mirror of
https://github.com/openmv/openmv.git
synced 2025-11-04 14:49:50 +08:00
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
# 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, time, 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("frontalface", stages=25)
|
|
print(face_cascade)
|
|
|
|
# First set of keypoints
|
|
kpts1 = None
|
|
|
|
# Find a face!
|
|
while (kpts1 == 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(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()))
|