76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
import numpy as np
|
|
import cv2
|
|
|
|
|
|
def EuclideanDistance(detected_color, target_colors):
|
|
return np.linalg.norm(detected_color - target_colors, axis=1)
|
|
|
|
|
|
def ManhattanDistance(detected_color, target_colors):
|
|
return np.sum(np.abs(detected_color - target_colors), axis=1)
|
|
|
|
|
|
def CosineSimilarity(detected_color, target_colors):
|
|
return -np.dot(target_colors, detected_color) / (np.linalg.norm(detected_color) * np.linalg.norm(target_colors, axis=1))
|
|
|
|
|
|
def RGBWeightedDistance(detected_color, target_colors):
|
|
detected_color = np.array(detected_color)
|
|
target_colors = np.array(target_colors)
|
|
|
|
weights = np.array([0.299, 0.587, 0.114])
|
|
|
|
weighted_detected_color = np.dot(detected_color, weights)
|
|
weighted_target_colors = np.dot(target_colors, weights)
|
|
|
|
return np.abs(weighted_detected_color - weighted_target_colors)
|
|
|
|
|
|
def RGBWeightedSimilarity(detected_color, target_colors):
|
|
detected_color = np.array(detected_color)
|
|
target_colors = np.array(target_colors)
|
|
|
|
weights = np.array([0.299, 0.587, 0.114])
|
|
|
|
weighted_detected_color = np.dot(detected_color, weights)
|
|
weighted_target_colors = np.dot(target_colors, weights)
|
|
|
|
dot_products = np.dot(weighted_detected_color, weighted_target_colors)
|
|
norm1 = np.linalg.norm(weighted_detected_color)
|
|
norm2 = np.linalg.norm(weighted_target_colors)
|
|
|
|
return -dot_products / (norm1 * norm2)
|
|
|
|
|
|
def HSVColorSimilarity(detected_color, target_colors):
|
|
detected_color = np.array(detected_color)
|
|
target_colors = np.array(target_colors)
|
|
|
|
h1, s1, _ = detected_color
|
|
h2 = target_colors[:, 0]
|
|
s2 = target_colors[:, 1]
|
|
|
|
h1_rad = np.radians(h1)
|
|
h2_rad = np.radians(h2)
|
|
|
|
v1_x = s1 * np.cos(h1_rad)
|
|
v1_y = s1 * np.sin(h1_rad)
|
|
v1 = np.array([v1_x, v1_y])
|
|
|
|
|
|
v2_x = s2 * np.cos(h2_rad)
|
|
v2_y = s2 * np.sin(h2_rad)
|
|
v2 = np.vstack([v2_x, v2_y])
|
|
|
|
dot_products = np.dot(v1, v2)
|
|
|
|
v1_norm = np.linalg.norm(v1)
|
|
v2_norms = np.linalg.norm(v2, axis=0)
|
|
|
|
similarities = dot_products / (v1_norm * v2_norms)
|
|
|
|
return -similarities
|
|
|
|
|
|
def Blur(image, kernel_size):
|
|
return cv2.medianBlur(image.astype(np.uint8), kernel_size) |