解释 src_h, src_w = image_src.shape[:2] print(src_h, src_w) dst_h, dst_w = dst_size # 判断应该按哪个边做等比缩放 h = dst_w * (float(src_h) / src_w) # 按照ｗ做等比缩放 w = dst_h * (float(src_w) / src_h) #

这段代码是一个图片等比缩放的函数，输入参数为原图片和目标尺寸。首先，通过 `image_src.shape[:2]` 获取原图片的高和宽。然后，通过 `dst_size` 获取目标的高和宽。接下来，通过比较原图和目标图的高宽比，判断应该按哪个边做等比缩放。如果原图的宽高比更小，则按照目标宽度进行等比缩放，计算出新的高度 `h`；否则按照目标高度进行等比缩放，计算出新的宽度 `w`。最后返回新的尺寸 `(h, w)`。

相关问题

能给一个完整的实例吗，比方说以下python代码：import cv2 import numpy as np # 加载图像 image = cv2.imread("/root/camera/test/v4l2_cap.jpg") # 查看图像中是否存在蓝色和红色 blue_pixels = np.sum(image[:, :, 0]) # 蓝色通道 red_pixels = np.sum(image[:, :, 2]) # 红色通道 colors = "0" if blue_pixels > red_pixels: color = "Blue" elif blue_pixels < red_pixels: color = "Red" else: color = "None" # 将图像转换为灰度图像 gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 边缘增强 enhanced_image = cv2.Canny(gray_image, 33, 45) # 形态学操作（腐蚀和膨胀） kernel = np.ones((3, 3), np.uint8) edges1 = cv2.dilate(enhanced_image, kernel, iterations=3) # 在灰度图像中检测圆形 circles = cv2.HoughCircles(edges1, cv2.HOUGH_GRADIENT, dp=1, minDist=100, param1=66, param2=25, minRadius=90, maxRadius=185) shape="" if circles is not None: # 在原始图像上绘制检测到的圆 circles = np.uint16(np.around(circles)) for circle in circles[0, :]: x, y, radius = circle[0], circle[1], circle[2] if abs(x - image.shape[1] // 2) > 100: continue shape = "Circle" cv2.circle(image, (x, y), 90, (0, 255, 0), 2) cv2.circle(image, (x, y), 2, (0, 0, 255), 3) else: shape = "None" # 判断是否同时出现 Rectangle 和 Triangle以及颜色是否有红，绿 if color == "Red" and shape != "Circle" : result = 'r' elif color == "Blue" and shape == "Circle" : result = 'b' else: result = 'n' # 打印检测到的形状、颜色 #print(f"Color:{color}") #print(f"shape:{shape}") print(f"Result: {result}") #cv2.imshow("enhanced_image", enhanced_image) #cv2.imshow("edges1", edges1) #cv2.imshow("Image", image) #cv2.waitKey(0) #cv2.destroyAllWindows()

下面是将你提供的Python代码转换为Cython的示例：

import cv2
import numpy as np

cdef extern from "opencv2/core/core_c.h":
    void* cvLoadImage(const char* filename, int flags)

cdef extern from "opencv2/highgui/highgui_c.h":
    void cvShowImage(const char* name, void* image)
    void cvWaitKey(int delay)
    void cvDestroyAllWindows()

cdef extern from "opencv2/imgproc/imgproc_c.h":
    void cvCvtColor(void* src, void* dst, int code)
    void cvCanny(void* image, void* edges, double threshold1, double threshold2)
    void cvDilate(void* src, void* dst, void* kernel, int iterations)
    void cvHoughCircles(void* image, void* circles, int method, double dp, double minDist,
                        double param1, double param2, int minRadius, int maxRadius)
    void cvCircle(void* img, void* center, int radius, void* color, int thickness)

def detect_shape_and_color():
    cdef np.ndarray[np.uint8_t, ndim=3] image = np.zeros((480, 640, 3), dtype=np.uint8)
    cdef np.ndarray[np.uint8_t, ndim=2] gray_image = np.zeros((480, 640), dtype=np.uint8)
    cdef np.ndarray[np.uint8_t, ndim=2] enhanced_image = np.zeros((480, 640), dtype=np.uint8)
    cdef np.ndarray[np.uint8_t, ndim=2] edges = np.zeros((480, 640), dtype=np.uint8)
    cdef np.ndarray[np.float32_t, ndim=3] circles = np.zeros((1, 1, 3), dtype=np.float32)
    cdef np.ndarray[np.uint8_t, ndim=3] color = np.zeros((1, 1, 3), dtype=np.uint8)
    
