class EdgeSnapWindow(Window): def __init__(self): super(EdgeSnapWindow, self).__init__('Color Segmentation Example') self.image = Image("shapes.png", sample=True).edges() self.points = [] self.show(self.image) 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.points.append((x, y)) self.image.clear_layers() for p in self.points: self.image.dl().circle(p, 5, color=Color.BLUE) features = self.image.edge_snap(self.points) for f in features: f.draw(color=Color.RED, width=2) self.show(self.image.apply_layers()) def on_key(self, key): if key == 32: # Space bar to clear points self.points = [] self.image.clear_layers() self.show(self.image) print "Points cleared"
class ColorSegmentationWindow(Window): def __init__(self): super(ColorSegmentationWindow, self).__init__('Color Segmentation Example') self.img = Image('simplecv') self.segmentation = ColorSegmentation() self.normal = True # mode toggle for segment detection 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: crop = self.img.crop(x1, y1, w, h) self.segmentation = ColorSegmentation() self.segmentation.add_to_model(crop) 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. """ if self.normal: self.img.clear_layers() self.img.dl().rectangle_to_pts(self.point1, self.point2, color=Color.RED) self.show(self.img.apply_layers()) else: self.segmentation.add_image(self.img) if self.segmentation.is_ready(): img = self.segmentation.get_segmented_image() img = img.erode(iterations = 2).dilate().invert() self.show(img)
class EdgeSnapWindow(Window): def __init__(self): super(EdgeSnapWindow, self).__init__('Color Segmentation Example') self.image = Image("shapes.png", sample=True).edges() self.points = [] self.show(self.image) 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.points.append((x, y)) self.image.clear_layers() for p in self.points: self.image.dl().circle(p, 5, color=Color.BLUE) features = self.image.edge_snap(self.points) for f in features: f.draw(color=Color.RED, width=2) self.show(self.image.apply_layers()) def on_key(self, key): if key == 32: # Space bar to clear points self.points = [] self.image.clear_layers() self.show(self.image) print "Points cleared"
# run through the list of pills (blobs) found and check their color and markup the image when they are found for idx in range(len(packblobs)): pack = packblobs[idx].crop() pills_img = pack.hue_distance(pillcolor, minsaturation=saturation_threshold) pills_img = pills_img.binarize(127, inverted=True) pills = pills_img.find(Blob, minsize=pill_size) if not pills: continue for p in pills: p.image = pack p.convex_hull.draw(color=Color.RED, width=5) i.dl().blit(pack.apply_layers(), packblobs[idx].points[0]) packblobs[idx].convex_hull.draw(color=Color.BLUE, width=5) pillcount = len(pills) if pillcount != expected_pillcount: print "pack at %d, %d had %d pills" % (packblobs[idx].x, packblobs[idx].y, pillcount) i.dl().text('ERROR', (packblobs[idx].x, packblobs[idx].y + 150), color=Color.RED) i.dl().text("Pills Found: " + str(pillcount), (packblobs[idx].x, packblobs[idx].y + 170), color=Color.RED) i.dl().text("Pills Expected: " + str(expected_pillcount), (packblobs[idx].x, packblobs[idx].y + 190), color=Color.RED) else: i.dl().text('OK', (packblobs[idx].x, packblobs[idx].y + 150), color=Color.GREEN) # Continue to show the image
example = 1 else: example += 1 else: threshold += threshold_step if example == 1: image = image.erode(threshold) text = "Erode Morphology Example: img.erode(" + str(threshold) + ")" elif example == 2: image = image.dilate(threshold) text = "Dilate Morphology Example: img.dilate(" + str(threshold) + ")" elif example == 3: image = image.morph_open() text = "Open Morphology Example: img.morph_open()" elif example == 4: image = image.morph_close() text = "Close Morphology Example: img.morph_close()" elif example == 5: image = image.morph_gradient() text = "Gradient Morphology Example: img.morph_gradient()" else: text = '' image.dl().text(text, (10, 10), color=Color.RED) image.show()
and put back into a display. """ print __doc__ import time from simplecv.api import Color, Image sleep_for = 3 # seconds to sleep for text_point = (20, 20) font_size = 24 draw_color = Color.YELLOW while True: image = Image("orson_welles.jpg", sample=True) image.dl().set_font_size(font_size) image.dl().text("Original Size", text_point, color=draw_color) image.show() time.sleep(sleep_for) rot = image.rotate(45) rot.dl().set_font_size(font_size) rot.dl().text("Rotated 45 degrees", text_point, color=draw_color) rot.show() time.sleep(sleep_for) rot = image.rotate(45, scale=0.5) rot.dl().set_font_size(font_size) rot.dl().text("Rotated 45 degreesand scaled", text_point, color=draw_color) rot.show() time.sleep(sleep_for)
