问题在于将直方图而不是像素强度数组传递给GaussianMixture.fit gmm = gmm.fit(hist)。我还发现，n_components = 6视觉上适合此特定直方图的最低要求。
import numpy as np
import cv2
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture
# Read image
img = cv2.imread("test.jpg",0)
hist = cv2.calcHist([img],[0],None,[256],[0,256])
hist[0] = 0 # Removes background pixels
data = img.ravel()
data = data[data != 0]
data = data[data != 1] #Removes background pixels (intensities 0 and 1)
# Fit GMM
gmm = GaussianMixture(n_components = 6)
gmm = gmm.fit(X=np.expand_dims(data,1))
# Evaluate GMM
gmm_x = np.linspace(0,253,256)
gmm_y = np.exp(gmm.score_samples(gmm_x.reshape(-1,1)))
# Plot histograms and gaussian curves
fig, ax = plt.subplots()
ax.hist(img.ravel(),255,[2,256], normed=True)
ax.plot(gmm_x, gmm_y, color="crimson", lw=4, label="GMM")
ax.set_ylabel("Frequency")
ax.set_xlabel("Pixel Intensity")
plt.legend()
plt.show()
602392714
清零编程群