图像分割中出现Too many indices error的问题排查求助

Hey, let's sort out this error step by step! The core issue here is a simple mix-up between single-channel grayscale data and 3-channel RGB data.

What's causing the error? #

• When you use rgb2gray(img), it converts your 3-channel RGB image (shape: (height, width, 3)) into a 2D single-channel grayscale array (shape: (height, width)). There's no third dimension here anymore!
• Your line img_gray[:,:,0] tries to access the 0th index of a third dimension that doesn't exist in the grayscale array. That's exactly why you get the "too many indices for array" error.
• On top of that, your goal is to segment the image using RGB channel thresholds—converting to grayscale is totally unnecessary here! You should work directly with the original RGB image.

Fixed Code #

Here's the corrected version that targets the RGB channels properly, plus an important note about OpenCV's color format:

import cv2
import numpy as np
import matplotlib.pyplot as plt
from skimage.color import rgb2gray  # Assuming you're using scikit-image's rgb2gray

img = cv2.imread('sample.jpg')
img = np.asarray(img)
img = img[:,:,0:3]
plt.imshow(img)
plt.show()

# Critical note: cv2.imread loads images in BGR order, not RGB!
# If your threshold values are for RGB channels, convert first:
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Now apply your RGB channel thresholds directly on the 3-channel image
img_select = np.where(
    (img_rgb[:,:,0] < 150) &  # Check red channel
    (img_rgb[:,:,1] > 70) &   # Check green channel
    (img_rgb[:,:,2] < 90),    # Check blue channel
    1, 
    0
)

plt.imshow(img_select, cmap='gray')
plt.show()

Key Notes #

1. OpenCV Color Order: Always remember cv2.imread() returns images in BGR format, but matplotlib.pyplot.imshow() expects RGB. If you skip converting to RGB, your channel thresholds will apply to the wrong color channels (e.g., you'll be checking blue instead of red), leading to incorrect segmentation.
2. Skip Grayscale Conversion: Since you need to use RGB channel data for thresholding, the entire grayscale conversion block (img_gray = rgb2gray(img) and subsequent lines) can be removed—it's irrelevant to your goal.

内容的提问来源于stack exchange，提问作者madmantel