diff options
Diffstat (limited to 'utils/raspberrypi/ctt/ctt_dots_locator.py')
| -rw-r--r-- | utils/raspberrypi/ctt/ctt_dots_locator.py | 118 |
1 files changed, 118 insertions, 0 deletions
diff --git a/utils/raspberrypi/ctt/ctt_dots_locator.py b/utils/raspberrypi/ctt/ctt_dots_locator.py new file mode 100644 index 00000000..4945c04b --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_dots_locator.py @@ -0,0 +1,118 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2023, Raspberry Pi Ltd +# +# find_dots.py - Used by CAC algorithm to convert image to set of dots + +''' +This file takes the black and white version of the image, along with +the color version. It then located the black dots on the image by +thresholding dark pixels. +In a rather fun way, the algorithm bounces around the thresholded area in a random path +We then use the maximum and minimum of these paths to determine the dot shape and size +This info is then used to return colored dots and locations back to the main file +''' + +import numpy as np +import random +from PIL import Image, ImageEnhance, ImageFilter + + +def find_dots_locations(rgb_image, color_threshold=100, dots_edge_avoid=75, image_edge_avoid=10, search_path_length=500, grid_scan_step_size=10, logfile=open("log.txt", "a+")): + # Initialise some starting variables + pixels = Image.fromarray(rgb_image) + pixels = pixels.convert("L") + enhancer = ImageEnhance.Contrast(pixels) + im_output = enhancer.enhance(1.4) + # We smooth it slightly to make it easier for the dot recognition program to locate the dots + im_output = im_output.filter(ImageFilter.GaussianBlur(radius=2)) + bw_image = np.array(im_output) + + location = [0, 0] + dots = [] + dots_location = [] + # the program takes away the edges - we don't want a dot that is half a circle, the + # centroids would all be wrong + for x in range(dots_edge_avoid, len(bw_image) - dots_edge_avoid, grid_scan_step_size): + for y in range(dots_edge_avoid, len(bw_image[0]) - dots_edge_avoid, grid_scan_step_size): + location = [x, y] + scrap_dot = False # A variable used to make sure that this is a valid dot + if (bw_image[location[0], location[1]] < color_threshold) and not (scrap_dot): + heading = "south" # Define a starting direction to move in + coords = [] + for i in range(search_path_length): # Creates a path of length `search_path_length`. This turns out to always be enough to work out the rough shape of the dot. + # Now make sure that the thresholded area doesn't come within 10 pixels of the edge of the image, ensures we capture all the CA + if ((image_edge_avoid < location[0] < len(bw_image) - image_edge_avoid) and (image_edge_avoid < location[1] < len(bw_image[0]) - image_edge_avoid)) and not (scrap_dot): + if heading == "south": + if bw_image[location[0] + 1, location[1]] < color_threshold: + # Here, notice it does not go south, but actually goes southeast + # This is crucial in ensuring that we make our way around the majority of the dot + location[0] = location[0] + 1 + location[1] = location[1] + 1 + heading = "south" + else: + # This happens when we reach a thresholded edge. We now randomly change direction and keep searching + dir = random.randint(1, 2) + if dir == 1: + heading = "west" + if dir == 2: + heading = "east" + + if heading == "east": + if bw_image[location[0], location[1] + 1] < color_threshold: + location[1] = location[1] + 1 + heading = "east" + else: + dir = random.randint(1, 2) + if dir == 1: + heading = "north" + if dir == 2: + heading = "south" + + if heading == "west": + if bw_image[location[0], location[1] - 1] < color_threshold: + location[1] = location[1] - 1 + heading = "west" + else: + dir = random.randint(1, 2) + if dir == 1: + heading = "north" + if dir == 2: + heading = "south" + + if heading == "north": + if bw_image[location[0] - 1, location[1]] < color_threshold: + location[0] = location[0] - 1 + heading = "north" + else: + dir = random.randint(1, 2) + if dir == 1: + heading = "west" + if dir == 2: + heading = "east" + # Log where our particle travels across the dot + coords.append([location[0], location[1]]) + else: + scrap_dot = True # We just don't have enough space around the dot, discard this one, and move on + if not scrap_dot: + # get the size of the dot surrounding the dot + x_coords = np.array(coords)[:, 0] + y_coords = np.array(coords)[:, 1] + hsquaresize = max(list(x_coords)) - min(list(x_coords)) + vsquaresize = max(list(y_coords)) - min(list(y_coords)) + # Create the bounding coordinates of the rectangle surrounding the dot + # Program uses the dotsize + half of the dotsize to ensure we get all that color fringing + extra_space_factor = 0.45 + top_left_x = (min(list(x_coords)) - int(hsquaresize * extra_space_factor)) + btm_right_x = max(list(x_coords)) + int(hsquaresize * extra_space_factor) + top_left_y = (min(list(y_coords)) - int(vsquaresize * extra_space_factor)) + btm_right_y = max(list(y_coords)) + int(vsquaresize * extra_space_factor) + # Overwrite the area of the dot to ensure we don't use it again + bw_image[top_left_x:btm_right_x, top_left_y:btm_right_y] = 255 + # Add the color version of the dot to the list to send off, along with some coordinates. + dots.append(rgb_image[top_left_x:btm_right_x, top_left_y:btm_right_y]) + dots_location.append([top_left_x, top_left_y]) + else: + # Dot was too close to the image border to be useable + pass + return dots, dots_location |