else: img = cv.cvtColor(img, cv.COLOR_BGR2RGB) plt.imshow(img)

这段代码是用来显示彩色图像的。其中，如果图像不是二维的，即为彩色图像，则会执行下面的代码。cv.cvtColor(img, cv.COLOR_BGR2RGB) 表示将图像从BGR格式转换为RGB格式，因为OpenCV中读取的彩色图像默认为BGR格式，而Matplotlib中显示图像需要RGB格式。然后，使用plt.imshow(img) 显示图像。

相关问题

def myplot(images,titles): # For plotting multiple images at once fig, axs=plt.subplots(1,len(images),sharey=True) fig.set_figwidth(15) for img,ax,title in zip(images,axs,titles): if img.shape[-1]==3: img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # OpenCV reads images as BGR, so converting back them to RGB else: img=cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) ax.imshow(img) ax.set_title(title)

这是一个 Python 函数，用于同时绘制多个图像。函数名为 myplot，输入参数为 images 和 titles，分别表示要绘制的图片和对应的标题。函数中使用了 matplotlib 库进行绘图，通过创建 subplots 实现将多个图片放在一起显示。函数中还包含了一些对图像的处理，如将灰度图像转换为 BGR 彩色图像以便显示等。

解释如下代码：def draw_matches(img1, kp1, img2, kp2, matches, color=None): """Draws lines between matching keypoints of two images. Keypoints not in a matching pair are not drawn. Args: img1: An openCV image ndarray in a grayscale or color format. kp1: A list of cv2.KeyPoint objects for img1. img2: An openCV image ndarray of the same format and with the same element type as img1. kp2: A list of cv2.KeyPoint objects for img2. matches: A list of DMatch objects whose trainIdx attribute refers to img1 keypoints and whose queryIdx attribute refers to img2 keypoints. """ # We're drawing them side by side. Get dimensions accordingly. # Handle both color and grayscale images. if len(img1.shape) == 3: new_shape = (max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], img1.shape[2]) elif len(img1.shape) == 2: new_shape = (max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1]) new_img = np.zeros(new_shape, type(img1.flat[0])) # Place images onto the new image. new_img[0:img1.shape[0],0:img1.shape[1]] = img1 new_img[0:img2.shape[0],img1.shape[1]:img1.shape[1]+img2.shape[1]] = img2 # Draw lines between matches. Make sure to offset kp coords in second image appropriately. r = 2 thickness = 1 print(len(kp1),len(kp2), len(matches) ) if color: c = color for m in matches[0:20]: # Generate random color for RGB/BGR and grayscale images as needed. if not color: c = np.random.randint(0,256,3) if len(img1.shape) == 3 else np.random.randint(0,256) # So the keypoint locs are stored as a tuple of floats. cv2.line(), like most other things, # wants locs as a tuple of ints. c = [255,255,255] end1 = tuple(np.round(kp1[m.queryIdx].pt).astype(int)) end2 = tuple(np.round(kp2[m.trainIdx].pt).astype(int) + np.array([img1.shape[1], 0])) cv2.line(new_img, end1, end2, c, thickness) cv2.circle(new_img, end1, r, c, thickness) cv2.circle(new_img, end2, r, c, thickness) plt.figure(figsize=(15,15)) plt.imshow(new_img) plt.show()

这是一个 Python 函数，用于将两张图片中的匹配关键点连接起来，并显示出来。函数的输入参数包括：img1，kp1，img2，kp2，matches，以及连接线的颜色(color)。

函数首先根据输入的两张图片的大小创建一个新的图像，用于在其中绘制匹配关键点。然后将输入的两张图片分别放置在新图像的左侧和右侧。

接下来，函数根据 matches 中的关键点信息，在新图像中绘制连接线。如果 color 参数不为空，则使用指定的颜色，否则使用随机颜色。对于每个匹配，函数将在两个关键点处绘制圆圈，并使用 cv2.line 函数画出连接线。

最后，函数展示绘制好的新图像。可以看出，这个函数的作用是可视化两张图片中的匹配关键点。