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import traceback
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
def get_binary_img_(img):
gray_img = img
if len(img.shape) > 2:
gray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
binary_img = cv.Canny(gray_img, 80, 150)
return binary_img
def get_morp_dilate_(binary_img):
kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
# morp_dilate = cv.morphologyEx(binaryImg, cv.MORPH_DILATE, kernel=(1, 3), iterations=3)
# morp_dilate = cv.morphologyEx(morp_dilate, cv.MORPH_DILATE, kernel=(3, 1), iterations=3)
# morp_dilate = cv.morphologyEx(binaryImg, cv.MORPH_DILATE, kernel=(11, 11), iterations=3)
morp_dilate = cv.morphologyEx(binary_img, cv.MORPH_DILATE, kernel=kernel, iterations=3)
return morp_dilate
def get_water_img_(img, morp_dilate):
# 寻找图像轮廓 返回修改后的 图像的轮廓 以及它们的层次
# contours, hierarchy = cv.findContours(gray_img, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
# contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
# contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
contours, hierarchy = cv.findContours(morp_dilate, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
# 32位有符号整数类型，
marks = np.zeros(morp_dilate.shape[:2], np.int32)
# 绘制每一个轮廓
for index in range(len(contours)):
# 对marks进行标记，对不同区域的轮廓使用不同的亮度绘制，相当于设置注水点，有多少个轮廓，就有多少个轮廓
# 图像上不同线条的灰度值是不同的，底部略暗，越往上灰度越高
marks = cv.drawContours(marks, contours, index, (index, index, index), 1, 8, hierarchy)
# 使用分水岭算法
# 经过watershed函数的处理，不同区域间的值被置为-1（边界）没有标记清楚的区域被置为0，其他每个区域的值保持不变：1，2，...，contours.size()
marks_water = cv.watershed(img, marks)
return marks_water
def get_mask_img_(morp_dilate, file_dir):
contours, hierarchy = cv.findContours(morp_dilate, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
# 32位有符号整数类型，
marks = np.zeros(morp_dilate.shape[:2], np.int32)
for index in range(len(contours)):
dist = cv.pointPolygonTest(contours[index], (marks.shape[0] // 2, marks.shape[1] // 2), True)
if dist >= 0:
marks = cv.drawContours(marks, contours, contourIdx=index, color=1, thickness=1, lineType=8,
hierarchy=hierarchy)
edges = np.zeros((marks.shape[0] + 2, marks.shape[1] + 2), np.uint8) # 掩码，长短需要加2个像素
try:
cv.floodFill(marks, edges, (marks.shape[0] // 2, marks.shape[1] // 2), 1, cv.FLOODFILL_MASK_ONLY) # 漫水填充
except Exception as e:
if file_dir:
print(file_dir)
print(e)
print("=================")
print(traceback.format_exc())
# raise e
marks = np.ones(morp_dilate.shape[:2], np.int32)
return marks
def get_binary_img(binary_img, mask):
masked_binary_img = cv.bitwise_and(binary_img, binary_img, mask=mask.astype('uint8'))
return masked_binary_img
def get_water_img(img, morp_dilate, mask):
water_img = get_water_img_(img, morp_dilate)
masked_water = cv.bitwise_and(water_img, water_img, mask=mask.astype('uint8'))
return masked_water
def get_more_dim(img, file_dir, source_img=None):
if source_img is None:
source_img = img
# img: ndarray: 852, 847, 3
binary_img = get_binary_img_(img)
morp_dilate = get_morp_dilate_(binary_img)
mask = get_mask_img_(morp_dilate, file_dir)
masked_binary_img = get_binary_img(binary_img, mask)
masked_water = get_water_img(source_img, morp_dilate, mask)
# print(f"masked_binary_img shape:{masked_binary_img.shape} masked_water shape:{masked_water.shape}")
# print(f"type(masked_binary_img):{type(masked_binary_img)} type(masked_water):{type(masked_water)}")
# return np.stack((masked_binary_img, mask), axis=0)
return masked_binary_img, masked_water
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