openmv/scripts/examples/01-Camera/00-Snapshot/snapshot_on_face_detection.py
2022-09-01 00:02:30 +02:00

52 lines
1.8 KiB
Python

# Snapshot on Face Detection Example
#
# Note: You will need an SD card to run this example.
#
# This example demonstrates using face tracking on your OpenMV Cam to take a
# picture.
import sensor, image, pyb
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(time = 2000) # Let new settings take affect.
# Load up a face detection HaarCascade. This is object that your OpenMV Cam
# can use to detect faces using the find_features() method below. Your OpenMV
# Cam has fontalface HaarCascade built-in. By default, all the stages of the
# HaarCascade are loaded. However, You can adjust the number of stages to speed
# up processing at the expense of accuracy. The frontalface HaarCascade has 25
# stages.
face_cascade = image.HaarCascade("frontalface", stages=25)
while(True):
pyb.LED(RED_LED_PIN).on()
print("About to start detecting faces...")
sensor.skip_frames(time = 2000) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
print("Now detecting faces!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected a face after 10 frames.
while(diff):
img = sensor.snapshot()
# Threshold can be between 0.0 and 1.0. A higher threshold results in a
# higher detection rate with more false positives. The scale value
# controls the matching scale allowing you to detect smaller faces.
faces = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)
if faces:
diff -= 1
for r in faces:
img.draw_rectangle(r)
pyb.LED(BLUE_LED_PIN).off()
print("Face detected! Saving image...")
sensor.snapshot().save("snapshot-%d.jpg" % pyb.rng()) # Save Pic.