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add test opencv with tkinter

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songtianlun 2024-07-02 11:53:23 +08:00
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commit 54274c77ac
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176
study_python/tkinter-opencv/main.py Normal file
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import cv2
import numpy as np
import os
from tkinter import Tk
from tkinter.filedialog import askopenfilename
# 创建一个 Tkinter 根窗口
root = Tk()
root.withdraw()
# 打开文件对话框，选择图片文件
filename = askopenfilename(title='选择图片', filetypes=[('Image Files', ('*.jpg', '*.jpeg', '*.png', '*.bmp'))])
# 检查是否选择了文件
if filename:
# 读取选中的图片文件
image = cv2.imread(filename)
# 定义全局变量
points = []
completed = False
extracted_image = None
# 定义鼠标回调函数
def mouse_callback(event, x, y, flags, param):
global points, completed, extracted_image
if event == cv2.EVENT_LBUTTONDOWN:
# 存储点击的坐标
points.append((x, y))
# 绘制已选择的点
cv2.circle(image, (x, y), 5, (0, 0, 255), -1)
# 绘制连接的线段
if len(points) > 1:
cv2.line(image, points[-2], points[-1], (0, 0, 255), 5)
# 绘制最后一个点与第一个点的连线
if len(points) == 4:
cv2.line(image, points[-1], points[0], (0, 0, 255), 5)
completed = True
# 创建窗口并注册鼠标回调函数
cv2.namedWindow('image')
cv2.setMouseCallback('image', mouse_callback)
while True:
# 显示图像
cv2.imshow('image', image)
# 按下 'Esc' 键退出循环
if cv2.waitKey(1) == 27:
break
# 检查是否成功获取四边形
if completed:
# 创建与原图像大小相同的掩膜图像
mask = np.zeros_like(image[:, :, 0])
# 使用填充多边形函数将四边形区域设置为白色
cv2.fillPoly(mask, [np.array(points)], 255)
# 将掩膜应用到原图像上，提取方框内的内容
extracted_image = cv2.bitwise_and(image, image, mask=mask)
# 将方框外的像素值设置为0
extracted_image[np.where(extracted_image == 0)] = 0
# 显示提取的图像
cv2.imshow('extracted_image', extracted_image)
# 获取文件名和扩展名
file_dir = os.path.dirname(filename)
file_name = os.path.basename(filename)
file_name, file_ext = os.path.splitext(file_name)
# 构建新的文件名
extracted_image_file_name = os.path.join(file_dir, file_name + "_boundary" + file_ext)
# 保存提取后的图像
cv2.imwrite(extracted_image_file_name, extracted_image)
# 转换提取后的图像到 LAB 色彩空间
lab_image = cv2.cvtColor(extracted_image, cv2.COLOR_BGR2LAB)
segmented_image = []
combined_image = []
# 定义鼠标事件回调函数
def mouse_callback1(event, x, y, flags, param):
global segmented_image, combined_image
if event == cv2.EVENT_LBUTTONDOWN:
if combined_image[y, x, 0] == 0 and combined_image[y, x, 1] == 0 and combined_image[y, x, 2] == 0:
# 创建临时变量用于处理连通域
segmented_image[y-8:y+8, x-8:x+8] = 255
colored_segmented_image = cv2.cvtColor(segmented_image, cv2.COLOR_GRAY2BGR)
combined_image = cv2.bitwise_and(extracted_image, colored_segmented_image)
cv2.imshow('segmented_image', combined_image)
leaf_image_file_name = os.path.join(file_dir, file_name + "_leaf" + file_ext)
cv2.imwrite(leaf_image_file_name, combined_image)
nonzero_pixels_b = np.count_nonzero(extracted_image[:, :, 0])
nonzero_pixels_leaf = np.count_nonzero(combined_image[:, :, 0])
result_file = os.path.join(file_dir, file_name + "_result.txt")
