/
color_correction.py
751 lines (625 loc) · 30.9 KB
/
color_correction.py
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# Color Corrections Functions
import os
import errno
import cv2
import numpy as np
from plantcv.plantcv import print_image
from plantcv.plantcv import plot_image
from plantcv.plantcv import fatal_error
from plantcv.plantcv import params
from plantcv.plantcv.roi import circle
from plantcv.plantcv import params
def get_color_matrix(rgb_img, mask):
""" Calculate the average value of pixels in each color chip for each color channel.
Inputs:
rgb_img = RGB image with color chips visualized
mask = a gray-scale img with unique values for each segmented space, representing unique, discrete
color chips.
Outputs:
color_matrix = a 22x4 matrix containing the average red value, average green value, and average blue value
for each color chip.
headers = a list of 4 headers corresponding to the 4 columns of color_matrix respectively
:param rgb_img: numpy.ndarray
:param mask: numpy.ndarray
:return headers: string array
:return color_matrix: numpy.ndarray
"""
# Autoincrement the device counter
params.device += 1
# Check for RGB input
if len(np.shape(rgb_img)) != 3:
fatal_error("Input rgb_img is not an RGB image.")
# Check mask for gray-scale
if len(np.shape(mask)) != 2:
fatal_error("Input mask is not an gray-scale image.")
# create empty color_matrix
color_matrix = np.zeros((len(np.unique(mask))-1, 4))
# create headers
headers = ["chip_number", "r_avg", "g_avg", "b_avg"]
# declare row_counter variable and initialize to 0
row_counter = 0
# for each unique color chip calculate each average RGB value
for i in np.unique(mask):
if i != 0:
chip = rgb_img[np.where(mask == i)]
color_matrix[row_counter][0] = i
color_matrix[row_counter][1] = np.mean(chip[:, 2])
color_matrix[row_counter][2] = np.mean(chip[:, 1])
color_matrix[row_counter][3] = np.mean(chip[:, 0])
row_counter += 1
return headers, color_matrix
def get_matrix_m(target_matrix, source_matrix):
""" Calculate Moore-Penrose inverse matrix for use in calculating transformation_matrix
Inputs:
target_matrix = a 22x4 matrix containing the average red value, average green value, and average blue value
for each color chip.
source_matrix = a 22x4 matrix containing the average red value, average green value, and average blue value
for each color chip.
Outputs:
matrix_a = a concatenated 22x9 matrix of source_matrix red, green, and blue values to the powers 1, 2, 3
matrix_m = a 9x22 Moore-Penrose inverse matrix
matrix_b = a 22x9 matrix of linear, square, and cubic rgb values from target_img
:param target_matrix: numpy.ndarray
:param source_matrix: numpy.ndarray
:return matrix_a: numpy.ndarray
:return matrix_m: numpy.ndarray
:return matrix_b: numpy.ndarray
"""
# Autoincrement the device counter
params.device += 1
# if the number of chips in source_img match the number of chips in target_matrix
if np.shape(target_matrix) == np.shape(source_matrix):
t_cc, t_r, t_g, t_b = np.split(target_matrix, 4, 1)
s_cc, s_r, s_g, s_b = np.split(source_matrix, 4, 1)
else:
combined_matrix = np.zeros((np.ma.size(source_matrix, 0), 7))
row_count = 0
for r in range(0, np.ma.size(target_matrix, 0)):
for i in range(0, np.ma.size(source_matrix, 0)):
if target_matrix[r][0] == source_matrix[i][0]:
combined_matrix[row_count][0] = target_matrix[r][0]
combined_matrix[row_count][1] = target_matrix[r][1]
combined_matrix[row_count][2] = target_matrix[r][2]
combined_matrix[row_count][3] = target_matrix[r][3]
combined_matrix[row_count][4] = source_matrix[i][1]
combined_matrix[row_count][5] = source_matrix[i][2]
combined_matrix[row_count][6] = source_matrix[i][3]
row_count += 1
t_cc, t_r, t_g, t_b, s_r, s_g, s_b = np.split(combined_matrix, 7, 1)
t_r2 = np.square(t_r)
t_r3 = np.power(t_r, 3)
t_g2 = np.square(t_g)
t_g3 = np.power(t_g, 3)
t_b2 = np.square(t_b)
t_b3 = np.power(t_b, 3)
s_r2 = np.square(s_r)
s_r3 = np.power(s_r, 3)
s_g2 = np.square(s_g)
s_g3 = np.power(s_g, 3)
s_b2 = np.square(s_b)
s_b3 = np.power(s_b, 3)
# create matrix_a
matrix_a = np.concatenate((s_r, s_g, s_b, s_b2, s_g2, s_r2, s_b3, s_g3, s_r3), 1)
# create matrix_m
matrix_m = np.linalg.solve(np.matmul(matrix_a.T, matrix_a), matrix_a.T)
# create matrix_b
matrix_b = np.concatenate((t_r, t_r2, t_r3, t_g, t_g2, t_g3, t_b, t_b2, t_b3), 1)
return matrix_a, matrix_m, matrix_b
def calc_transformation_matrix(matrix_m, matrix_b):
""" Calculates transformation matrix (transformation_matrix).
