def main(): scale_amount = (200, 150) cam = Camera(0) prev = cam.get_image().resize(scale_amount[0], scale_amount[1]) time.sleep(0.5) t = 0.5 buffer = 20 count = 0 while True: current = cam.get_image() current = current.resize(scale_amount[0], scale_amount[1]) if count < buffer: count = count + 1 else: fs = current.find(Motion, prev, window=15, method="BM") length_of_fs = len(fs) if fs: dx = 0 dy = 0 for f in fs: dx = dx + f.dx dy = dy + f.dy dx = dx / length_of_fs dy = dy / length_of_fs motion_str = movement_check(dx, dy, t) current.dl().text(motion_str, (10, 10)) prev = current time.sleep(0.01) current.show()
def main(): scale_amount = (200, 150) cam = Camera(0) prev = cam.get_image().resize(scale_amount[0], scale_amount[1]) time.sleep(0.5) t = 0.5 buffer = 20 count = 0 while True: current = cam.get_image() current = current.resize(scale_amount[0], scale_amount[1]) if count < buffer: count = count + 1 else: fs = current.find(Motion, prev, window=15, method="BM") length_of_fs = len(fs) if fs: dx = 0 dy = 0 for f in fs: dx = dx + f.dx dy = dy + f.dy dx = dx / length_of_fs dy = dy / length_of_fs motion_str = movement_check(dx, dy, t) current.dl().text(motion_str, (10, 10)) prev = current time.sleep(0.01) current.show()
def __init__(self): super(ColorSegmentationWindow, self).__init__('Color Segmentation Example') self.cam = Camera() self.template_img = None self.point1 = (0, 0) self.point2 = (0, 0) self.mosue_down = False
class ColorSegmentationWindow(Window): def __init__(self): super(ColorSegmentationWindow, self).__init__('Color Segmentation Example') self.cam = Camera() self.template_img = None self.point1 = (0, 0) self.point2 = (0, 0) self.mosue_down = False def on_mouse(self, event, x, y, mouse_key, data=None): """ Callback for mouse events event - int - see cv2.EVENT_* constants x, y - int, int - position of the cursor mouse_key - int - mouse key """ if event == cv2.EVENT_LBUTTONDOWN: self.point1 = (x, y) self.point2 = (x, y) self.mosue_down = True elif event == cv2.EVENT_MOUSEMOVE: if self.mosue_down == True: self.point2 = (x, y) elif event == cv2.EVENT_LBUTTONUP: self.mosue_down = False self.point2 = (x, y) x1, y1, w, h = points_to_roi(self.point1, self.point2) if w > 0 and h > 0: self.template_img = self.cam.get_image().crop(x1, y1, w, h) print "Mode:", "Detecting features..." def on_key(self, key): if key == 32: # Space bar to select template self.template_img = None print "Mode:", "Selecting template..." def on_update(self): """ Callback for periodic update. """ img = self.cam.get_image() if self.mosue_down: img.dl().rectangle_to_pts(self.point1, self.point2, color=Color.RED) img = img.apply_layers() elif self.template_img is not None: # Detect and draw key points img = img.draw_keypoint_matches(self.template_img) img = img.apply_layers() self.show(img)
class ColorSegmentationWindow(Window): def __init__(self): super(ColorSegmentationWindow, self).__init__('Color Segmentation Example') self.cam = Camera() self.template_img = None self.point1 = (0, 0) self.point2 = (0, 0) self.mosue_down = False def on_mouse(self, event, x, y, mouse_key, data=None): """ Callback for mouse events event - int - see cv2.EVENT_* constants x, y - int, int - position of the cursor mouse_key - int - mouse key """ if event == cv2.EVENT_LBUTTONDOWN: self.point1 = (x, y) self.point2 = (x, y) self.mosue_down = True elif event == cv2.EVENT_MOUSEMOVE: if self.mosue_down == True: self.point2 = (x, y) elif event == cv2.EVENT_LBUTTONUP: self.mosue_down = False self.point2 = (x, y) x1, y1, w, h = points_to_roi(self.point1, self.point2) if w > 0 and h > 0: self.template_img = self.cam.get_image().crop(x1, y1, w, h) print "Mode:", "Detecting features..." def on_key(self, key): if key == 32: # Space bar to select template self.template_img = None print "Mode:", "Selecting template..." def on_update(self): """ Callback for periodic update. """ img = self.cam.get_image() if self.mosue_down: img.dl().rectangle_to_pts(self.point1, self.point2, color=Color.RED) img = img.apply_layers() elif self.template_img is not None: # Detect and draw key points img = img.draw_keypoint_matches(self.template_img) img = img.apply_layers() self.show(img)
