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阈值函数（Threshold Function）是图像处理中的一个基本操作，常用于去噪、边缘检测等场景。通过对图像像素值进行比对，设置一个阈值，根据结果将像素值置零或设定为最大值，从而实现图像的二值化处理。
阈值函数的主要作用是对图像进行二值化处理。通过设置一个阈值，图像中的像素值如果高于或低于该阈值，会被替换为0或最大值（通常是255）。这种方法可以有效地去噪，提取图像中的边缘或特定区域。
retval, dst = cv.threshold(src, thresh, maxval, type[, dst])
src：原图像。dst：结果图像。thresh：当前阈值。maxval：最大阈值，通常为255。type：阈值类型，常见有以下几种：| 阈值类型 | 对应值 | 特殊说明 |
|---|---|---|
| THRESH_BINARY | 0 | 二值化，>thresh置255，<=thresh置0 |
| THRESH_BINARY_INV | 1 | 二值化，>thresh置0，<=thresh置255 |
| THRESH_TRUNC | 2 | 截断，大于thresh的值置maxval |
| THRESH_TOZERO | 3 | 到零，大于thresh的值保留原值，<=thresh置0 |
| THRESH_TOZERO_INV | 4 | 到零，>thresh置0，<=thresh保留原值 |
| THRESH_MASK | 7 | 二值掩膜，不支持32位图像 |
| THRESH_OTSU | 8 | 门限值优化，仅支持单通道图像 |
| THRESH_TRIANGLE | 16 | 三角形门限值优化，不支持32位图像 |
retval：与参数thresh一致。dst：结果图像。将图像中大于thresh的像素值设为255，小于等于thresh的设为0。
ret, dst = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY)
与THRESH_BINARY相反，大于thresh的值置0，小于等于thresh的值置255。
ret, dst = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY_INV)
将大于thresh的像素值截断至maxval，其余值保持不变。
ret, dst = cv2.threshold(img, 128, 255, cv2.THRESH_TRUNC)
将大于thresh的像素值保留原值，小于等于thresh的值置0。
ret, dst = cv2.threshold(img, 128, 255, cv2.THRESH_TOZERO)
与THRESH_TOZERO相反，>thresh置0，<=thresh保留原值。
ret, dst = cv2.threshold(img, 131, 255, cv2.THRESH_TOZERO_INV)
用于自动门限值选择，结合其他阈值类型使用，仅适用于单通道图像。
与OTSU类似，用于三角形门限值优化，仅支持单通道图像。
以下是一个使用OpenCV进行阈值处理的Python代码示例：
import numpy as npimport cv2# 创建一个6x6的BGR三通道图像img = np.zeros((6, 6, 3), dtype=np.uint8)# 生成随机像素值img[:, :, 0] = np.random.randint(0, 100, size=36).reshape(6, 6) # 蓝色通道img[:, :, 1] = np.random.randint(100, 200, size=36).reshape(6, 6) # 绿色色通道img[:, :, 2] = np.random.randint(200, 256, size=36).reshape(6, 6) # 红色通道# 使用THRESH_BINARY进行阈值处理ret, dst = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY)print("阈值处理后的图像：")print(dst) [[[ 0 255 255] [ 0 0 255] [ 0 255 255] [ 0 0 255] [ 0 255 255] [ 0 0 255]][[ 0 0 255] [ 0 255 255] [ 0 255 255] [ 0 255 255] [ 0 255 255] [ 0 0 255]][[ 0 255 255] [ 0 255 255] [ 0 0 255] [ 0 255 255] [ 0 255 255] [ 0 0 255]][[ 0 255 255] [ 0 255 255] [ 0 255 255] [ 0 255 255] [ 0 255 255] [ 0 255 255]][[ 0 0 255] [ 0 0 255] [ 0 255 255] [ 0 0 255] [ 0 255 255] [ 0 0 255]][[ 0 255 255] [ 0 255 255] [ 0 0 255] [ 0 0 255] [ 0 255 255] [ 0 255 255]]
以下是一个使用PyQt5编写的图像阈值处理 GUI 工具示例：
import cv2import numpy as npfrom PyQt5.QtWidgets import (QWidget, QPushButton, QHBoxLayout, QVBoxLayout, QLabel, QFileDialog, QHBoxLayout, QLCDNumber, QProgressBar)from PyQt5.QtCore import Qt, QTimerclass ThresholdDemo(QWidget): def __init__(self): super().__init__() self.setup_ui() self.setup_slots() def setup_ui(self): self.title = "阈值处理工具" self.setGeometry(100, 100, 800, 600) self.img_path = "" self.threshold = 1 self.max_val = 255 self.threshold_type = cv2.THRESH_BINARY self.extend_type = 0 self.check_gray = False # UI组件 self.main_layout = QVBoxLayout() self.top_layout = QHBoxLayout() self.param_layout = QVBoxLayout() self.button_layout = QVBoxLayout() # 参数设置 self.param_layout.addWidget(QLabel("阈值：")) self.param_layout.addWidget(QLCDNumber(self.threshold)) self.param_layout.addWidget(QLabel("最大值：")) self.param_layout.addWidget(QLCDNumber(self.max_val)) self.param_layout.addWidget(QLabel("阈值类型：")) self.param_layout.addWidget(QComboBox(['THRESH_BINARY', 