为何使用cv2与scipy.misc读取同一张图片得到的形状不同？

Great question! Let's break down exactly why you're seeing this shape mismatch, and how you can get consistent results if you need them:

1. Default Color Channel Behavior is the Culprit #

The core issue boils down to how each library handles color channels by default:

• cv2.imread: OpenCV's image reader defaults to loading images in the BGR color space (3 channels), even if your image appears grayscale. That's why you get the 3D shape (1010, 250, 3)—the third dimension represents the blue, green, and red channels.
• scipy.misc.imread: This function (which is deprecated in newer SciPy versions, more on that later) defaults to converting images to grayscale (single channel) unless you explicitly tell it otherwise. Hence the 2D shape (1010, 250) with no channel dimension.

2. How to Make Their Outputs Match #

If you want both libraries to return the same shape, just tweak their parameters:

• To get a grayscale image from OpenCV (matching scipy.misc.imread's default), use the cv2.IMREAD_GRAYSCALE flag:

  img_cv2_gray = cv2.imread("your_image_path.jpg", cv2.IMREAD_GRAYSCALE)
  # Shape will be (1010, 250)
  

• To get a 3-channel color image from scipy.misc.imread (matching cv2.imread's default), specify the mode='RGB' parameter:

  img_scipy_color = scipy.misc.imread("your_image_path.jpg", mode='RGB')
  # Shape will be (1010, 250, 3)
  

3. A Quick Heads-Up on Deprecation #

Just a note: scipy.misc.imread has been deprecated since SciPy 1.0.0. It's no longer maintained, so for future projects, consider using imageio.imread or PIL.Image.open instead—these tools have more predictable channel handling and active support.

内容的提问来源于stack exchange，提问作者Chen Dingyan