openmv/usr/examples/face_tracking.py
iabdalkader c050509b9d Update descriptor scripts.
* Use the new match_descriptor.
* Add commented line that loads descriptor from file
2016-02-28 03:56:02 +02:00

76 lines
2.5 KiB
Python

import sensor, time, image
# Rotation.
NORMALIZED=False
# Keypoint extractor threshold, range from 0 to any number.
# This threshold is used when extracting keypoints, the lower
# the threshold the higher the number of keypoints extracted.
KEYPOINTS_THRESH=32
# Keypoint-level threshold, range from 0 to 100.
# This threshold is used when matching two keypoint descriptors, it's the
# percentage of the distance between two descriptors to the max distance.
# In other words, the minimum matching percentage between 2 keypoints.
MATCHING_THRESH=80
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.QQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
# Skip a few frames to allow the sensor settle down
# Note: This takes more time when exec from the IDE.
for i in range(0, 10):
img = sensor.snapshot()
img.draw_string(0, 0, "Please wait...")
# 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=1.5)
if objects:
# Expand the ROI by 11 pixels in each direction (half the pattern scale)
face = (objects[0][0]-22, objects[0][1]-22,objects[0][2]+22*2, objects[0][3]+22*2)
# Extract keypoints using the detect face size as the ROI
kpts1 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=face)
# Draw a rectangle around the first face
img.draw_rectangle(objects[0])
# Draw keypoints
print(kpts1)
img.draw_keypoints(kpts1, size=12)
time.sleep(1000)
# FPS clock
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
# Extract keypoints using the detect face size as the ROI
kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED)
if (kpts2):
# Match the first set of keypoints with the second one
c=image.match_descriptor(image.FREAK, kpts1, kpts2, threshold=MATCHING_THRESH)
# If more than 10% of the keypoints match draw the matching set
if (c[2]>25):
img.draw_cross(c[0], c[1], size=5)
img.draw_string(0, 10, "Match %d%%"%(c[2]))
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))