'THRESH_BINARY_INV', 'THRESH_TRUNC', 'THRESH_TOZERO', 'THRESH_TOZERO_INV', 'THRESH_OTSU', 'THRESH_TRIANGLE'])) # 按钮 self.button_layout.addWidget(QPushButton("浏览图片")) self.button_layout.addWidget(QPushButton("更新阈值")) self.button_layout.addWidget(QPushButton("切换灰度")) self.button_layout.addWidget(QPushButton("查看帮助")) # 显示区域 self.image_display = QLabel() self.image_display.setFixedSize(640, 480) self.main_layout.addWidget(self.image_display) # 布局设置 self.top_layout.addLayout(self.param_layout) self.top_layout.addLayout(self.button_layout) self.setLayout(self.main_layout) def setup_slots(self): # 浏览图片按钮 self.btnBrowse.clicked.connect(self.slot_btn_browse) # 阈值参数变化 self.threshold LCD绑定self.slot_threshold_changed # 最大值参数变化 self.max_val LCD绑定self.slot_max_val_changed # 阈值类型变化 self.threshold_type_combobox绑定self.slot_threshold_type_changed # 灰度切换 self.btnCheckGray.toggled.connect(self.slot_check_gray) def slot_btn_browse(self): try: file, _ = QFileDialog.getOpenFileName(self, 'Open Image', 'D:\\tmp\\pics', '*.*') if file: self.imagePath.setText(file) img_init = cv2.imdecode(np.fromfile(file, dtype=np.uint8), -1) self.img_threshold = img_init.copy() cv2.cvtColor(img_init, cv2.COLOR_BGR2RGB, img_init) h, w, c = img_init.shape qImg = QImage(img_init, w, h, w * c, QImage.Format_RGB888) self.initPicFrame.setPixmap(QPixmap.fromImage(qImg)) self.update_threshold_image() except Exception as e: print(e) def update_threshold_image(self): try: img = self.img_threshold.copy() if self.check_gray: img_gray = np.zeros((img.shape[0], img.shape[1]), dtype=np.uint8) cv2.cvtColor(img, cv2.COLOR_BGR2GRAY, img_gray) ret, img_threshold = cv2.threshold(img_gray, self.threshold, self.max_val, self.threshold_type | self.extend_type) else: ret, img_threshold = cv2.threshold(img, self.threshold, self.max_val, self.threshold_type) print(f"thresh: {self.threshold}, maxval: {self.max_val}, flags: {self.threshold_type} | {self.extend_type}") h, w = img_threshold.shape qImg = QImage(img_threshold, w, h, w, QImage.Format_Indexed8) self.thresholdPicFrame.setPixmap(QPixmap.fromImage(qImg)) except Exception as e: print(e) def slot_threshold_changed(self, val): self.threshold = val self.threshold LCD显示更新 self.update_threshold_image() def slot_max_val_changed(self, val): self.max_val = val self.max_val LCD显示更新 self.update_threshold_image() def slot_threshold_type_changed(self, type): type_dict = { 'THRESH_BINARY': cv2.THRESH_BINARY, 'THRESH_BINARY_INV': cv2.THRESH_BINARY_INV, 'THRESH_TRUNC': cv2.THRESH_TRUNC, 'THRESH_TOZERO': cv2.THRESH_TOZERO, 'THRESH_TOZERO_INV': cv2.THRESH_TOZERO_INV, 'THRESH_OTSU': cv2.THRESH_OTSU, 'THRESH_TRIANGLE': cv2.THRESH_TRIANGLE } self.threshold_type = type_dict[type] self.update_threshold_image() def slot_check_gray(self, bGray): self.check_gray = bGray self.update_threshold_image()def thresholdDemo(): img = np.zeros((6, 6, 3), dtype=np.uint8) img[:, :, 0] = np.random.randint(0, 100, size=36).reshape(6, 6) img[:, :, 1] = np.random.randint(100, 200, size=36).reshape(6, 6) img[:, :, 2] = np.random.randint(200, 256, size=36).reshape(6, 6) cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) if __name__ == '__main__': qApp = QApplication(sys.argv) demo = ThresholdDemo() demo.show() qApp.exec_() 请访问开源代码下载页面获取最新版本。
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