Update face tracking script.
# script finds a face in the image using the frontalface Haar Cascade.
# After which the script uses the keypoints feature to automatically learn your
# face and track it. Keypoints can be used to automatically track anything.
-
import sensor, time, image
# Reset sensor
sensor.reset()
-
-# Sensor settings
-sensor.set_contrast(1)
+sensor.set_contrast(3)
sensor.set_gainceiling(16)
-sensor.set_framesize(sensor.QQVGA)
+sensor.set_framesize(sensor.VGA)
+sensor.set_windowing((320, 240))
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...")
+sensor.skip_frames(60)
# Load Haar Cascade
# By default this will use all stages, lower satges is faster but less accurate.
@@ -36,19 +31,20 @@ 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)
+ objects = img.find_features(face_cascade, threshold=0.5, scale=1.25)
if objects:
# Expand the ROI by 31 pixels in every direction
face = (objects[0][0]-31, objects[0][1]-31,objects[0][2]+31*2, objects[0][3]+31*2)
# Extract keypoints using the detect face size as the ROI
- kpts1 = img.find_keypoints(scale_factor=1.2, max_keypoints=100, roi=face)
+ kpts1 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, 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)
+img.draw_keypoints(kpts1, size=24)
+img = sensor.snapshot()
+time.sleep(2000)
# FPS clock
clock = time.clock()
@@ -56,12 +52,12 @@ clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
- # Extract keypoints using the detect face size as the ROI
- kpts2 = img.find_keypoints(scale_factor=1.2, max_keypoints=100)
+ # Extract keypoints from the whole frame
+ kpts2 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, normalized=True)
if (kpts2):
# Match the first set of keypoints with the second one
- c=image.match_descriptor(kpts1, kpts2)
+ c=image.match_descriptor(kpts1, kpts2, threshold=85)
match = c[6] # C[6] contains the number of matches.
if (match>5):
img.draw_rectangle(c[2:6])
