def watershed_demo(image):
blurred = cv.pyrMeanShiftFiltering(image, 10, 100)
gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
cv.imshow("binary", binary)
kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
mb = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel, iterations=2)
sure_bg = cv.dilate(binary, kernel, iterations=3)
cv.imshow("mor", sure_bg)
dist = cv.distanceTransform(mb, cv.DIST_L2, 3)
dist_output = cv.normalize(dist, 0, 1.0, cv.NORM_MINMAX)
cv.imshow("dist", dist_output * 50)
ret, surface = cv.threshold(dist, dist.max() * 0.6, 255, cv.THRESH_BINARY)
cv.imshow("interface", surface)
surface_fg = np.uint8(surface)
unknow = cv.subtract(sure_bg, surface_fg)
ret, markers = cv.connectedComponents(surface_fg)
print(ret)
markers += 1
markers[unknow == 255] = 0
markers = cv.watershed(image, markers=markers)
image[markers == -1] = [0, 0, 255]
cv.imshow("result", image)
def watershed(image, image_color):
print('watershed', image.shape)
cv2.imshow('Image', image)
gradiente = segmentar_iterativo(image)
cv2.imshow('Gradiente', gradiente)
gradiente_inverso = image_not(gradiente)
cv2.imshow('Gradiente inverso', gradiente_inverso)
kernel = np.ones((3, 3), np.uint8)
thresh = gradiente_inverso
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=3)
gradiente_erode = cv2.erode(opening, kernel, iterations=13)
cv2.imshow('Gradiente erode', gradiente_erode)
gradiente_erode = np.uint8(gradiente_erode)
unknown = cv2.subtract(gradiente_inverso, gradiente_erode)
cv2.imshow('gradiente_inverso - gradiente_erode', unknown)
ret, markers = cv2.connectedComponents(gradiente_erode)
# gradiente_inverso é a imagem dos limites da barragem
markers = markers + 1
markers[unknown == 255] = 0
# markers[markers >= 1] = 250
cv2.imshow('markers', markers)
markers = cv2.watershed(image_color, markers)
image_color[markers == -1] = [255, 255, 0]
cv2.imshow('Resultado - Watershed', image_color)
cv2.waitKey(0)
def s2(img): #segmentacionWarershed
img = img
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
# Eliminación del ruido
kernel = np.ones((3, 3), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
# Encuentra el área del fondo
sure_bg = cv2.dilate(opening, kernel, iterations=3)
# Encuentra el área del primer
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(),
255, 0)
# Encuentra la región desconocida (bordes)
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
# Etiquetado
ret, markers = cv2.connectedComponents(sure_fg)
# Adiciona 1 a todas las etiquetas para asegurra que el fondo sea 1 en lugar de cero
markers = markers + 1
# Ahora se marca la región desconocida con ceros
markers[unknown == 255] = 0
markers = cv2.watershed(img, markers)
img[markers == -1] = [255, 0, 0]
return img
def watershed(self, _img=None):
# # 灰度和二值转换
_img = self.img if _img is None else _img
_gray = cv2.cvtColor(_img, cv2.COLOR_BGR2GRAY)
_, _binary = cv2.threshold(_gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
# # 形态学操作
# # # 形态学操作卷积核
_kernel = np.ones((3, 3), np.uint8)
# # # 开运算去噪(去掉椒盐噪声的影响)
_opening = cv2.morphologyEx(_binary, cv2.MORPH_OPEN, _kernel, iterations=2)
# # # 如果能画出背景和前景, 分割算法会很好
# # # 考虑到数据量的原因, 使用程序 机械的找出
# # # 找出一定是背景的部分 膨胀操作: 扩大图形区的面积
_sure_bg = cv2.dilate(_opening, _kernel, iterations=3)
# cv_show(_sure_bg)
# # 距离变换函数: 对原始图像进行计算 之后二值处理, 获取前景
# # 该函数的第一个参数只能是单通道的二值的图像, 第二个参数是距离方法
# # 计算图像上255点与最近的0点之间的距离 DIST_L2应是欧氏距离, 会输出小数
# # DIST_L1应是哈密顿距离, 不会有小数
_dist_transform = cv2.distanceTransform(_opening, cv2.DIST_L1, 5)
# cv_show(_dist_transform)
# # 距离变换之后做一二值变换, 得到大概率是图像前景的点
_, _sure_fg = cv2.threshold(_dist_transform, 0.5 * _dist_transform.max(), 255, cv2.THRESH_BINARY)