Inputs:
matrix_m = a 9x22 Moore-Penrose inverse matrix
matrix_b = a 22x9 matrix of linear, square, and cubic rgb values from target_img
Outputs:
1-t_det = "deviance" the measure of how greatly the source image deviates from the target image's color space.
Two images of the same color space should have a deviance of ~0.
transformation_matrix = a 9x9 matrix of linear, square, and cubic transformation coefficients
:param matrix_m: numpy.ndarray
:param matrix_b: numpy.ndarray
:return red: numpy.ndarray
:return blue: numpy.ndarray
:return green: numpy.ndarray
:return 1-t_det: float
:return transformation_matrix: numpy.ndarray
"""
# check matrix_m and matrix_b are matrices
if len(np.shape(matrix_b)) != 2 or len(np.shape(matrix_m)) != 2:
fatal_error("matrix_m and matrix_b must be n x m matrices such that m,n != 1.")
# check matrix_b has 9 columns
if np.shape(matrix_b)[1] != 9:
fatal_error("matrix_b must have 9 columns.")
# check matrix_m and matrix_b for multiplication
if np.shape(matrix_m)[0] != np.shape(matrix_b)[1] or np.shape(matrix_m)[1] != np.shape(matrix_b)[0]:
fatal_error("Cannot multiply matrices.")
# Autoincrement the device counter
params.device += 1
t_r, t_r2, t_r3, t_g, t_g2, t_g3, t_b, t_b2, t_b3 = np.split(matrix_b, 9, 1)
# multiply each 22x1 matrix from target color space by matrix_m
red = np.matmul(matrix_m, t_r)
green = np.matmul(matrix_m, t_g)
blue = np.matmul(matrix_m, t_b)
red2 = np.matmul(matrix_m, t_r2)
green2 = np.matmul(matrix_m, t_g2)
blue2 = np.matmul(matrix_m, t_b2)
red3 = np.matmul(matrix_m, t_r3)
green3 = np.matmul(matrix_m, t_g3)
blue3 = np.matmul(matrix_m, t_b3)
# concatenate each product column into 9X9 transformation matrix
transformation_matrix = np.concatenate((red, green, blue, red2, green2, blue2, red3, green3, blue3), 1)
# find determinant of transformation matrix
t_det = np.linalg.det(transformation_matrix)
return 1-t_det, transformation_matrix
def apply_transformation_matrix(source_img, target_img, transformation_matrix):
""" Apply the transformation matrix to the source_image.
Inputs:
source_img = an RGB image to be corrected to the target color space
target_img = an RGB image with the target color space
transformation_matrix = a 9x9 matrix of tranformation coefficients
Outputs:
corrected_img = an RGB image in correct color space
:param source_img: numpy.ndarray
:param target_img: numpy.ndarray
:param transformation_matrix: numpy.ndarray
:return corrected_img: numpy.ndarray
"""
# check transformation_matrix for 9x9
if np.shape(transformation_matrix) != (9, 9):
fatal_error("transformation_matrix must be a 9x9 matrix of transformation coefficients.")
# Check for RGB input
if len(np.shape(source_img)) != 3:
fatal_error("Source_img is not an RGB image.")