def __init__(self, parent=None): QtGui.QWidget.__init__(self,parent) self.MainWindow = Ui_Dialog() self.MainWindow.setupUi(self) self.webcam = Camera(0, {"width": 640, "height": 480}) self.timer = QtCore.QTimer() self.connect(self.timer, QtCore.SIGNAL('timeout()'), self.show_frame) self.timer.start(1)
class BallTrackWindow(Window): def __init__(self): super(BallTrackWindow, self).__init__('Ball Track Example') self.cam = Camera() # initialize the camera self.normal = True # mode toggle for segment detection def on_key(self, key): if key == 32: # Space bar to switch between modes self.normal = not self.normal print "Display Mode:", "Normal" if self.normal else "Segmented" def on_update(self): """ Callback for periodic update. """ img = self.cam.get_image().flip_horizontal() # grab image from camera dist = img.color_distance(Color.BLACK).dilate(2) # try to separate colors in image segmented = dist.stretch(200, 255) # really try to push out white colors if not self.normal: img = segmented blobs = segmented.find_blobs() # search the image for blob objects if blobs: # if blobs are found circles = blobs.filter([b.is_circle(0.2) for b in blobs]) # filter out only circle shaped blobs if circles: # draw the circle on the main image img.dl().circle((circles[-1].x, circles[-1].y), circles[-1].radius(), Color.BLUE, width=3) self.show(img.apply_layers())
def __init__(self): super(ColorSegmentationWindow, self).__init__('Color Segmentation Example') self.cam = Camera() self.template_img = None self.point1 = (0, 0) self.point2 = (0, 0) self.mosue_down = False
class Webcam(QtGui.QMainWindow): def __init__(self, parent=None): QtGui.QWidget.__init__(self,parent) self.MainWindow = Ui_Dialog() self.MainWindow.setupUi(self) self.webcam = Camera(0, {"width": 640, "height": 480}) self.timer = QtCore.QTimer() self.connect(self.timer, QtCore.SIGNAL('timeout()'), self.show_frame) self.timer.start(1) def show_frame(self): ipl_image = self.webcam.get_image() ipl_image.dl().circle((150, 75), 50, Color.RED, filled=True) data = ipl_image.tostring() image = QtGui.QImage(data, ipl_image.width, ipl_image.height, 3 * ipl_image.width, QtGui.QImage.Format_RGB888) pixmap = QtGui.QPixmap() pixmap.convertFromImage(image.rgbSwapped()) self.MainWindow.label.setPixmap(pixmap)
#!/usr/bin/python """ This program basically simulates some kind of 80's music video. """ print __doc__ from simplecv.api import Camera, Blob cam = Camera() # settings for the project min_size = 0.1 * cam.get_property("width") * cam.get_property("height") # Change threshold thresh = 10 # frame difference threshold last_img = cam.get_image() last_img.dl().text("Move around to get the party started!", (5, 5)) last_img.show() while True: new_img = cam.get_image() track_img = new_img - last_img # difference the images blobs = track_img.find(Blob, -1, threshblocksize=99) # use adapative blob detection if blobs: blobs.draw(autocolor=True) track_img.show() last_img = new_img # update the image
import time from simplecv.api import Camera from pyfirmata import Arduino, util board = Arduino('/dev/ttyUSB0') # The location of the Arduino analog_pin_1 = board.get_pin('a:1:i') # Use pin 1 as input analog_pin_2 = board.get_pin('a:2:i') # Use pin 2 as input button_13 = board.get_pin('d:13:i') # Use pin 13 for button input it = util.Iterator(board) # Initalize the pin monitor for the Arduino it.start() # Start the pin monitor loop multiplier = 400.0 # A value to adjust the edge threshold by cam = Camera() # Initalize the camera while True: t1 = analog_pin_1.read() # Read the value from pin 1 t2 = analog_pin_2.read() # Read the value from pin 2 b13 = button_13.read() # Read if the button has been pressed. if not t1: # Set a default if no value read t1 = 50 else: t1 *= multiplier if not t2: # Set a default if no value read t2 = 100 else: t2 *= multiplier
