def runIt():
print("track_qr_code")
cam5procs.logLine("track_qr_code")
clock = time.clock()
framecount = 0
ledState = 0
ledCounter = 0
while cam5procs.receive_packet() == 0:
ledCounter += 1
framecount += 1
if ((ledCounter % 5) == 0):
if (ledState == 0):
ledState = 1
cam5procs.ledShowColour([0, 0, 255])
else:
ledState = 0
cam5procs.ledShowColour([0, 0, 0])
clock.tick()
code = "TBD!!"
tracked = [
framecount & 0xff, 0,
ord(code[0]),
ord(code[1]),
ord(code[2]),
ord(code[3]),
ord(code[4])
]
cam5procs.send_packet(tracked, 7, cam5procs.TRK_BLOB)
#print (tracked)
#if<
#while<
return True
def runIt():
cam5procs.rcv_type = cam5procs.old_rcv_type
print("make_con_movie")
cam5procs.logLine("make_con_movie")
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(10) # Let new settings take affect.
clock = time.clock() # Tracks FPS.
cam5procs.ledShowColour([0, 255, 0])
sensor.skip_frames(30) # Give the user time to get ready.
cam5procs.ledShowColour([0, 0, 0])
cam5procs.ledShowColour([0, 255, 0])
while not cam5procs.receive_packet():
try:
m = mjpeg.Mjpeg(cam5procs.fileNamePrefix %
(cam5procs.fileNameCounter) + ".mjpeg",
loop=True)
cam5procs.fileNameCounter += 1
except:
cam5procs.logLine("Error creating movie file")
cam5procs.ledShowColour([255, 0, 0])
return 1
for i in range(75):
if (cam5procs.receive_packet() == 0):
clock.tick()
m.add_frame(sensor.snapshot())
else:
m.close(clock.fps())
cam5procs.ledShowColour([0, 0, 0])
return 1
m.close(clock.fps())
#while<
cam5procs.ledShowColour([0, 0, 0])
return 0
def runIt():
print("track_eye")
cam5procs.logLine("track_eye")
clock = time.clock()
framecount = 0
ledState = 0
ledCounter = 0
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
face_cascade = image.HaarCascade("frontalface", stages=25)
eyes_cascade = image.HaarCascade("eye", stages=24)
#print(face_cascade,eyes_cascade)
while not cam5procs.receive_packet():
ledCounter += 1
if ((ledCounter % 5) == 0):
if (ledState == 0):
ledState = 1
cam5procs.ledShowColour([0, 0, 255])
else:
ledState = 0
cam5procs.ledShowColour([0, 0, 0])
clock.tick()
img = sensor.snapshot() # Capture snapshot
# Find faces.
# Note: Lower scale factor scales-down the image more and detects smaller objects.
# Higher threshold results in a higher detection rate, with more false positives.
faces = img.find_features(face_cascade, threshold=0.75, scale=1.35)
# Draw objects
framecount += 1
for face in faces[:4]:
img.draw_rectangle(face)
# Now find eyes within each face.
# Note: Use a higher threshold here (more detections) and lower scale (to find small objects)
eyes = img.find_features(eyes_cascade,
threshold=0.5,
scale=1.2,
roi=face)
for e in eyes[:2]:
img.draw_rectangle(e)
tracked = [
framecount & 0xff, 0, 0, e[0], e[1], e[0] + e[2],
e[1] + e[3]
]
#print("fps: ", clock.fps(),tracked)
cam5procs.send_packet(tracked, 7, cam5procs.TRK_BLOB)
# Print FPS.
# Note: Actual FPS is higher, streaming the FB makes it slower.
return True
def runIt_00():
print("track_qr_code")
cam5procs.logLine("track_qr_code")
clock = time.clock()
framecount = 0
ledState = 0
ledCounter = 0
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing(
(240, 240)) # look at center 240x240 pixels of the VGA resolution.
sensor.skip_frames(30)
sensor.set_auto_gain(
False) # must turn this off to prevent image washout...
while cam5procs.receive_packet() == 0:
ledCounter += 1
if ((ledCounter % 5) == 0):
if (ledState == 0):
ledState = 1
cam5procs.ledShowColour([0, 0, 255])
else:
ledState = 0
cam5procs.ledShowColour([0, 0, 0])
clock.tick()
img = sensor.snapshot()
if (img != None):
framecount += 1
for code in img.find_qrcodes():
tracked = [
framecount & 0xff, 0,
ord(code[4][0]),
ord(code[4][1]),
ord(code[4][2]),
ord(code[4][3]),
ord(code[4][4])
]
cam5procs.send_packet(tracked, 7, cam5procs.TRK_QRCODE)
img.draw_string(10, 10, code[4], 0)
#for<
#if<
#while<
return True
def runIt():
framecount = 0
ledState = 0
ledCounter = 0
print("track_blob")
cam5procs.logLine("tracking blob")
clock = time.clock()
lastBlob = None
clock.tick() # Track elapsed milliseconds between snapshots().