# Autoincrement the device counter
params.device += 1
# split transformation_matrix
red, green, blue, red2, green2, blue2, red3, green3, blue3 = np.split(transformation_matrix, 9, 1)
# find linear, square, and cubic values of source_img color channels
source_b, source_g, source_r = cv2.split(source_img)
source_b2 = np.square(source_b)
source_b3 = np.power(source_b, 3)
source_g2 = np.square(source_g)
source_g3 = np.power(source_g, 3)
source_r2 = np.square(source_r)
source_r3 = np.power(source_r, 3)
# apply linear model to source color channels
b = 0 + source_r * blue[0] + source_g * blue[1] + source_b * blue[2] + source_r2 * blue[3] + source_g2 * blue[
4] + source_b2 * blue[5] + source_r3 * blue[6] + source_g3 * blue[7] + source_b3 * blue[8]
g = 0 + source_r * green[0] + source_g * green[1] + source_b * green[2] + source_r2 * green[3] + source_g2 * green[
4] + source_b2 * green[5] + source_r3 * green[6] + source_g3 * green[7] + source_b3 * green[8]
r = 0 + source_r * red[0] + source_g * red[1] + source_b * red[2] + source_r2 * red[3] + source_g2 * red[
4] + source_b2 * red[5] + source_r3 * red[6] + source_g3 * red[7] + source_b3 * red[8]
# merge corrected color channels onto source_image
bgr = [b, g, r]
corrected_img = cv2.merge(bgr)
# round corrected_img elements to be within range and of the correct data type
corrected_img = np.rint(corrected_img)
corrected_img[np.where(corrected_img > 255)] = 255
corrected_img = corrected_img.astype(np.uint8)
if params.debug == "print":
# If debug is print, save the image to a file
print_image(corrected_img, os.path.join(params.debug_outdir, str(params.device) + "_corrected.png"))
elif params.debug == "plot":
# If debug is plot, print a horizontal view of source_img, corrected_img, and target_img to the plotting device
# plot horizontal comparison of source_img, corrected_img (with rounded elements) and target_img
plot_image(np.hstack([source_img, corrected_img, target_img]))
# return corrected_img
return corrected_img
def save_matrix(matrix, filename):
""" Serializes a matrix as an numpy.ndarray object and save to a .npz file.
Inputs:
matrix = a numpy.matrix
filename = name of file to which matrix will be saved. Must end in .npz
:param matrix: numpy.ndarray
:param filename: string ending in ".npz"
"""
if ".npz" not in filename:
fatal_error("File must be an .npz file.")
# Autoincrement the device counter
params.device += 1
np.savez(filename, matrix)
def load_matrix(filename):
""" Deserializes from file an numpy.ndarray object as a matrix
Inputs:
filename = .npz file to which a numpy.matrix or numpy.ndarray is saved
Outputs:
matrix = a numpy.matrix
:param filename: string ending in ".npz"
:return matrix: numpy.matrix
"""
# Autoincrement the device counter
params.device += 1
matrix_file = np.load(filename, encoding="latin1")
matrix = matrix_file['arr_0']
np.asmatrix(matrix)
return matrix
def correct_color(target_img, target_mask, source_img, source_mask, output_directory):
"""Takes a target_img with preferred color_space and converts source_img to that color_space.
Inputs:
target_img = an RGB image with color chips visualized
source_img = an RGB image with color chips visualized
target_mask = a gray-scale image with color chips and background each represented with unique values
target_mask = a gray-scale image with color chips and background each represented as unique values
output_directory = a file path to which outputs will be saved
Outputs:
target_matrix = saved in .npz file, a 22x4 matrix containing the average red value, average green value, and
average blue value for each color chip.
source_matrix = saved in .npz file, a 22x4 matrix containing the average red value, average green value, and
average blue value for each color chip.
transformation_matrix = saved in .npz file, a 9x9 transformation matrix
corrected_img = the source_img converted to the correct color space.
