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color_detection_class.py
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from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import webcolors
from PIL import Image
import cv2
import numpy as np
class ColorDetection:
def __init__(self,pasted_crops_image):
self.pasted_crops_image = pasted_crops_image
def centroid_histogram(self,clt):
"""
:param clt:
:return:
"""
numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
(hist, _) = np.histogram(clt.labels_, bins=numLabels)
hist = hist.astype("float")
hist /= hist.sum()
return hist
def plot_colors(self,hist, centroids):
"""
:param hist:
:param centroids:
:return:
"""
bar = np.zeros((50, 300, 3), dtype="uint8")
startX = 0
for (percent, color) in zip(hist, centroids):
endX = startX + (percent * 300)
cv2.rectangle(bar, (int(startX), 0), (int(endX), 50), color.astype("uint8").tolist(), -1)
startX = endX
return bar
def closest_colour(self,requested_colour):
"""
:param requested_colour:
:return:
"""
min_colours = {}
webcolors.css3_hex_to_names['#000080'] = 'maroon'
webcolors.css3_hex_to_names['#ff69b4'] = 'hotpink'
webcolors.css3_hex_to_names['#ff1493'] = 'deeppink'
webcolors.css3_hex_to_names['#c71585'] = 'mediumvoiletred'
webcolors.css3_hex_to_names['#660033'] = 'darkpink'
webcolors.css3_hex_to_names['#7d3759'] = 'darkpink'
webcolors.css3_hex_to_names['#ffff00'] = 'yellow'
webcolors.css3_hex_to_names['#cdcd00'] = 'yellow'
webcolors.css3_hex_to_names['#b8b800'] = 'yellow'
webcolors.css3_hex_to_names['#a4a400'] = 'yellow'
webcolors.css3_hex_to_names['#8f8f00'] = 'yellow'
webcolors.css3_hex_to_names['#7b7b00'] = 'yellow'
webcolors.css3_hex_to_names['#999900'] = 'yellow'
webcolors.css3_hex_to_names['#666600'] = 'darkyellow'
webcolors.css3_hex_to_names['#333300'] = 'darkyellow'
webcolors.css3_hex_to_names['#969623'] = 'darkyellow'
webcolors.css3_hex_to_names['#ff4500'] = 'orangered'
webcolors.css3_hex_to_names['#ff8c00'] = 'darkorange'
webcolors.css3_hex_to_names['#ff7f50'] = 'coral'
webcolors.css3_hex_to_names['#994c00'] = 'darkorange'
for key, name in webcolors.css3_hex_to_names.items():
r_c, g_c, b_c = webcolors.hex_to_rgb(key)
rd = (r_c - requested_colour[0]) ** 2
gd = (g_c - requested_colour[1]) ** 2
bd = (b_c - requested_colour[2]) ** 2
min_colours[(rd + gd + bd)] = name
return min_colours[min(min_colours.keys())]
def get_colour_name(self,requested_colour):
"""
:param requested_colour:
:return:
"""
try:
closest_name = actual_name = webcolors.rgb_to_name(requested_colour)
except ValueError:
closest_name = self.closest_colour(requested_colour)
actual_name = None
return actual_name, closest_name
def preprocessing_and_nearest_color_from_dict(self):
"""
:return:
"""
image = self.pasted_crops_image
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = image.reshape((image.shape[0] * image.shape[1], 3))
clt = KMeans(n_clusters=3)
clt.fit(image)
hist = self.centroid_histogram(clt)
bar = self.plot_colors(hist, clt.cluster_centers_)
bar = Image.fromarray(bar)
size = bar.size
colors_list = bar.getcolors(size[0] * size[1])
rgb_count_dict = {}
for count, rgb in colors_list:
rgb_count_dict[rgb] = count
key_to_delete = max(rgb_count_dict, key=lambda k: rgb_count_dict[k])
del rgb_count_dict[key_to_delete]
key_of_max_count = max(rgb_count_dict, key=lambda k: rgb_count_dict[k])
max_count = rgb_count_dict[key_of_max_count]
key_of_min_count = min(rgb_count_dict, key=lambda k: rgb_count_dict[k])
min_count = rgb_count_dict[key_of_min_count]
threshold_value = ((rgb_count_dict[key_of_max_count]) / 2) + ((rgb_count_dict[key_of_max_count]) / 10)
for rgb, count in list(rgb_count_dict.items()):
if count < threshold_value:
rgb_count_dict.pop(rgb)
key_of_max_count = max(rgb_count_dict, key=lambda k: rgb_count_dict[k])
max_count = rgb_count_dict[key_of_max_count]
key_of_min_count = min(rgb_count_dict, key=lambda k: rgb_count_dict[k])
min_count = rgb_count_dict[key_of_min_count]
if max_count - min_count > 1000:
rgb_count_dict.pop(key_of_min_count)
print("colors dict", rgb_count_dict)
rgb_list = []
for rgb in list(rgb_count_dict.keys()):
rgb_list.append(rgb)
final_detected_colors = []
for rgb in rgb_list:
requested_colour = rgb
actual_name, closest_name = self.get_colour_name(requested_colour)
print ("Actual colour name:", actual_name, ", closest colour name:", closest_name)
final_detected_colors.append(closest_name)
return final_detected_colors