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.HQVGA)
sensor.skip_frames(5)
sensor.set_auto_whitebal(False)
while cam5procs.receive_packet() == 0:
ledCounter += 1
if ((ledCounter % 5) == 0):
if (ledState == 0):
ledState = 1
cam5procs.ledShowColour([0, 0, 255])
else:
ledState = 0
cam5procs.ledShowColour([0, 0, 0])
img = sensor.snapshot()
if (img != None):
blobs = img.find_blobs([
colour.map[0], colour.map[1], colour.map[2], colour.map[3],
colour.map[4], colour.map[5], colour.map[6], colour.map[7]
])
sorted_blobs = sorted(blobs, key=getKey, reverse=True)
if (sorted_blobs):
framecount += 1
for blob in sorted_blobs[:8]:
img.draw_rectangle(blob.rect())
img.draw_cross(blob.cx(), blob.cy())
tracked = [
framecount & 0xff, 0, blob[8], blob[0], blob[1],
blob[0] + blob[2], blob[1] + blob[3]
]
cam5procs.send_packet(tracked, 7, cam5procs.TRK_BLOB)
#for<
#if<
#if<
cam5procs.ledShowColour([0, 0, 0])
def runIt():
framecount = 0
print("track_line")
cam5procs.logLine("tracking line")
ledState = 0
ledCounter = 0
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.HQVGA)
sensor.skip_frames(30)
sensor.set_auto_gain(False)
sensor.set_auto_whitebal(False)
GRAYSCALE_THRESHOLD = [(0, 64)]
ROIS = [(0, i * 15, 250, 15) for i in range(8)]
while not cam5procs.receive_packet():
ledCounter += 1
if ((ledCounter % 5) == 0):
if (ledState == 0):
ledState = 1
cam5procs.ledShowColour([0, 0, 255])
else:
ledState = 0
cam5procs.ledShowColour([0, 0, 0])
img = sensor.snapshot()
if (img != None):
framecount += 1
for i, r in enumerate(ROIS):
blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r, merge=True)
if blobs:
largest_blob = max(blobs, key=lambda b: b.pixels())
tracked = [
framecount & 0xff, 0, i,
largest_blob.rect()[0],
largest_blob.rect()[1],
largest_blob.rect()[0] + largest_blob.rect()[2],
largest_blob.rect()[1] + largest_blob.rect()[3]
]
cam5procs.send_packet(tracked, 7, cam5procs.TRK_BLOB)
img.draw_rectangle(largest_blob.rect())
img.draw_cross(largest_blob.cx(), largest_blob.cy())
def runIt():
print("track_face")
cam5procs.logLine("track_face")
clock = time.clock()
ledState = 0
ledCounter = 0
framecount = 0
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)
while not cam5procs.receive_packet():
ledCounter += 1
if ((ledCounter % 5) == 0):
if (ledState == 0):
ledState = 1
cam5procs.ledShowColour([0, 0, 255])
else:
ledState = 0
cam5procs.ledShowColour([0, 0, 0])
clock.tick()
img = sensor.snapshot() # Capture snapshot
# Find faces.
# Note: Lower scale factor scales-down the image more and detects smaller objects.
# Higher threshold results in a higher detection rate, with more false positives.
faces = img.find_features(face_cascade, threshold=0.75, scale=1.35)
# Draw objects
framecount += 1
for r in faces[:8]:
img.draw_rectangle(r)
tracked = [
framecount & 0xff, 0, 0, r[0], r[1], r[0] + r[2], r[1] + r[3]
]
cam5procs.send_packet(tracked, 7, cam5procs.TRK_BLOB)
# Print FPS.
# Note: Actual FPS is higher, streaming the FB makes it slower.
return True