# 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 # # Face Tracking Example # # This example shows off using the keypoints feature of your OpenMV Cam to track # a face after it has been detected by a Haar Cascade. The first part of this # script finds a face in the image using the frontalface Haar Cascade. # After which the script uses the keypoints feature to automatically learn your # face and track it. Keypoints can be used to automatically track anything. import sensor import time import image # Reset sensor sensor.reset() sensor.set_contrast(3) sensor.set_gainceiling(16) sensor.set_framesize(sensor.VGA) sensor.set_windowing((320, 240)) sensor.set_pixformat(sensor.GRAYSCALE) # Skip a few frames to allow the sensor settle down sensor.skip_frames(time=2000) # Load Haar Cascade # By default this will use all stages, lower satges is faster but less accurate. face_cascade = image.HaarCascade("/rom/haarcascade_frontalface.cascade", stages=25) print(face_cascade) # First set of keypoints kpts1 = None # Find a face! while kpts1 is None: img = sensor.snapshot() img.draw_string(0, 0, "Looking for a face...") # Find faces objects = img.find_features(face_cascade, threshold=0.5, scale=1.25) if objects: # Expand the ROI by 31 pixels in every direction face = ( objects[0][0] - 31, objects[0][1] - 31, objects[0][2] + 31 * 2, objects[0][3] + 31 * 2, ) # Extract keypoints using the detect face size as the ROI kpts1 = img.find_keypoints( threshold=10, scale_factor=1.1, max_keypoints=100, roi=face ) # Draw a rectangle around the first face img.draw_rectangle(objects[0]) # Draw keypoints print(kpts1) img.draw_keypoints(kpts1, size=24) img = sensor.snapshot() time.sleep_ms(2000) # FPS clock clock = time.clock() while True: clock.tick() img = sensor.snapshot() # Extract keypoints from the whole frame kpts2 = img.find_keypoints( threshold=10, scale_factor=1.1, max_keypoints=100, normalized=True ) if kpts2: # Match the first set of keypoints with the second one c = image.match_descriptor(kpts1, kpts2, threshold=85) match = c[6] # C[6] contains the number of matches. if match > 5: img.draw_rectangle(c[2:6]) img.draw_cross(c[0], c[1], size=10) print(kpts2, "matched:%d dt:%d" % (match, c[7])) # Draw FPS img.draw_string(0, 0, "FPS:%.2f" % (clock.fps()))