OpenCV 是一個跨平臺的計算機視覺庫, 支持多語言, 功能強大. 今天小白就帶大家一起攜手走進 OpenCV 的世界.
分水嶺算法 (Watershed Algorithm) 是一種圖像區域分割算法. 在分割的過程中, 分水嶺算法會把跟臨近像素間的相似性作為重要的根據.
距離變換 (Distance Transform)通過計算圖像中非零像素點到最近像素的距離, 實現了像素與圖像區域的距離變換.
連通域 (Connected Components) 指的是圖像中具有相同像素且位置相鄰的前景像素點組成的圖像區域.
算法會根據 markers 傳入的輪廓作為種子, 對圖像上其他的像素點根據分水嶺算法規則進行判斷, 并對每個像素點的區域歸屬進行劃定. 區域之間的分界處的值被賦值為 -1.
import numpy as np
import cv2
from matplotlib import pyplot as plt
def watershed(image):
"""分水嶺算法"""
# 卷積核
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# 均值遷移濾波
blur = cv2.pyrMeanShiftFiltering(image, 10, 100)
# 轉換成灰度圖
image_gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
# 二值化
ret1, thresh1 = cv2.threshold(image_gray, 0, 255, cv2.THRESH_OTSU)
# 開運算
open = cv2.morphologyEx(thresh1, cv2.MORPH_OPEN, kernel, iterations=2)
# 膨脹
dilate = cv2.dilate(open, kernel, iterations=3)
# 距離變換
dist = cv2.distanceTransform(dilate, cv2.DIST_L2, 3)
dist = cv2.normalize(dist, 0, 1.0, cv2.NORM_MINMAX)
print(dist.max())
# 二值化
ret2, thresh2 = cv2.threshold(dist, dist.max() * 0.6, 255, cv2.THRESH_BINARY)
thresh2 = np.uint8(thresh2)
# 分水嶺計算
unknown = cv2.subtract(dilate, thresh2)
ret3, component = cv2.connectedComponents(thresh2)
print(ret3)
# 分水嶺計算
markers = component + 1
markers[unknown == 255] = 0
result = cv2.watershed(image, markers=markers)
image[result == -1] = [0, 0, 255]
# 圖片展示
image_show((image, blur, image_gray, thresh1, open, dilate), (dist, thresh2, unknown, component, markers, image))
return image
def image_show(graph1, graph2):
"""繪制圖片"""
# 圖像1
original, blur, gray, binary1, open, dilate = graph1
# 圖像2
dist, binary2, unknown, component, markers, result = graph2
f, ax = plt.subplots(3, 2, figsize=(12, 16))
# 繪制子圖
ax[0, 0].imshow(cv2.cvtColor(original, cv2.COLOR_BGR2RGB))
ax[0, 1].imshow(cv2.cvtColor(blur, cv2.COLOR_BGR2RGB))
ax[1, 0].imshow(gray, "gray")
ax[1, 1].imshow(binary1, "gray")
ax[2, 0].imshow(open, "gray")
ax[2, 1].imshow(dilate, "gray")
# 標題
ax[0, 0].set_title("original")
ax[0, 1].set_title("image blur")
ax[1, 0].set_title("image gray")
ax[1, 1].set_title("image binary1")
ax[2, 0].set_title("image open")
ax[2, 1].set_title("image dilate")
plt.show()
f, ax = plt.subplots(3, 2, figsize=(12, 16))
# 繪制子圖
ax[0, 0].imshow(dist, "gray")
ax[0, 1].imshow(binary2, "gray")
ax[1, 0].imshow(unknown, "gray")
ax[1, 1].imshow(component, "gray")
ax[2, 0].imshow(markers, "gray")
ax[2, 1].imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
# 標題
ax[0, 0].set_title("image distance")
ax[0, 1].set_title("image binary2")
ax[1, 0].set_title("image unknown")
ax[1, 1].set_title("image component")
ax[2, 0].set_title("image markers")
ax[2, 1].set_title("result")
plt.show()
if __name__ == "__main__":
# 讀取圖片
image = cv2.imread("coin.jpg")
# 分水嶺算法
result = watershed(image)
# 保存結果
cv2.imwrite("result.jpg", result)
到此這篇關于OpenCV半小時掌握基本操作之分水嶺算法的文章就介紹到這了,更多相關OpenCV分水嶺算法內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
