图像文本检测Python代码求助：已完成预处理，需校正径向畸变

Got it, let's add the radial distortion correction step to your existing image preprocessing workflow. Since you're already using PIL for initial tweaks, we can integrate OpenCV here—it has robust, easy-to-use tools for fixing radial distortion. Here's how to make it work:

Step 1: Install Dependencies #

First, make sure you have OpenCV installed (it's not part of the standard library):

pip install opencv-python

Step 2: Add Distortion Correction Code #

You can append this right after your existing PIL preprocessing code. It picks up the temp_temp1.png you saved and applies the correction:

import cv2
import numpy as np

# Load your preprocessed image
img = cv2.imread("temp_temp1.png", cv2.IMREAD_GRAYSCALE)

# --- Configure Distortion Parameters ---
# Note: If you have a calibrated camera, replace these with your actual values!
# These are sample parameters to get you started—adjust based on your image's distortion.
# Camera matrix (focal length + image center)
camera_matrix = np.array([[1200, 0, img.shape[1]/2],
                          [0, 1200, img.shape[0]/2],
                          [0, 0, 1]], dtype=np.float32)
# Distortion coefficients (k1, k2, p1, p2, k3)
# k1 controls radial distortion: positive = barrel distortion, negative = pincushion
distortion_coeffs = np.array([[-0.25, 0.08, 0, 0, 0]], dtype=np.float32)

# Calculate optimal camera matrix to avoid black borders after correction
new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(
    camera_matrix, distortion_coeffs, (img.shape[1], img.shape[0]), 1, (img.shape[1], img.shape[0])
)

# Apply the distortion correction
undistorted_img = cv2.undistort(img, camera_matrix, distortion_coeffs, None, new_camera_matrix)

# Optional: Crop the image to remove any remaining black borders
x, y, w, h = roi
undistorted_img = undistorted_img[y:y+h, x:x+w]

# Save the final corrected image for OCR
cv2.imwrite("temp_temp_undistorted.png", undistorted_img)

# Preview the result
cv2.imshow("Undistorted Image", undistorted_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Key Notes for Adjustment: #

• Camera Calibration: For the most accurate results, you should calibrate your camera using a chessboard pattern (OpenCV has a straightforward workflow for this). The sample parameters above are just a starting point.
• Tweak Distortion Coefficients: If the correction doesn't look right, adjust the first value in distortion_coeffs:
  • Positive values fix pincushion distortion (image edges curve inward)
  • Negative values fix barrel distortion (image edges curve outward)
• Skip Cropping: If you don't want to crop the image, remove the lines that use roi to keep the full frame (even if it has black borders).

Once you have the undistorted image, you can feed it directly into an OCR tool like Tesseract for text detection!

内容的提问来源于stack exchange，提问作者Priyank Thakur