mirror of
https://github.com/openmv/openmv.git
synced 2025-11-04 14:49:50 +08:00
76 lines
2.5 KiB
Python
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()))
|