#!/usr/bin/python """ This SimpleCV example uses a technique called frame differencing to determine if motion has occurred. You take an initial image, then another, subtract the difference, what is left over is what has changed between those two images this are typically blobs on the images, so we do a blob search to count the number of blobs and if they exist then motion has occurred """ import time from simplecv.api import Camera, Blob cam = Camera() # setup the camera # settings for the project # make the threshold adaptable for various camera sizes min_size = 0.1 * cam.get_property("width") * cam.get_property("height") thresh = 10 # frame diff threshold show_message_for = 2 # the amount of seconds to show the motion detected message motion_timestamp = int(time.time()) message_text = "Motion detected" draw_message = False last_img = cam.get_image() last_img.show() while True: new_img = cam.get_image() track_img = new_img - last_img # diff the images blobs = track_img.find(Blob) # use adaptive blob detection now = int(time.time())
from simplecv.api import Camera from simplecv.color import Color from simplecv.segmentation.mog_segmentation import MOGSegmentation mog = MOGSegmentation(history=200, mixtures=5, bg_ratio=0.3, noise_sigma=16, learning_rate=0.3) cam = Camera() while True: mog.add_image(cam.get_image()) segmented_image = mog.get_segmented_image() blobs = mog.get_segmented_blobs() for blob in blobs: segmented_image.dl().circle((blob.x, blob.y), 10, Color.RED) segmented_image.show()
from simplecv.api import Camera c = Camera() while True: img = c.get_image() split = img.split(2, 1) left = split[0][0] mirrorred = img.blit(left.flip_horizontal(), (left.width + 1, 0)) mirrorred.show()
#!/usr/bin/python """ This program is basically the hello world in SimpleCV all it does is grab an image from the camera and display it """ print __doc__ from simplecv.api import Camera cam = Camera() while True: img = cam.get_image() img.show()
#!/usr/bin/python """ This example just takes an image, finds the edges, and draws them the threshold is used for the edge detection, if you adjust the max_threshold and threshold_step values and run the program you will see it change over time """ print __doc__ from simplecv.api import Camera, Color cam = Camera() # initialize the camera max_threshold = 255 # this is used for the edge detection threshold_step = 0.5 # this is the amount to adjust the threshold by each time the display is updated threshold = max_threshold while True: image = cam.get_image() # get image (or frame) from camera flipped_image = image.flip_horizontal() # flip it so it looks mirrored edged_image = flipped_image.edges(threshold) # get the image edges # This just automatically cycles through threshold levels if threshold <= 0: threshold = max_threshold else: threshold -= 0.5 edged_image.dl().text("Current Edge Threshold:" + str(threshold), (10, 20), color=Color.GREEN) edged_image.show()
""" This program super imposes the camera onto the television in the picture """ print __doc__ from simplecv.api import Camera, Image tv_original = Image("family_watching_television_1958.jpg", sample=True) tv_coordinates = [(353, 379), (433, 380), (432, 448), (354, 446)] tv_mask = Image(tv_original.size_tuple).invert().warp(tv_coordinates) tv = tv_original - tv_mask c = Camera() while True: bwimage = c.get_image().to_gray().resize(tv.width, tv.height).to_bgr() on_tv = tv + bwimage.warp(tv_coordinates) on_tv.show()
from simplecv.api import Camera c = Camera() while True: img = c.get_image() split = img.split(2, 1) left = split[0][0] mirrorred = img.blit(left.flip_horizontal(),(left.width + 1, 0)) mirrorred.show()