    # 加载图像
    image = cv2.imread(b"/root/camera/test/v4l2_cap.jpg", -1)

    # 查看图像中是否存在蓝色和红色
    blue_pixels = np.sum(image[:, :, 0])  # 蓝色通道
    red_pixels = np.sum(image[:, :, 2])  # 红色通道

    colors = "0"
    if blue_pixels > red_pixels:
        color = "Blue"
    elif blue_pixels < red_pixels:
        color = "Red"
    else:
        color = "None"

    # 将图像转换为灰度图像
    cvCvtColor(image, gray_image, cv2.COLOR_BGR2GRAY)

    # 边缘增强
    cvCanny(gray_image, edges, 33, 45)

    # 形态学操作（腐蚀和膨胀）
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    cvDilate(edges, enhanced_image, kernel, 3)

    # 在灰度图像中检测圆形
    cvHoughCircles(enhanced_image, circles, cv2.HOUGH_GRADIENT, 1, 100, 66, 25, 90, 185)
    shape = ""
    if circles is not None:
        # 在原始图像上绘制检测到的圆
        circles = np.uint16(np.around(circles))
        for circle in circles[0, :]:
            x, y, radius = circle[0], circle[1], circle[2]
            if abs(x - image.shape[1] // 2) > 100:
                continue
            shape = "Circle"
            cvCircle(image, (x, y), 90, (0, 255, 0), 2)
            cvCircle(image, (x, y), 2, (0, 0, 255), 3)
    else:
        shape = "None"

    # 判断是否同时出现 Rectangle 和 Triangle以及颜色是否有红，绿
    if color == "Red" and shape != "Circle":
        result = 'r'
    elif color == "Blue" and shape == "Circle":
        result = 'b'
    else:
        result = 'n'

    # 打印检测到的形状、颜色
    print(f"Result: {result}")

    #cv2.imshow("enhanced_image", enhanced_image)
    #cv2.imshow("edges", edges)
    #cv2.imshow("Image", image)
    #cv2.waitKey(0)
    #cv2.destroyAllWindows()

if __name__ == "__main__":
    detect_shape_and_color()

请注意，上述代码中使用了Cython的类型注释来声明变量类型，以提高性能。还添加了Cython所需的C函数声明和转换函数。

在使用Cython之前，确保已按照之前提到的步骤安装了Cython并正确配置了环境。然后，将上述代码保存到一个.pyx文件中，并创建一个适当的setup.py文件来构建和编译Cython代码。

希望这个示例能够帮助你了解如何使用Cython将Python代码转换为C代码。如果有任何问题，请随时提问！

使用 cv2.resize 变换尺寸

使用cv2.resize函数可以实现图像的尺寸变换。该函数的语法为：
dst = cv2.resize(src, dsize=(new_width, new_height), fx, fy, interpolation)
其中，src表示原始图像，dsize表示目标图像的尺寸，可以直接输入新的宽度和高度，也可以通过设置缩放因子fx和fy实现倍数的放大和缩小。interpolation表示变换的方法，有多种选项可供选择，如最近邻插值法(CV_INTER_NN)、双线性插值法(CV_INTER_LINEAR)和双三次插值法(CV_INTER_CUBIC)等。

下面是一个使用cv2.resize函数的示例代码：
def resize_demo(image):
print("Origin size:", image.shape)
# 第一种方法：通过fx，fy缩放因子
res = cv.resize(image, None, fx=1, fy=3, interpolation=cv.INTER_CUBIC) # fx宽 fy高
print("After resize 1 size:", res.shape)
# 第二种方法：直接设置输出图像的尺寸，所以不用设置缩放因子
height,width = image.shape[:2]
res=cv.resize(image,(2*width,2*height),interpolation=cv.INTER_CUBIC)
print("After resize 2 size:", res.shape)
while(1):
cv.imshow('res',res)
cv.imshow('img',image)
if cv.waitKey(1) & 0xFF == 27:
break

以上代码中，首先打印出原始图像的尺寸，然后使用两种不同的方法进行图像的尺寸变换，分别输出变换后的图像尺寸。最后通过cv.imshow函数显示变换后的图像，并通过按键输入来退出显示窗口。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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