Python OpenCV实现按照像素点图片切割

import cv2
import numpy as np
import matplotlib.pyplot as plt
from skimage.segmentation import slic
import tensorflow as tf
 
# 读取图像并转换为灰度
img = cv2.imread('image.jpg', 0)
 
# 1. 阈值分割
ret, thresh = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
plt.imshow(thresh, cmap='gray')
plt.title('Thresholding')
plt.show()
 
# 2. 自适应阈值分割
adaptive_thresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
plt.imshow(adaptive_thresh, cmap='gray')
plt.title('Adaptive Thresholding')
plt.show()
 
# 3. Otsu's二值化
ret, otsu = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
plt.imshow(otsu, cmap='gray')
plt.title('Otsu\'s Binarization')
plt.show()
 
# 4. 分水岭算法
D = cv2.distanceTransform(img, cv2.DIST_L2, 5)
localMax = cv2.dilate(D, None, iterations=2)
markers = cv2.watershed(cv2.cvtColor(img, cv2.COLOR_GRAY2BGR), localMax)
markers = cv2.cvtColor(markers, cv2.COLOR_BGR2RGB)
plt.imshow(markers)
plt.title('Watershed Segmentation')
plt.show()
 
# 5. GrabCut算法
mask = np.zeros(img.shape[:2], np.uint8)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (50, 50, 450, 290)
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
img = img * mask2[:, :, np.newaxis]
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.title('GrabCut')
plt.show()
 
# 6. SLIC超像素分割
segments_slic = slic(img, n_segments=200, compactness=10, sigma=1)
plt.imshow(segments_slic)
plt.title('SLIC Superpixels')
plt.show()
 
# 7. 基于深度学习的分割方法（示例代码简化，实际应用需安装并配置相关深度学习框架）
# model = tf.keras.models.load_model('your_model.h5')
# predictions = model.predict(img[np.newaxis, :, :, np.newaxis])  # 注意输入形状可能需要调整
# plt.imshow(predictions[0, :, :, 0], cmap='gray')  # 假设输出是单通道图像
# plt.title('Deep Learning Segmentation')
# plt.show()