# run through the list of pills (blobs) found and check their color and markup the image when they are found for idx in range(len(packblobs)): pack = packblobs[idx].crop() pills_img = pack.hue_distance(pillcolor, minsaturation=saturation_threshold) pills_img = pills_img.binarize(127, inverted=True) pills = pills_img.find(Blob, minsize=pill_size) if not pills: continue for p in pills: p.image = pack p.convex_hull.draw(color=Color.RED, width=5) i.dl().blit(pack.apply_layers(), packblobs[idx].points[0]) packblobs[idx].convex_hull.draw(color=Color.BLUE, width=5) pillcount = len(pills) if pillcount != expected_pillcount: print "pack at %d, %d had %d pills" % (packblobs[idx].x, packblobs[idx].y, pillcount) i.dl().text('ERROR', (packblobs[idx].x, packblobs[idx].y + 150), color=Color.RED) i.dl().text("Pills Found: " + str(pillcount), (packblobs[idx].x, packblobs[idx].y + 170), color=Color.RED) i.dl().text("Pills Expected: " + str(expected_pillcount), (packblobs[idx].x, packblobs[idx].y + 190), color=Color.RED) else:
example = 1 else: example += 1 else: threshold += threshold_step if example == 1: image = image.erode(threshold) text = "Erode Morphology Example: img.erode(" + str(threshold) + ")" elif example == 2: image = image.dilate(threshold) text = "Dilate Morphology Example: img.dilate(" + str(threshold) + ")" elif example == 3: image = image.morph_open() text = "Open Morphology Example: img.morph_open()" elif example == 4: image = image.morph_close() text = "Close Morphology Example: img.morph_close()" elif example == 5: image = image.morph_gradient() text = "Gradient Morphology Example: img.morph_gradient()" else: text = '' image.dl().text(text, (10, 10), color=Color.RED) image.show()
to think of this is if you played the card matching game, the cards would pretty much have to be identical. The template method doesn't allow much for the scale to change, nor for rotation. This is the most basic pattern matching SimpleCV offers. If you are looking for something more complex you will probably want to look into img.find() """ print __doc__ import time from simplecv.api import Image, Color, TemplateMatch source = Image("templatetest.png", sample=True) # the image to search template = Image("template.png", sample=True) # the template to search the image for t = 5 methods = [ "SQR_DIFF", "SQR_DIFF_NORM", "CCOEFF", "CCOEFF_NORM", "CCORR", "CCORR_NORM" ] # the various types of template matching available for m in methods: img = Image("templatetest.png", sample=True) img.dl().text("current method: {}".format(m), (10, 20), color=Color.RED) fs = source.find(TemplateMatch, template, threshold=t, method=m) for match in fs: img.dl().rectangle((match.x, match.y), (match.width, match.height), color=Color.RED) img.apply_layers().show() time.sleep(3)
img = Image('coins.jpg', sample=True) coins = img.invert().find(Blob, minsize=200) # Here we compute the scale factor if coins: c = coins[-1] diameter = c.radius * 2 size_ratio = quarter_size / diameter # Now we print the measurements back on the picture for coin in coins: # get the physical size of the coin size = (coin.radius * 2) * size_ratio # label the coin accordingly if 18 < size < 20: coin_type = "penny" elif 20 < size < 23: coin_type = "nickel" elif 16 < size < 18: coin_type = "dime" elif 23 < size < 26: coin_type = "quarter" else: coin_type = "unknown" text = "Type: {}, Size: {}mm".format(coin_type, size) img.dl().text(text, (coin.x + 45, coin.y + 45), color=Color.BLUE) img.show() time.sleep(10)
""" This example uses the built in template matching. The easiest way to think of this is if you played the card matching game, the cards would pretty much have to be identical. The template method doesn't allow much for the scale to change, nor for rotation. This is the most basic pattern matching SimpleCV offers. If you are looking for something more complex you will probably want to look into img.find() """ print __doc__ import time from simplecv.api import Image, Color, TemplateMatch source = Image("templatetest.png", sample=True) # the image to search template = Image("template.png", sample=True) # the template to search the image for t = 5 methods = ["SQR_DIFF", "SQR_DIFF_NORM", "CCOEFF", "CCOEFF_NORM", "CCORR", "CCORR_NORM"] # the various types of template matching available for m in methods: img = Image("templatetest.png", sample=True) img.dl().text("current method: {}".format(m), (10, 20), color=Color.RED) fs = source.find(TemplateMatch, template, threshold=t, method=m) for match in fs: img.dl().rectangle((match.x, match.y), (match.width, match.height), color=Color.RED) img.apply_layers().show() time.sleep(3)
img = Image('coins.jpg', sample=True) coins = img.invert().find(Blob, minsize=200) # Here we compute the scale factor if coins: c = coins[-1] diameter = c.radius * 2 size_ratio = quarter_size / diameter # Now we print the measurements back on the picture for coin in coins: # get the physical size of the coin size = (coin.radius * 2) * size_ratio # label the coin accordingly if 18 < size < 20: coin_type = "penny" elif 20 < size < 23: coin_type = "nickel" elif 16 < size < 18: coin_type = "dime" elif 23 < size < 26: coin_type = "quarter" else: coin_type = "unknown" text = "Type: {}, Size: {}mm".format(coin_type, size) img.dl().text(text, (coin.x + 45, coin.y + 45), color=Color.BLUE) img.show() time.sleep(10)