:param target_img: numpy.ndarray
:param source_img: numpy.ndarray
:param target_mask: numpy.ndarray
:param source_mask: numpy.ndarray
:param output_directory: string
:return target_matrix: numpy.matrix
:return source_matrix: numpy.matrix
:return transformation_matrix: numpy.matrix
:return corrected_img: numpy.ndarray
"""
# check output_directory, if it does not exist, create
if not os.path.exists(output_directory):
os.mkdir(output_directory)
# get color matrices for target and source images
target_headers, target_matrix = get_color_matrix(target_img, target_mask)
source_headers, source_matrix = get_color_matrix(source_img, source_mask)
# save target and source matrices
save_matrix(target_matrix, os.path.join(output_directory, "target_matrix.npz"))
save_matrix(source_matrix, os.path.join(output_directory, "source_matrix.npz"))
# get matrix_m
matrix_a, matrix_m, matrix_b = get_matrix_m(target_matrix=target_matrix, source_matrix=source_matrix)
# calculate transformation_matrix and save
deviance, transformation_matrix = calc_transformation_matrix(matrix_m, matrix_b)
save_matrix(transformation_matrix, os.path.join(output_directory, "transformation_matrix.npz"))
# apply transformation
corrected_img = apply_transformation_matrix(source_img, target_img, transformation_matrix)
return target_matrix, source_matrix, transformation_matrix, corrected_img
def create_color_card_mask(rgb_img, radius, start_coord, spacing, nrows, ncols, exclude=[]):
"""Create a labeled mask for color card chips
Inputs:
rgb_img = Input RGB image data containing a color card.
radius = Radius of color masks.
start_coord = Two-element tuple of the first chip mask starting x and y coordinate.
spacing = Two-element tuple of the horizontal and vertical spacing between chip masks.
nrows = Number of chip rows.
ncols = Number of chip columns.
exclude = Optional list of chips to exclude. List in largest to smallest index (e.g. [20, 0])
Returns:
mask = Labeled mask of chips
:param rgb_img: numpy.ndarray
:param radius: int
:param start_coord: tuple
:param spacing: tuple
:param nrows: int
:param ncols: int
:param exclude: list
:return mask: numpy.ndarray
"""
# Autoincrement the device counter
params.device += 1
# Initialize chip list
chips = []
# Store user debug
debug = params.debug
# Temporarily disable debug
params.debug = None
# Loop over each color card row
for i in range(0, nrows):
# The upper left corner is the y starting coordinate + the chip offset * the vertical spacing between chips
y = start_coord[1] + i * spacing[1]
# Loop over each column
for j in range(0, ncols):
# The upper left corner is the x starting coordinate + the chip offset * the
# horizontal spacing between chips
x = start_coord[0] + j * spacing[0]
# Create a chip ROI
chips.append(circle(img=rgb_img, x=x, y=y, r=radius))
# Remove any excluded chips
for chip in exclude:
del chips[chip]
# Create mask
mask = np.zeros(shape=np.shape(rgb_img)[:2], dtype=np.uint8())
# Mask label index
i = 1
# Draw labeled chip boxes on the mask
for chip in chips:
mask = cv2.drawContours(mask, chip[0], -1, (i * 10), -1)
i += 1
# Reset debug
params.debug = debug
if params.debug is not None:
# Create a copy of the input image for plotting
canvas = np.copy(rgb_img)
# Draw chip ROIs on the canvas image
for chip in chips:
cv2.drawContours(canvas, chip[0], -1, (255, 255, 0), params.line_thickness)
if params.debug == "print":
print_image(img=canvas, filename=os.path.join(params.debug_outdir,
str(params.device) + "_color_card_mask_rois.png"))
print_image(img=mask, filename=os.path.join(params.debug_outdir,
str(params.device) + "_color_card_mask.png"))
elif params.debug == "plot":
plot_image(canvas)
return mask
def quick_color_check(target_matrix, source_matrix, num_chips):
""" Quickly plot target matrix values against source matrix values to determine
over saturated color chips or other issues.
