openmv/scripts/examples/25-Machine-Learning/nn_haar_smile_detection.py
2018-06-16 22:17:54 +02:00

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1011 B
Python

# Simle detection using Haar Cascade + CNN.
import sensor, time, image, os, nn
sensor.reset() # Reset and initialize the sensor.
sensor.set_contrast(2)
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565
sensor.set_framesize(sensor.QQVGA) # Set frame size to QVGA (320x240)
sensor.skip_frames(time=2000)
sensor.set_auto_gain(False)
# Load smile detection network
net = nn.load('/smile.network')
# Load Face Haar Cascade
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)
# FPS clock
clock = time.clock()
while (True):
clock.tick()
# Capture snapshot
img = sensor.snapshot()
# Find faces.
objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)
# Detect smiles
for r in objects:
img.draw_rectangle(r)
out = net.forward(img, roi=r, softmax=True)
img.draw_string(r[0], r[1], ':)' if (out[0]/127 > 0.8) else ':(', color=(255), scale=2)
print(clock.fps())