openmv/scripts/examples/05-Feature-Detection/lbp.py
2025-05-30 11:05:14 -07:00

59 lines
1.7 KiB
Python

# 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
#
# Local Binary Patterns (LBP) Example
#
# This example shows off how to use the local binary pattern feature descriptor
# on your OpenMV Cam. LBP descriptors work like Freak feature descriptors.
#
# WARNING: LBP supports needs to be reworked! As of right now this feature needs
# a lot of work to be made into somethin useful. This script will remain to show
# that the functionality exists, but, in its current state is inadequate.
import sensor
import time
import image
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
# 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)
# 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, 30):
img = sensor.snapshot()
img.draw_string(0, 0, "Please wait...")
d0 = None
# d0 = image.load_descriptor("desc.lbp")
clock = time.clock()
while True:
clock.tick()
img = sensor.snapshot()
objects = img.find_features(face_cascade, threshold=0.5, scale_factor=1.25)
if objects:
face = objects[0]
d1 = img.find_lbp(face)
if d0 is None:
d0 = d1
else:
dist = image.match_descriptor(d0, d1)
img.draw_string(0, 10, "Match %d%%" % (dist))
img.draw_rectangle(face)
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f" % (clock.fps()))