Inputs:
source_matrix = a 22x4 matrix containing the average red value, average green value, and
average blue value for each color chip of the source image
target_matrix = a 22x4 matrix containing the average red value, average green value, and
average blue value for each color chip of the target image
num_chips = number of color card chips included in the matrices (integer)
:param source_matrix: numpy.ndarray
:param target_matrix: numpy.ndarray
:param num_chips: int
"""
# Imports
from plotnine import ggplot, geom_point, geom_smooth, theme_seaborn, facet_grid, geom_label, scale_x_continuous, \
scale_y_continuous, scale_color_manual, aes
import pandas as pd
# Extract and organize matrix info
tr = target_matrix[:num_chips, 1:2]
tg = target_matrix[:num_chips, 2:3]
tb = target_matrix[:num_chips, 3:4]
sr = source_matrix[:num_chips, 1:2]
sg = source_matrix[:num_chips, 2:3]
sb = source_matrix[:num_chips, 3:4]
# Create columns of color labels
red = []
blue = []
green = []
for i in range(num_chips):
red.append('red')
blue.append('blue')
green.append('green')
# Make a column of chip numbers
chip = np.arange(0, num_chips).reshape((num_chips, 1))
chips = np.row_stack((chip, chip, chip))
# Combine info
color_data_r = np.column_stack((sr, tr, red))
color_data_g = np.column_stack((sg, tg, green))
color_data_b = np.column_stack((sb, tb, blue))
all_color_data = np.row_stack((color_data_b, color_data_g, color_data_r))
# Create a dataframe with headers
dataset = pd.DataFrame({'source': all_color_data[:, 0], 'target': all_color_data[:, 1],
'color': all_color_data[:, 2]})
# Add chip numbers to the dataframe
dataset['chip'] = chips
dataset = dataset.astype({'color': str, 'chip': str, 'target': float, 'source': float})
# Make the plot
p1 = ggplot(dataset, aes(x='target', y='source', color='color', label='chip')) + \
geom_point(show_legend=False, size=2) + \
geom_smooth(method='lm', size=.5, show_legend=False) + \
theme_seaborn() + facet_grid('.~color') + \
geom_label(angle=15, size=7, nudge_y=-.25, nudge_x=.5, show_legend=False) + \
scale_x_continuous(limits=(-5, 270)) + scale_y_continuous(limits=(-5, 275)) + \
scale_color_manual(values=['blue', 'green', 'red'])
# Reset debug
if params.debug is not None:
if params.debug == 'print':
p1.save(os.path.join(params.debug_outdir, 'color_quick_check.png'))
elif params.debug == 'plot':
print(p1)
def find_color_card(rgb_img, threshold_type='adaptgauss', threshvalue=125, blurry=False, background='dark'):
"""Automatically detects a color card and output info to use in create_color_card_mask function
Inputs:
rgb_img = Input RGB image data containing a color card.
threshold = Threshold method, either 'normal', 'otsu', or 'adaptgauss', optional (default 'adaptgauss')
threshvalue = Thresholding value, optional (default 125)
blurry = Bool (default False) if True then image sharpening applied
background = Type of image background either 'dark' or 'light' (default 'dark'); if 'light' then histogram
expansion applied to better detect edges, but histogram expansion will be hindered if there
is a dark background
Returns:
df = Dataframe containing information about the filtered contours
start_coord = Two element tuple of starting coordinates, location of the top left pixel detected
spacing = Two element tuple of spacing between centers of chips
:param rgb_img: numpy.ndarray
:param threshold: str
:param threshvalue: int
:param blurry: bool
:param background: str
:return df: pandas.core.frame.DataFrame
:return start_coord: tuple
:return spacing: tuple
"""
# Imports
import skimage
import pandas as pd
from scipy.spatial.distance import squareform, pdist
# Get image attributes
height, width, channels = rgb_img.shape
totalpx = float(height * width)
# Minimum and maximum square size based upon 12 MP image
minarea = 1000. / 12000000. * totalpx
maxarea = 8000000. / 12000000. * totalpx
# Create gray image for further processing
gray_img = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY)
# Laplacian Fourier Transform detection of blurriness
blurfactor = cv2.Laplacian(gray_img, cv2.CV_64F).var()
# If image is blurry then try to deblur using kernel
if blurry:
# from https://www.packtpub.com/mapt/book/Application+Development/9781785283932/2/ch02lvl1sec22/Sharpening
kernel = np.array([[-1, -1, -1, -1, -1],
[-1, 2, 2, 2, -1],
[-1, 2, 8, 2, -1],
[-1, 2, 2, 2, -1],
[-1, -1, -1, -1, -1]]) / 8.0
# Store result back out for further processing
gray_img = cv2.filter2D(gray_img, -1, kernel)
# In darker samples, the expansion of the histogram hinders finding the squares due to problems with the otsu
# thresholding. If your image has a bright background then apply
if background == 'light':
clahe = cv2.createCLAHE(clipLimit=3.25, tileGridSize=(4, 4))
# apply CLAHE histogram expansion to find squares better with canny edge detection
gray_img = clahe.apply(gray_img)
elif background != 'dark':
fatal_error('Background parameter ' + str(background) + ' is not "light" or "dark"!')
