如何从skimage.filters.try_all_threshold自动筛选最优阈值处理结果？

Hey there! I get it—try_all_threshold dumps 7 solid thresholding options on you, but now you need to narrow it down to one for your workflow. Let’s break down both manual selection and automated optimal picking below.

1. Manually Pick a Single Threshold Result #

First off, know that try_all_threshold returns a dictionary where keys are algorithm names (like otsu, yen, li) and values are the binarized images. You can yank the exact result you want just by referencing its key.

Here’s a quick, actionable example:

from skimage import filters, io
import matplotlib.pyplot as plt

# Load your grayscale image (convert to grayscale if your input is colored)
image = io.imread("text_numbers.png", as_gray=True)

# Generate all threshold results (this also plots them for visual sanity checks)
fig, ax = plt.subplots(figsize=(10, 8))
threshold_results = filters.try_all_threshold(image, figsize=(10, 8), ax=ax)

# Pick one result manually—say, Otsu's method (super reliable for text/digits)
selected_binary_img = threshold_results["otsu"]

# Plug this image into your downstream processing (text detection, digit recognition, etc.)
# ...

The default algorithm keys you can use are: otsu, yen, li, isodata, mean, triangle, minimum—these are the 7 methods the function runs by default.

2. Dynamically Select the Optimal Threshold Result #

If you’re working with batches of images and can’t manually check each one, you’ll need an evaluation metric that fits your text/digit use case (since "optimal" depends on your goal—we care about clean separation of text from background here).

Example: Use Inter-Class Variance (Otsu’s Core Criterion) #

This metric measures how well the threshold splits foreground (text) and background. Higher variance means sharper separation.

import numpy as np

def compute_inter_class_variance(original_gray, binary_img):
    # Split pixels into foreground and background groups
    foreground_pixels = original_gray[binary_img == 1]
    background_pixels = original_gray[binary_img == 0]
    
    # Handle edge cases where all pixels are one class (avoids division by zero)
    if len(foreground_pixels) == 0 or len(background_pixels) == 0:
        return 0.0
    
    # Calculate weights, means, and inter-class variance
    weight_fg = len(foreground_pixels) / (len(foreground_pixels) + len(background_pixels))
    weight_bg = 1 - weight_fg
    mean_fg = np.mean(foreground_pixels)
    mean_bg = np.mean(background_pixels)
    
    inter_variance = weight_fg * weight_bg * (mean_fg - mean_bg) ** 2
    return inter_variance

# Evaluate all threshold results to find the best one
best_score = -1
best_binary_img = None
best_method = ""

for method_name, binary_img in threshold_results.items():
    current_score = compute_inter_class_variance(image, binary_img)
    if current_score > best_score:
        best_score = current_score
        best_binary_img = binary_img
        best_method = method_name

print(f"Best threshold method: {best_method} (inter-class variance: {best_score:.2f})")

# Use best_binary_img in your downstream workflow
# ...

Alternative Metrics for Text/Digits #

If inter-class variance doesn’t hit the mark for your specific images, try these:

• Connected Component Count: If you know roughly how many digits/text regions should be present, pick the result whose component count is closest to your expected number.
• Foreground Coverage: Measure the percentage of the image that’s foreground (text)—filter out results where coverage is too high (too much noise) or too low (missing text).
• Edge Preservation: Run edge detection (like Canny) on the binary image and check if text edges are intact.

内容的提问来源于stack exchange，提问作者Prabhat Mishra