openmv/scripts/examples/03-Machine-Learning/00-TensorFlow/tf_object_detection.py

66 lines
2.1 KiB
Python

# This work is licensed under the MIT license.
# Copyright (c) 2013-2023 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# 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('<object_detection_network>', 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")