# Thresholding
if threshold_type == "otsu":
# Blur slightly so defects on card squares and background patterns are less likely to be picked up
gaussian = cv2.GaussianBlur(gray_img, (5, 5), 0)
ret, threshold = cv2.threshold(gaussian, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
elif threshold_type == "normal":
# Blur slightly so defects on card squares and background patterns are less likely to be picked up
gaussian = cv2.GaussianBlur(gray_img, (5, 5), 0)
ret, threshold = cv2.threshold(gaussian, threshvalue, 255, cv2.THRESH_BINARY)
elif threshold_type == "adaptgauss":
# Blur slightly so defects on card squares and background patterns are less likely to be picked up
gaussian = cv2.GaussianBlur(gray_img, (11, 11), 0)
threshold = cv2.adaptiveThreshold(gaussian, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 51, 2)
else:
fatal_error('Threshold ' + str(threshold_type) + ' is not "otsu", "normal", or "adaptgauss"!')
# Apply automatic Canny edge detection using the computed median
edges = skimage.feature.canny(threshold)
edges.dtype = 'uint8'
# Compute contours to find the squares of the card
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]
# Variable of which contour is which
mindex = []
# Variable to store moments
mu = []
# Variable to x,y coordinates in tuples
mc = []
# Variable to x coordinate as integer
mx = []
# Variable to y coordinate as integer
my = []
# Variable to store area
marea = []
# Variable to store whether something is a square (1) or not (0)
msquare = []
# Variable to store square approximation coordinates
msquarecoords = []
# Variable to store child hierarchy element
mchild = []
# Fitted rectangle height
mheight = []
# Fitted rectangle width
mwidth = []
# Ratio of height/width
mwhratio = []
# Extract moments from contour image
for x in range(0, len(contours)):
mu.append(cv2.moments(contours[x]))
marea.append(cv2.contourArea(contours[x]))
mchild.append(int(hierarchy[0][x][2]))
mindex.append(x)
# Cycle through moment data and compute location for each moment
for m in mu:
if m['m00'] != 0: # This is the area term for a moment
mc.append((int(m['m10'] / m['m00']), int(m['m01']) / m['m00']))
mx.append(int(m['m10'] / m['m00']))
my.append(int(m['m01'] / m['m00']))
else:
mc.append((0, 0))
mx.append((0))
my.append((0))
# Loop over our contours and extract data about them
for index, c in enumerate(contours):
# Area isn't 0, but greater than min-area and less than max-area
if marea[index] != 0 and minarea < marea[index] < maxarea:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.1 * peri, True)
center, wh, angle = cv2.minAreaRect(c) # Rotated rectangle
mwidth.append(wh[0])
mheight.append(wh[1])
mwhratio.append(wh[0] / wh[1])
msquare.append(len(approx))
# If the approx contour has 4 points then we can assume we have 4-sided objects
if len(approx) == 4 or len(approx) == 5:
msquarecoords.append(approx)
else: # It's not square
#msquare.append(0)
msquarecoords.append(0)
else: # Contour has area of 0, not interesting
msquare.append(0)
msquarecoords.append(0)
mwidth.append(0)
mheight.append(0)
mwhratio.append(0)
# Make a pandas df from data for filtering out junk
locarea = {'index': mindex, 'X': mx, 'Y': my, 'width': mwidth, 'height': mheight, 'WHratio': mwhratio,
'Area': marea, 'square': msquare, 'child': mchild}
df = pd.DataFrame(locarea)
# Add calculated blur factor to output
df['blurriness'] = blurfactor
# Filter df for attributes that would isolate squares of reasonable size
df = df[(df['Area'] > minarea) & (df['Area'] < maxarea) & (df['child'] != -1) &
(df['square'].isin([4, 5])) & (df['WHratio'] < 1.2) & (df['WHratio'] > 0.85)]
# Filter nested squares from dataframe, was having issues with median being towards smaller nested squares
df = df[~(df['index'].isin(df['index'] + 1))]
