# 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 # # Snapshot on Face Detection Example # # Note: You will need an SD card to run this example. # # This example demonstrates using face tracking on your OpenMV Cam to take a # picture. import sensor import image import random import machine sensor.reset() # Reset and initialize the sensor. sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA sensor.skip_frames(time=2000) # Wait for settings take effect. led = machine.LED("LED_RED") # Load up a face detection HaarCascade. This is object that your OpenMV Cam # can use to detect faces using the find_features() method below. Your OpenMV # Cam has fontalface HaarCascade built-in. By default, all the stages of the # HaarCascade are loaded. However, You can adjust the number of stages to speed # up processing at the expense of accuracy. The frontalface HaarCascade has 25 # stages. face_cascade = image.HaarCascade("/rom/haarcascade_frontalface.cascade", stages=25) while True: print("About to start detecting faces...") sensor.skip_frames(time=2000) # Give the user time to get ready. print("Now detecting faces!") diff = 10 # We'll say we detected a face after 10 frames. while diff: img = sensor.snapshot() # Threshold can be between 0.0 and 1.0. A higher threshold results in a # higher detection rate with more false positives. The scale value # controls the matching scale allowing you to detect smaller faces. faces = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5) if faces: diff -= 1 for r in faces: img.draw_rectangle(r) led.on() print("Face detected! Saving image...") sensor.snapshot().save("snapshot-%d.jpg" % random.getrandbits(32)) # Save Pic. led.off()