我尝试从模特穿着产品（例如，一条裙子）的图像中提取产品颜色和背景颜色。我目前的方法涉及屏蔽皮肤和头发颜色，然后使用KMeans聚类来找到主色。然而，结果并不总是准确的，特别是当产品颜色与背景或模特的皮肤/头发相似时。
下面是我一直在使用的代码：

import cv2
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

def mask_human_colors(image):
    # Convert the image to HSV
    hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
    
    # Define a range for skin colors in HSV
    lower_skin = np.array([0, 20, 70], dtype=np.uint8)
    upper_skin = np.array([20, 255, 255], dtype=np.uint8)
    skin_mask = cv2.inRange(hsv, lower_skin, upper_skin)
    
    # Define a range for common hair colors in HSV (This is a rough estimate and might need adjustments)
    lower_hair = np.array([0, 0, 0], dtype=np.uint8)
    upper_hair = np.array([180, 255, 60], dtype=np.uint8)
    hair_mask = cv2.inRange(hsv, lower_hair, upper_hair)
    
    # Combine the skin and hair masks
    human_mask = cv2.bitwise_or(skin_mask, hair_mask)
    
    # Mask the image to remove human colors
    masked_image = cv2.bitwise_and(image, image, mask=~human_mask)
    
    return masked_image

def extract_top_colors(image, k=2):
    pixels = image.reshape(-1, 3)
    pixels = pixels[np.any(pixels != [0, 0, 0], axis=1)]  # Removing black pixels
    kmeans = KMeans(n_clusters=k, n_init=10)
    kmeans.fit(pixels)
    sorted_labels = np.argsort(np.bincount(kmeans.labels_))[::-1]
    colors = [kmeans.cluster_centers_[label].astype(int).tolist() for label in sorted_labels]
    return colors

# Usage:

image_path = '/content/white_dress.JPG'
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Mask human colors
masked_image = mask_human_colors(image)

# Extract top colors
dominant_colors = extract_top_colors(masked_image, k=2)

print(f"1st Dominant Color (RGB): {dominant_colors[0]}")
print(f"2nd Dominant Color (RGB): {dominant_colors[1]}")

fig, ax = plt.subplots(1, 3, figsize=(20, 5))
ax[0].imshow(image)
ax[0].set_title("Original Image")
ax[1].imshow(masked_image)
ax[1].set_title("Image after Masking Human Colors")
ax[2].imshow([dominant_colors])
ax[2].set_title("Top 2 Dominant Colors")
plt.tight_layout()
plt.show()

下面是我正在使用的示例图像：x1c 0d1x
在某些情况下，提取的产品颜色比实际产品的颜色浅，或者它会选择背景颜色。
目前的解决方案结果显示：

我正在寻找提高颜色提取准确性的建议或可能更有效的替代技术。
谢谢你