#!/usr/bin/python """ This program does basic motion blurring. It averages the number of maxframes that are set using some basic image math """ print __doc__ from operator import add from simplecv.api import Camera cam = Camera() # initialize the camera # the number of frames maxframes = 3 frames = [] frames.append(cam.get_image()) frames[0].show() while True: frames.append(cam.get_image()) #add the next frame to the end of the set if len(frames) > maxframes: frames.pop(0) # remove the earliest frame if we're at max pic = reduce(add, [i / float(len(frames)) for i in frames]) # add the frames in the array, weighted by 1 / number of frames pic.show()
""" This program super imposes the camera onto the television in the picture """ print __doc__ from simplecv.api import Camera, Image tv_original = Image("family_watching_television_1958.jpg", sample=True) tv_coordinates = [(353, 379), (433, 380), (432, 448), (354, 446)] tv_mask = Image(tv_original.size_tuple).invert().warp(tv_coordinates) tv = tv_original - tv_mask c = Camera() while True: bwimage = c.get_image().to_gray().resize(tv.width, tv.height).to_bgr() on_tv = tv + bwimage.warp(tv_coordinates) on_tv.show()
#!/usr/bin/python """ This example just takes an image, finds the edges, and draws them the threshold is used for the edge detection, if you adjust the max_threshold and threshold_step values and run the program you will see it change over time """ print __doc__ from simplecv.api import Camera, Color cam = Camera() # initialize the camera max_threshold = 255 # this is used for the edge detection threshold_step = 0.5 # this is the amount to adjust the threshold by each time the display is updated threshold = max_threshold while True: image = cam.get_image() # get image (or frame) from camera flipped_image = image.flip_horizontal() # flip it so it looks mirrored edged_image = flipped_image.edges(threshold) # get the image edges # This just automatically cycles through threshold levels if threshold <= 0: threshold = max_threshold else: threshold -= 0.5 edged_image.dl().text("Current Edge Threshold:" + str(threshold), (10, 20), color=Color.GREEN) edged_image.show()
#!/usr/bin/python from simplecv.api import Camera from simplecv.stream import VideoStream import time c = Camera() vs = VideoStream("foo.avi") for i in range(0, 500): c.get_image().edges().invert().save(vs) time.sleep(0.05)
#!/usr/bin/python """ This program is basically the hello world in SimpleCV all it does is grab an image from the camera and display it """ print __doc__ from simplecv.api import Camera cam = Camera() while True: img = cam.get_image() img.show()
#!/usr/bin/python """ This program uses a Color model to try and do segmentation based on color """ print __doc__ import time from simplecv.color_model import ColorModel from simplecv.api import Camera c = Camera() i = c.get_image() cm = ColorModel(i) i.show() t = int(time.time()) ticks = 0 while True: cm.threshold(c.get_image()).show() time.sleep(0.01) ticks = ticks + 1 if int(time.time()) > t: print str(ticks) + " fps" ticks = 0 t = int(time.time())
#!/usr/bin/python """ This SimpleCV example uses a technique called frame differencing to determine if motion has occurred. You take an initial image, then another, subtract the difference, what is left over is what has changed between those two images this are typically blobs on the images, so we do a blob search to count the number of blobs and if they exist then motion has occurred """ import time from simplecv.api import Camera, Blob cam = Camera() # setup the camera # settings for the project # make the threshold adaptable for various camera sizes min_size = 0.1 * cam.get_property("width") * cam.get_property("height") thresh = 10 # frame diff threshold show_message_for = 2 # the amount of seconds to show the motion detected message motion_timestamp = int(time.time()) message_text = "Motion detected" draw_message = False last_img = cam.get_image() last_img.show() while True: new_img = cam.get_image() track_img = new_img - last_img # diff the images blobs = track_img.find(Blob) # use adaptive blob detection now = int(time.time())
