net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)]) ): if i == 0: continue # background class if len(detection_list) == 0: continue # no detections for this class? print("********** %s **********" % labels[i]) for d in detection_list: [x, y, w, h] = d.rect() center_x = math.floor(x + (w / 2)) center_y = math.floor(y + (h / 2)) print(f"x {center_x}\ty {center_y}") img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2) print(clock.fps(), "fps", end="\n")