107 lines
2.8 KiB
Python
107 lines
2.8 KiB
Python
#! /usr/bin/python
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import numpy as np
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from scipy.ndimage import label, find_objects, center_of_mass
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def normalize_coordinates(p, w, h):
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"""Normalize the coordinates of a point so it does not depends on the
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size of the picture.
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Args:
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p: The point (tuple-like)
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w: Width of the picture
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h: height of the picture
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Returns:
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(x,y): The normalized coordinates.
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"""
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j = float(max(w, h)) # Python2 is shitty
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A = np.array([
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[1.0/j, 0.0],
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[0.0, -1.0/j]
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])
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b = np.array([-float(w)/(2.0*j), float(h)/(2.0*j)])
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return np.array(p).dot(A) + b
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def find_targets(
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picture, threshold_blue=(140, 255), threshold_red=(0, 120),
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threshold_green=(0, 190), return_slices=False, return_binary=False):
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"""Find three blue targets in the given picture (RGB matrix).
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Args:
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picture: a 2D matrix of RGB values
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threshold_blue: interval of allowed values for blue channel.
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threshold_red: interval of allowed values for red channel
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threshold_green: interval of allowed values for green channel
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return_slices: Boolean stating if the slices locating the targets
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should be returned.
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return_binary: Boolean stating if the binary map should be returned.
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Returns:
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(H,L,R,[objects]) the positions of the targets in the picture (center of mass). objects is the list of slices controlled by the return_slices parameter.
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Raises:
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ValueError when less than three targets are found.
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"""
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blue_points = np.where(
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(threshold_red[0] <= picture[:, :, 0])
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& (picture[:, :, 0] <= threshold_red[1])
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& (threshold_green[0] <= picture[:, :, 1])
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& (picture[:, :, 1] <= threshold_green[1])
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& (threshold_blue[0] <= picture[:, :, 2])
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& (picture[:, :, 2] <= threshold_blue[1]),
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1,
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0
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)
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structure = [
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[0, 1, 0],
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[1, 1, 1],
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[0, 1, 0]
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]
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labels, n = label(blue_points, structure)
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if n < 3:
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raise ValueError("Less than three potential targets were found")
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objects = [(a[0], a[1], i+1) for i, a in enumerate(find_objects(labels))]
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objects = sorted(
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objects,
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key=lambda x: (x[0].stop - x[0].start) * (x[1].stop - x[1].start)
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)[-3:]
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coordinates = center_of_mass(
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blue_points,
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labels,
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index=[o[2] for o in objects]
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)
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# Highest point
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high = sorted(
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coordinates,
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key=lambda x: x[0]
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)
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H = tuple(reversed(high[0]))
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sides = sorted(
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high[1:],
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key=lambda x: x[1]
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)
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# Leftmost point
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L = tuple(reversed(sides[0]))
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# Rightmost point
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R = tuple(reversed(sides[-1]))
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ret = (H, L, R)
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if return_slices:
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ret = ret + (((o[0], o[1]) for o in objects),)
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if return_binary:
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ret = ret + (blue_points,)
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return ret
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