Lighting Condition Robustness

[Ishaan-Datta]

Histogram equalization can help improve the contrast of an image, which can be beneficial for object detection in varying lighting conditions.

import cv2

def preprocess_image(image):
    # Convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Apply histogram equalization
    equalized = cv2.equalizeHist(gray)
    return equalized`

Gamma correction adjusts the brightness of an image, which can help compensate for variations in lighting conditions.

import cv2
import numpy as np

def gamma_correction(image, gamma=1.0):
    # Build a lookup table mapping the pixel values [0, 255] to their adjusted gamma values
    inv_gamma = 1.0 / gamma
    table = np.array([((i / 255.0) ** inv_gamma) * 255 for i in np.arange(0, 256)]).astype("uint8")
    # Apply gamma correction using the lookup table
    corrected = cv2.LUT(image, table)
    return corrected

Adaptive thresholding can be useful for segmenting objects from the background in images with varying lighting conditions.

import cv2

def adaptive_thresholding(image):
    # Convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Apply adaptive thresholding
    thresholded = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
    return thresholded

Color normalization techniques can help reduce the impact of varying lighting conditions on color-based features in object detection.

import cv2

def color_normalization(image):
    # Convert image to LAB color space
    lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
    # Compute mean and standard deviation of the L channel
    l_mean, l_std = cv2.meanStdDev(lab[:, :, 0])
    # Normalize the L channel
    normalized_l = (lab[:, :, 0] - l_mean) / l_std
    # Merge normalized L channel with original AB channels
    normalized_lab = cv2.merge([normalized_l, lab[:, :, 1], lab[:, :, 2]])
    # Convert back to BGR color space
    normalized_image = cv2.cvtColor(normalized_lab, cv2.COLOR_LAB2BGR)
    return normalized_image

You can combine multiple preprocessing techniques to further enhance the robustness of your object detection system against varying lighting conditions.

def preprocess_image(image):
    # Apply a combination of preprocessing techniques
    equalized = cv2.equalizeHist(image)
    gamma_corrected = gamma_correction(equalized, gamma=1.2)
    thresholded = cv2.adaptiveThreshold(gamma_corrected, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
    return thresholded