# # 转换类型, 否则会很危险
_sure_fg = np.uint8(_sure_fg)
# cv_show(_sure_fg)
# # 绘制unknown区 交给算法, 自下而上的洪泛算法
_unknown = cv2.subtract(_sure_bg, _sure_fg)
# cv_show(_unknown)
_, _markers = cv2.connectedComponents(_sure_fg)
_markers = _markers + 1
_markers[_unknown == 255] = 0
_img1 = _img.copy()
_markers = cv2.watershed(_img1, _markers)
# # 圈出来 之后可以根据结果将一部分的值变为黑色
def random_color(a: int):
return np.random.randint(0, 255, (a, 3))
_markers_label = np.unique(_markers)
_colors = random_color(_markers_label.size)
for _mark, _color in zip(_markers_label, _colors):
_img1[_markers == _mark] = _color
# # 展示
cv_show(_img1)
def watershed(image, image_color):
gradiente = segmentar_iterativo(image)
gradiente_inverso = image_not(gradiente)
kernel = np.ones((3, 3), np.uint8)
thresh = gradiente_inverso
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=3)
gradiente_erode = cv2.erode(opening, kernel, iterations=13)
gradiente_erode = np.uint8(gradiente_erode)
unknown = cv2.subtract(gradiente_inverso, gradiente_erode)
ret, markers = cv2.connectedComponents(gradiente_erode)
markers = markers + 1
markers[unknown == 255] = 0
markers = cv2.watershed(image_color, markers)
image_color[markers == -1] = [255, 255, 0]
return image_color
def watershedAlgorithm(image):
img = cv.imread(image)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
ret, thresh = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV + cv.THRESH_OTSU)
# noise removal
kernel = np.ones((5, 5), np.uint8)
opening = cv.morphologyEx(thresh, cv.MORPH_OPEN, kernel, iterations=2)
# sure background area
sure_bg = cv.dilate(opening, kernel, iterations=3)
# Finding sure foreground area
dist_transform = cv.distanceTransform(opening, cv.DIST_L2, 5)
ret, sure_fg = cv.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv.subtract(sure_bg, sure_fg)
# Marker labelling
ret, markers = cv.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers + 1
# Now, mark the region of unknown with zero
markers[unknown == 255] = 0
markers = cv.watershed(img, markers)
img[markers == -1] = [255, 0, 0]
return img
def watershed(img, img_gray):
# mean = np.average(img_gray)
# _, thresh1 = cv2.threshold(img_gray,mean,255,cv2.THRESH_BINARY_INV)
# _, thresh2 = cv2.threshold(img_gray,200,255,cv2.THRESH_BINARY)
# thresh = np.bitwise_or(thresh1, thresh2)
_, thresh = cv2.threshold(img_gray,np.average(img_gray)-40,255,cv2.THRESH_BINARY_INV)
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel,iterations=2)
sure_bg = cv2.dilate(opening,kernel,iterations=2)
dist_transform = cv2.distanceTransform(sure_bg,cv2.DIST_L2,5)
_, sure_fg = cv2.threshold(dist_transform,0.5*dist_transform.max(),255,0)
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_fg, sure_bg)
ret, markers = cv2.connectedComponents(unknown)
markers = markers + 1
markers[unknown == 255] = 0
markers = cv2.watershed(img,markers)
return dist_transform
dist_transform = cv2.distanceTransform(sure_bg, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.5 * dist_transform.max(), 255,
0)
sure_fg = np.uint8(sure_fg)
# Background에서 Foregrand를 제외한 영역을 Unknow영역으로 파악
unknown = cv2.subtract(sure_bg, sure_fg)
# unknown = sure_bg
# FG에 Labelling작업
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers + 1
markers[unknown == 255] = 0
# watershed를 적용하고 경계 영역에 색지정
markers = cv2.watershed(img, markers)
img[markers == -1] = [255, 0, 0]
images = [
gray, thresh, sure_bg, dist_transform, sure_fg, unknown, markers, img
]
titles = [
'Gray', 'Binary', 'Sure BG', 'Distance', 'Sure FG', 'Unknow', 'Markers',
'Result'
]
for i in range(len(images)):
plt.subplot(2, 4, i + 1), plt.imshow(images[i]), plt.title(
titles[i]), plt.xticks([]), plt.yticks([])
plt.show()