# Count up squares that are within a given radius, more squares = more likelihood of them being the card
# Median width of square time 2.5 gives proximity radius for searching for similar squares
median_sq_width_px = df["width"].median()
# Squares that are within 6 widths of the current square
pixeldist = median_sq_width_px * 6
# Computes euclidean distance matrix for the x and y contour centroids
distmatrix = pd.DataFrame(squareform(pdist(df[['X', 'Y']])))
# Add up distances that are less than ones have distance less than pixeldist pixels
distmatrixflat = distmatrix.apply(lambda dist: dist[dist <= pixeldist].count() - 1, axis=1)
# Append distprox summary to dataframe
df = df.assign(distprox=distmatrixflat.values)
# Compute how similar in area the squares are. lots of similar values indicates card
# isolate area measurements
filtered_area = df['Area']
# Create empty matrix for storing comparisons
sizecomp = np.zeros((len(filtered_area), len(filtered_area)))
# Double loop through all areas to compare to each other
for p in range(0, len(filtered_area)):
for o in range(0, len(filtered_area)):
big = max(filtered_area.iloc[p], filtered_area.iloc[o])
small = min(filtered_area.iloc[p], filtered_area.iloc[o])
pct = 100. * (small / big)
sizecomp[p][o] = pct
# How many comparisons given 90% square similarity
sizematrix = pd.DataFrame(sizecomp).apply(lambda sim: sim[sim >= 90].count() - 1, axis=1)
# Append sizeprox summary to dataframe
df = df.assign(sizeprox=sizematrix.values)
# Reorder dataframe for better printing
df = df[['index', 'X', 'Y', 'width', 'height', 'WHratio', 'Area', 'square', 'child',
'blurriness', 'distprox', 'sizeprox']]
# Loosely filter for size and distance (relative size to median)
minsqwidth = median_sq_width_px * 0.80
maxsqwidth = median_sq_width_px * 1.2
df = df[(df['distprox'] >= 5) & (df['sizeprox'] >= 5) & (df['width'] > minsqwidth) &
(df['width'] < maxsqwidth)]
# Filter for proximity again to root out stragglers
# Find and count up squares that are within given radius,
# more squares = more likelihood of them being the card
# Median width of square time 2.5 gives proximity radius for searching for similar squares
median_sq_width_px = df["width"].median()
# Squares that are within 6 widths of the current square
pixeldist = median_sq_width_px * 5
# Computes euclidean distance matrix for the x and y contour centroids
distmatrix = pd.DataFrame(squareform(pdist(df[['X', 'Y']])))
# Add up distances that are less than ones have distance less than pixeldist pixels
distmatrixflat = distmatrix.apply(lambda dist: dist[dist <= pixeldist].count() - 1, axis=1)
# Append distprox summary to dataframe
df = df.assign(distprox=distmatrixflat.values)
# Filter results for distance proximity to other squares
df = df[(df['distprox'] >= 4)]
# Remove all not numeric values use to_numeric with parameter, errors='coerce' - it replace non numeric to NaNs:
df['X'] = pd.to_numeric(df['X'], errors='coerce')
df['Y'] = pd.to_numeric(df['Y'], errors='coerce')
# Remove NaN
df = df.dropna()
if df['X'].min() is np.nan or df['Y'].min() is np.nan:
fatal_error('No color card found under current parameters')
else:
# Extract the starting coordinate
start_coord = (df['X'].min(), df['Y'].min())
# start_coord = (int(df['X'].min()), int(df['Y'].min()))
# Calculate the range
spacingx_short = (df['X'].max() - df['X'].min()) / 3
spacingy_short = (df['Y'].max() - df['Y'].min()) / 3
spacingx_long = (df['X'].max() - df['X'].min()) / 5
spacingy_long = (df['Y'].max() - df['Y'].min()) / 5
# Chip spacing since 4x6 card assumed
spacing_short = min(spacingx_short, spacingy_short)
spacing_long = max(spacingx_long, spacingy_long)
# Smaller spacing measurement might have a chip missing
spacing = int(max(spacing_short, spacing_long))
spacing = (spacing, spacing)
return df, start_coord, spacing