PiscTrace的开发者版

基于 PiscTrace 架构的视图处理的纯开发板，支持静态图片、实时视频流、摄像头视频流和网络视频流的处理。与 PiscTrace 应用版相比，开发者版通过直接的代码开发，提供了更高的灵活性和可定制性，适用于需要深度定制和复杂处理的应用场景。

1. 边缘检测 (Canny Edge Detection)

import cv2
class EdgeDetection:
    def __init__(self):
        pass
    def do(self, frame, device):
        # 将图像转换为灰度图
        gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # 应用 Canny 边缘检测
        edges = cv2.Canny(gray_image, 100, 200)
        return edges

2. 图像模糊 (Gaussian Blur)

图像模糊常用于去噪或柔化图像。高斯模糊是常见的一种模糊处理方法，可以通过 cv2.GaussianBlur 实现。

import cv2

class BlurEffect:
    def __init__(self):
        pass

    def do(self, frame, device):
        # 使用高斯模糊
        blurred_image = cv2.GaussianBlur(frame, (15, 15), 0)
        
        return blurred_image

3.轮廓检测 (Contours Detection)

import cv2
import numpy as np

class ContourDetection:
    def __init__(self):
        pass

    def do(self, frame, device):
        # 转为灰度图像
        gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # 应用 Canny 边缘检测
        edges = cv2.Canny(gray_image, 100, 200)
        
        # 查找轮廓
        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        # 绘制轮廓
        cv2.drawContours(frame, contours, -1, (0, 255, 0), 2)
        
        return frame

4. 直方图均衡 (Histogram Equalization)

import cv2

class HistogramEqualization:
    def __init__(self):
        pass

    def do(self, frame, device):
        # 将图像转换为灰度图
        gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # 直方图均衡化
        equalized_image = cv2.equalizeHist(gray_image)
        
        return equalized_image

5. 图像腐蚀和膨胀 (Erosion and Dilation)

import cv2
import numpy as np

class ErosionAndDilation:
    def __init__(self):
        pass

    def do(self, frame, device):
        # 创建一个内核
        kernel = np.ones((5, 5), np.uint8)
        
        # 腐蚀操作
        eroded_image = cv2.erode(frame, kernel, iterations=1)
        
        # 膨胀操作
        dilated_image = cv2.dilate(frame, kernel, iterations=1)
        
        return dilated_image

6. 图像梯度 (Image Gradient)

import cv2
import numpy as np

class ImageGradient:
    def __init__(self):
        pass

    def do(self, frame, device):
        # 转为灰度图像
        gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        
        # 计算 Sobel 梯度
        grad_x = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=3)
        grad_y = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=3)
        
        # 计算梯度的幅度
        gradient_magnitude = cv2.magnitude(grad_x, grad_y)
        
        # 转换为可显示的类型
        gradient_magnitude = np.uint8(np.absolute(gradient_magnitude))
        
        return gradient_magnitude