# 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, image, pyb RED_LED_PIN = 1 BLUE_LED_PIN = 3 sensor.reset() # Initialize the camera sensor. sensor.set_pixformat(sensor.GRAYSCALE) sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others) sensor.skip_frames(time = 2000) # Let new settings take affect. # 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("frontalface", stages=25) while(True): pyb.LED(RED_LED_PIN).on() print("About to start detecting faces...") sensor.skip_frames(time = 2000) # Give the user time to get ready. pyb.LED(RED_LED_PIN).off() print("Now detecting faces!") pyb.LED(BLUE_LED_PIN).on() 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) pyb.LED(BLUE_LED_PIN).off() print("Face detected! Saving image...") sensor.snapshot().save("snapshot-%d.jpg" % pyb.rng()) # Save Pic.