file = open(result_file, 'w')
file.truncate(0)
file.write('采样框像素点数为' + str(nonzero_pixels_b))
file.write('采样框内绿叶像素点数为' + str(nonzero_pixels_leaf))
file.close()
# 定义滑动条的回调函数
def on_trackbar(value):
global segmented_image
global combined_image
lower_th = cv2.getTrackbarPos('L', 'segmented_image')
upper_th = cv2.getTrackbarPos('A', 'segmented_image')
v_value = cv2.getTrackbarPos('B', 'segmented_image')
# 根据滑动条的值创建阈值范围
lower = np.array([lower_th, upper_th, v_value], dtype=np.uint8)
upper = np.array([255, 255, 255], dtype=np.uint8)
# 对 LAB 图像进行阈值分割
segmented_image = cv2.inRange(lab_image, lower, upper)
# 反转二值图像
segmented_image = cv2.bitwise_not(segmented_image)
# 将二值图像转换为彩色图像
colored_segmented_image = cv2.cvtColor(segmented_image, cv2.COLOR_GRAY2BGR)
# 进行位逻辑与操作，将彩色分割图像与提取图像进行合并
combined_image = cv2.bitwise_and(extracted_image, colored_segmented_image)
# 显示合并后的图像
cv2.imshow('segmented_image', combined_image)
leaf_image_file_name = os.path.join(file_dir, file_name + "_leaf" + file_ext)
cv2.imwrite(leaf_image_file_name, combined_image)
nonzero_pixels_b = np.count_nonzero(extracted_image[:, :, 0])
nonzero_pixels_leaf = np.count_nonzero(combined_image[:, :, 0])
result_file = os.path.join(file_dir, file_name + "_result.txt")
file = open(result_file, 'w')
file.truncate(0)
file.write('采样框像素点数为' + str(nonzero_pixels_b))
file.write('采样框内绿叶像素点数为' + str(nonzero_pixels_leaf))
file.close()
# 创建窗口用于显示分割结果
cv2.namedWindow('segmented_image')
cv2.setMouseCallback('segmented_image', mouse_callback1)
# 创建滑动条
cv2.createTrackbar('L', 'segmented_image', 0, 255, on_trackbar)
cv2.createTrackbar('A', 'segmented_image', 0, 255, on_trackbar)
cv2.createTrackbar('B', 'segmented_image', 0, 255, on_trackbar)
# 初始化滑动条的值
cv2.setTrackbarPos('L', 'segmented_image', 0)
cv2.setTrackbarPos('A', 'segmented_image', 115)
cv2.setTrackbarPos('B', 'segmented_image', 0)
while True:
# 按下 'Esc' 键退出循环
if cv2.waitKey(1) == 27:
break
# 重置标志和列表，以便选择下一个方框
completed = False
points.clear()
# 退出循环
break
# 释放窗口和销毁所有窗口
cv2.destroyAllWindows()
else:
print("未选择图像文件")

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study_python/tkinter-opencv/main.spec Normal file
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# -*- mode: python ; coding: utf-8 -*-
a = Analysis(
['main.py'],
pathex=[],
binaries=[],
datas=[],
hiddenimports=[],
hookspath=[],
hooksconfig={},
runtime_hooks=[],
excludes=[],
noarchive=False,
optimize=0,
)
pyz = PYZ(a.pure)
exe = EXE(
pyz,
a.scripts,
[],
exclude_binaries=True,
name='main',
debug=False,
bootloader_ignore_signals=False,
strip=False,
upx=True,
console=True,
disable_windowed_traceback=False,
argv_emulation=False,
target_arch=None,
codesign_identity=None,
entitlements_file=None,
)
coll = COLLECT(
exe,
a.binaries,
a.datas,
strip=False,
upx=True,
upx_exclude=[],
name='main',
)

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study_python/tkinter-opencv/requirements.txt Normal file
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numpy==2.0.0
opencv_python==4.10.0.84