print __doc__ import time from simplecv.api import Camera from pyfirmata import Arduino, util board = Arduino('/dev/ttyUSB0') # The location of the Arduino analog_pin_1 = board.get_pin('a:1:i') # Use pin 1 as input analog_pin_2 = board.get_pin('a:2:i') # Use pin 2 as input button_13 = board.get_pin('d:13:i') # Use pin 13 for button input it = util.Iterator(board) # Initalize the pin monitor for the Arduino it.start() # Start the pin monitor loop multiplier = 400.0 # A value to adjust the edge threshold by cam = Camera() # Initalize the camera while True: t1 = analog_pin_1.read() # Read the value from pin 1 t2 = analog_pin_2.read() # Read the value from pin 2 b13 = button_13.read() # Read if the button has been pressed. if not t1: # Set a default if no value read t1 = 50 else: t1 *= multiplier if not t2: # Set a default if no value read t2 = 100 else: t2 *= multiplier
#!/usr/bin/env python # # Released under the BSD license. See LICENSE file for details. """ This program basically overlays an edge detector window that gives the illusion of X-ray vision. It is mearly meant to show how to perform a basic image operation and overlay back onto the original image. """ print __doc__ from simplecv.api import Camera # Initialize the camera cam = Camera() # Loop forever while True: # Grab image from camera and flip it img = cam.get_image().flip_horizontal() # Set the x and the y location to scale crop_x = 50 crop_y = 50 crop_width = img.width - 100 crop_height = img.height - 100 # Crop out the section of image we want cropped_img = img.crop(crop_x, crop_y, crop_width, crop_height) # Get the edges of cropped region xray_img = cropped_img.edges().smooth() # Draw the cropped image onto the current image
def __init__(self): super(BallTrackWindow, self).__init__('Ball Track Example') self.cam = Camera() # initialize the camera self.normal = True # mode toggle for segment detection
>>> python webkit-gtk.py *Note: You are not required to run the webkit-gtk.py, you can also visit http://localhost:5000 """ print __doc__ from flask import Flask, jsonify, render_template, request from werkzeug import SharedDataMiddleware import tempfile import simplejson as json from simplecv.api import Camera app = Flask(__name__) cam = Camera() @app.route('/') def show(name=None): img = cam.getImage() tf = tempfile.NamedTemporaryFile(suffix=".png") loc = 'static/' + tf.name.split('/')[-1] tf.close() img.save(loc) return render_template('index.html', img=loc) @app.route('/_snapshot') def snapshot(): """
left_blue = left.merge_channels(None, b, g) # left_blue.save("blue.png", sample=True) (r, g, b) = right.split_channels() right_red = right.merge_channels(r, None, None) #right_red.save("red.png", sample=True) sz = (left.width + offset[0], left.height + offset[1]) output = left_blue.embiggen(size=sz, pos=(0, 0)) output = output.blit(right_red, alpha=0.5, pos=offset) output = output.crop(offset[0], y=offset[1], w=left.width - offset[0], h=left.height - offset[1]) return output print "Taking pictures. Please move your camera slightly to its right" print "after every picture." c = Camera() time.sleep(1) images = [] for i in range(5): images.append(c.get_image()) print "Picture %d taken" % (i + 1) time.sleep(1) offset = (0, 0) for i in range(4): left = images[i] right = images[i + 1] output = threedee_me(left, right, offset) print output.save(temp=True)
#!/usr/bin/python """ This program does basic motion blurring. It averages the number of maxframes that are set using some basic image math """ print __doc__ from operator import add from simplecv.api import Camera cam = Camera() # initialize the camera # the number of frames maxframes = 3 frames = [] frames.append(cam.get_image()) frames[0].show() while True: frames.append(cam.get_image()) #add the next frame to the end of the set if len(frames) > maxframes: frames.pop(0) # remove the earliest frame if we're at max pic = reduce(add, [i / float(len(frames)) for i in frames]) # add the frames in the array, weighted by 1 / number of frames pic.show()