def main():
"""Finds and interprets feature points"""
# Initialize Camera
print "Starting Webcam..."
try:
cam = Camera()
except:
print "Unable to initialize camera"
sys.exit(1)
display = Display(resolution = (RES_WIDTH, RES_HEIGHT))
while not display.isDone():
# capture the current frame
try:
capture = cam.getImage()
img = capture.smooth()
except cv.error:
print "Camera unsupported by OpenCV"
sys.exit(1)
# Build face and interpret expression
face_image = FaceImage(img)
if face_image.face:
#s face_image.interpret()
pass
capture.save(display)
time.sleep(0.01)
if display.mouseLeft:
display.done = True
def main():
# Try to connect to the camera.
cam = Camera()
if not cam:
print 'Error opening camera. Exiting...'
sys.exit(1)
# Create PyGame display.
disp = Display()
# Main processing loop.
while not disp.isDone():
# Get an image from the camera.
img = cam.getImage()
faces = img.findHaarFeatures("face.xml")
i = 0
if not faces is None:
for f in faces:
f.draw(Color.GREEN)
# Show the camera image.
img.save(disp)
from SimpleCV import Color, ColorCurve, Kinect, Image, pg, np
from SimpleCV.Display import Display
d = Display()
#create video streams
cam = Kinect()
#initialize the camera
compositeframe = Image((640, 480))
#populate the compositeframe
offtime = 5.0
laststroke = time.time()
while not d.isDone():
img = cam.getImage()
imgscene = img.copy()
depth = cam.getDepth()
mindepth = np.min(depth.getNumpy())
if mindepth < 180:
depthbin = depth.binarize(np.min(depth.getNumpy()) + np.std(depth.getNumpy()) / 4).erode(3)
#take the front 1/4 stdev of the depth map
img = img.crop(0,25, 605, 455).scale(640,480)
#img.dl().blit(img.crop(100, 25, 515, 455), (125,0))
#this is a bit of a hack to compensate for the offset between cam and depth sensor
#img = img.applyLayers()
img = img - depthbin.invert()
def main(camindex = 0, capture_width = 800, capture_height = 600, chessboard_width = 9, chessboard_height = 6, planemode = False, gridsize = 0.029, calibrationFile = "default"):
global save_location
if planemode:
mode = 7
else:
mode = 0
dims = (chessboard_width, chessboard_height)
cam = Camera(camindex, prop_set = { "width": capture_width, "height": capture_height })
d = Display((capture_width, capture_height))
save_location = "" #change this if you want to save your calibration images
calibration_set = [] #calibration images
fc_set = []
introMessage()
while not d.isDone():
time.sleep(0.01)
i = cam.getImage().flipHorizontal()
cb = i.findChessboard(dims, subpixel = False)
if cb:
cb = cb[0]
elif mode != 6:
showText(i, "Put a chessboard in the green outline")
if mode == 0: #10 pictures, chessboard filling 80% of the view space
findLargeFlat(cb, i, calibration_set, dims)
if (len(calibration_set) == 10):
mode = 1
elif mode == 1: #5 pictures, chessboard filling 60% of screen, at 45 deg horiz
findHorizTilted(cb, i, calibration_set, dims)
if (len(calibration_set) == 15):
mode = 2
elif mode == 2: #5 pictures, chessboard filling 60% of screen, at 45 deg vert
findVertTilted(cb, i, calibration_set, dims)
if (len(calibration_set) == 20):
mode = 3
elif mode == 3: #5 pictures, chessboard filling 40% of screen, corners at 45
findCornerTilted(cb, i, calibration_set, dims)
if (len(calibration_set) == 25):
mode = 4
elif mode == 4: #10 pictures, chessboard filling 12% - 25% of view space
findSmallFlat(cb, i, calibration_set, dims)
if (len(calibration_set) == 35):
mode = 5
elif mode == 5:
cam.calibrate(calibration_set, gridsize, dims)
cam.saveCalibration(calibrationFile)
mode = 6
elif mode == 6:
showText(i, "Saved calibration to " + calibrationFile)
elif mode == 7:
findPlane(cb, i, calibration_set, dims)
if (len(calibration_set) == 25):
mode = 5
if cb:
cb.draw()
i.save(d)
#!/usr/bin/python
import time
from SimpleCV import *
from SimpleCV.Display import Display, pg
display_width = 640
display_height = 480
display = Display(
resolution=(display_width,
display_height)) #create a new display to draw images on
cam = Camera() #initialize the camera
done = False # setup boolean to stop the program
# Loop until not needed
while not display.isDone():
image = cam.getImage().flipHorizontal()
crop_width = 200 #set the width of the crop window
crop_height = 200 #set the height of the crop window
crop_x = display.mouseX * image.width / display_width #set the x location to scale
crop_y = display.mouseY * image.height / display_height #set the y location to scale
if (display.mouseX <= 1): #mouse outside the left of the screen
crop_x = 1
if (display.mouseY <= 1): #mouse outside the top of the screen
crop_y = 1
if (display.mouseX + crop_width >=
display_width): #region outside the right side of the screen
crop_x = (display_width - crop_width)
if (display.mouseY + crop_height >=
display_height): #region below the bottom of the screen
#!/usr/bin/python
import time
from SimpleCV import *
from SimpleCV.Display import Display, pg
display = Display(resolution = (800, 600)) #create a new display to draw images on
cam = Camera() #initialize the camera
done = False # setup boolean to stop the program
# Loop until not needed
while not display.isDone():
cam.getImage().flipHorizontal().save(display) # get image, flip it so it looks mirrored, save to display
time.sleep(0.01) # Let the program sleep for 1 millisecond so the computer can do other things
if display.mouseLeft:
display.done = True #if the left arrow is pressed, close the program
from SimpleCV import * from SimpleCV.Display import Display import xaut mouse = xaut.mouse() delay = mouse.move_delay(0) custom_red = (121.0, 41.0, 37.0) disp = Display(resolution=(640,480)) white_img = Image((640,480)).invert() YELLOW=(179.0, 195.0, 112.0) cam = Camera() while not disp.isDone(): original_img = cam.getImage().flipHorizontal() red_distance_img = original_img.colorDistance(color=Color.RED) red_bin = red_distance_img.threshold(100).invert() blobs = red_bin.findBlobs(minsize=50,maxsize=10720) if(blobs is not None): blob = max(blobs) mouse.move(15*(blob.x-256),12.5*(blob.y-192)) red_bin.save(disp) if disp.mouseLeft: disp.done = True xaut.cleanup()
def thresholdOpGreen(in_image):
return in_image.hueDistance(60).binarize(thresh=70).invert().dilate(2)
morph_extractor = MorphologyFeatureExtractor()
morph_extractor.setThresholdOperation(thresholdOp)
classifierSVM.setFeatureExtractors([morph_extractor])
count = 0
cam = Camera(0)
blobber = BlobMaker()
disp = Display(resolution=(800, 600))
minsize = 100
maxsize = (800 * 600) / 10
while not disp.isDone():
img = cam.getImage().resize(800, 600)
blobs = []
w = img.width
h = img.height
dl = DrawingLayer((w, h))
#segment our items from the background
bw = thresholdOpGreen(img)
#extract the blobs
blobs = blobber.extractFromBinary(bw, img, minsize, maxsize)
# if we get a blob
if (len(blobs) > 0):
# sort the blobs by size
blobs = blobs.sortArea()
biggest = blobs[-1].mArea
# if we get a big blob ... ignore the frame