def test_calibration_transformation_matrix(self): test_matrix = numpy.zeros((200, 200)) corners = { 'top_left': V(0, 0), 'top_right': V(199, 0), 'bottom_left': V(0, 199), 'bottom_right': V(199, 199) } trans = calibration.calibration_transformation_matrix(200, 200, corners) self.assertEquals(trans[90][90], V(90, 90)) self.assertEquals(trans[0][0], V(0, 0)) self.assertEquals(trans[199][199], V(199, 199)) test_matrix = numpy.zeros((200, 200)) corners = { 'top_left': V(100, 0), 'top_right': V(199, 0), 'bottom_left': V(100, 199), 'bottom_right': V(199, 199) } trans = calibration.calibration_transformation_matrix(200, 200, corners) self.assertEquals(trans[100][100], V(0, 100)) self.assertEquals(trans[1][1], V(-199, 1)) self.assertEquals(trans[199][199], V(199, 199))
def _calibrate(self, white_frame, black_frame): """ Fill gui with white, capture a frame, fill with black, capture another frame. Substract the images and calculate a threshold, generate a gradient to get the borders. Calculate a transformation matrix that converts from the coordinates on the frame to screen coordinates. """ height, width = white_frame.shape # Calculate threshold and gradient to end up with an image with just the border of the screen as white pixels diff_frame = cv2.subtract(white_frame, black_frame) threshold_frame = cv2.threshold(diff_frame, settings.CALIBRATION_THRESHOLD, 255, cv2.THRESH_BINARY)[1] gradient_frame = cv2.Laplacian(threshold_frame, cv2.CV_64F) gradient_frame = self.threshold(gradient_frame, 255, 256, 255.0) cv2.imwrite("white.jpg", white_frame) cv2.imwrite("black.jpg", black_frame) cv2.imwrite("diff.jpg", diff_frame) cv2.imwrite("threshold.jpg", threshold_frame) cv2.imwrite("gradient.jpg", gradient_frame) self.gui.fill(Color(1, 1, 1)) self.gui.update() # Get list of all white pixels in the gradient as [(y,x), (y,x), ...] border_candidate_points = numpy.transpose(gradient_frame.nonzero()) if not border_candidate_points.any(): return borders = {} for border in ['left', 'right', 'top', 'bottom']: borders[border] = { 'count': 0, 'mean': V(0,0), 'vectors': [] } for x in range(0, 500): raw_y, raw_x = random.choice(border_candidate_points) can = V(raw_x, raw_y) up = down = left = right = None # Walk along a 13x13 square path around the point and look for other border points try: for x in range(-6, 7): for y in [-6, 7]: if gradient_frame[can.y+y][can.x+x] == 255: if y < 0: down = can + (x,y) else: up = can + (x,y) for y in range(-6, 7): for x in [-6, 7]: if gradient_frame[can.y+y][can.x+x] == 255: if x < 0: left = can + (x,y) else: right = can + (x,y) except IndexError: continue # If two opposing sides of the square have border points, and all three points are roughly on a line # then assume this is an actual proper point on the screen border point_found = False if up and down and not left and not right: p = up - can q = V(down.x - can.x, can.y - down.y) point_found = True elif left and right and not up and not down: p = V(can.x-left.x, left.y-can.y) q = V(right.x-can.x, can.y-right.y) point_found = True if point_found: # Test if the three points are roughly on one line if p*q/(p.abs()*q.abs()) > 0.95: # For now, we will simply assume that the image is roughly centered, # i.e. that the left edge is on the left half of the screen, etc if up and down: if can.x < width/2: border = 'left' else: border = 'right' else: if can.y < height/2: border = 'top' else: border = 'bottom' borders[border]['count'] += 1 borders[border]['mean'] += can borders[border]['vectors'].append(can) # Paint discovered border point on original black frame for debugging black_frame[can.y][can.x] = 255 else: black_frame[can.y][can.x] = 128 cv2.imwrite("debug.jpg", black_frame) # Go through list of discovered border points and calculate vectors for the borders self.gui.draw_image_slow(black_frame) for (border_name, border) in borders.items(): if not border['count']: self.gui.update() return # Divide mean vector by number of vectors to get actual mean for this border real_mean = border['real_mean'] = border['mean']/border['count'] # Calculate mean direction of border direction_mean = V(0,0) direction_count = 0 for vector in border['vectors']: if not vector: continue direction_vector = real_mean - vector if (direction_vector+direction_mean).abs() < direction_mean.abs(): direction_vector = -direction_vector direction_mean += direction_vector direction_count += 1 border['direction_mean'] = direction_mean = direction_mean/direction_count added = real_mean + direction_mean colors = { 'left': Color(255, 0, 0), 'right': Color(0, 255, 0), 'top': Color(255, 255, 0), 'bottom': Color(0, 0, 255) } self.gui.draw_line(real_mean.x, real_mean.y, added.x, added.y, stroke_color = colors[border_name]) self.gui.draw_circle((real_mean.x, real_mean.y), 5, stroke_color = colors[border_name]) corners = {} # Calculate corners from border intersections for border1, border2 in [('top', 'left'), ('top', 'right'), ('bottom', 'left'), ('bottom', 'right')]: o1 = borders[border1]['real_mean'] d1 = borders[border1]['direction_mean'] o2 = borders[border2]['real_mean'] d2 = borders[border2]['direction_mean'] corner = V.intersection(o1, d1, o2, d2) if not corner: self.gui.update() return print corner corners[border1+"_"+border2] = corner self.gui.draw_circle((corner.x, corner.y), 10, Color(255, 0, 255)) print corners # Calculate transformation matrix self.gui.calibration_matrix = calibration.calibration_transformation_matrix(width, height, self.gui.width, self.gui.height, corners) m = self.gui.calibration_matrix b = corners['top_left'] p = m[b.y+10][b.x+10] self.gui.draw_circle((p.x, p.y), 3, stroke_color = Color(0,1,0)) b = corners['top_right'] p = m[b.y+10][b.x-10] self.gui.draw_circle((p.x, p.y), 3, stroke_color = Color(0,1,0)) b = corners['bottom_left'] p = m[b.y-10][b.x+10] self.gui.draw_circle((p.x, p.y), 3, stroke_color = Color(0,1,0)) b = corners['bottom_right'] p = m[b.y-10][b.x-10] self.gui.draw_circle((p.x, p.y), 3, stroke_color = Color(0,1,0)) self.gui.update() print "Calibration successful."