Comparing images or movies

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With Python, comparing image using Structural Similarity Index (SSIM) or using a perceptual hashing algorithm like pHash. {html} <pre> pip install scikit-image opencv-python pip install python-phash </pre> {/html} !!Comparing Images using SSIM (Structural Similarity Index): {html} <pre> import cv2 import numpy as np from skimage import measure # Load images img1 = cv2.imread('image1.jpg', 0) # grayscale img2 = cv2.imread('image2.jpg', 0) # grayscale # Ensure both images have the same dimensions assert img1.shape == img2.shape, "Images must have the same dimensions" # Compute SSIM ssim = measure.compare_ssim(img1, img2) print("SSIM:", ssim) </pre> {/html} !!Comparing Images using pHash (Perceptual Hashing): {html} <pre> import phash # Load images img1 = cv2.imread('image1.jpg') img2 = cv2.imread('image2.jpg') # Compute pHash hash1 = phash.dct_image_hash(img1) hash2 = phash.dct_image_hash(img2) # Compare hashes distance = hamming_distance(hash1, hash2) print("Hamming Distance:", distance) </pre> {/html} Structural Similarity Index (SSIM) and perceptual hashing algorithms like pHash can be useful for comparing images that are not identical but visually similar. These techniques are based on different principles, but both aim to quantify the similarity between images from a human perception perspective. {br} # Structural Similarity Index (SSIM): SSIM is a metric that compares two images by assessing their structural information. It takes into account luminance, contrast, and structure to calculate a similarity score between 0 and 1, with 1 indicating perfect similarity. To use SSIM for image comparison, follow these steps: {br} * Preprocess images: Resize and normalize the images to a common size. * Calculate local mean, variance, and covariance for each image. * Compute the SSIM score for each local window (block) in the images. * Average the SSIM scores across all windows to get the global SSIM score. * A higher global SSIM score indicates greater similarity. {br} # Perceptual Hashing (pHash): Perceptual hashing algorithms create a compact digital representation (hash) of an image that captures its visual essence. When comparing two images, their hash values are compared to determine similarity. pHash uses the Discrete Cosine Transform (DCT) and principal component analysis (PCA) to reduce high-frequency noise and retain essential features. Here's a basic outline: {br} * Preprocess images: Resize the images to a small size (e.g., 8x8 pixels). * Apply DCT to the resized image to convert it into frequency coefficients. * Keep only the most significant coefficients (e.g., the top-left 8x8 block). * Apply PCA to reduce dimensions and discard less important components. * Create a hash value from the remaining coefficients (e.g., by averaging or converting to binary). * Compare the hash values of the two images using a distance metric (e.g., Hamming distance). {br} For example, take a bird species identification problem, you might use a combination of these techniques: {br} Feature extraction: Extract relevant features from images, such as color histograms, texture descriptors, or even deep learning-based features (e.g., using pre-trained convolutional neural networks). {br} # Similarity matching: Use SSIM or pHash to compare feature vectors or image representations from different birds. # Classifier: Train a classifier (e.g., SVM, Random Forest, or a deep learning model) to recognize specific bird species based on extracted features. This step is crucial for accurate identification. # Similarity threshold: Set a threshold for similarity scores; if the score is above the threshold, consider the images to be similar enough to belong to the same species. {br} Remember that for species identification, it's often beneficial to use more advanced computer vision techniques, like object detection and recognition, along with deep learning models trained on large datasets of labeled bird images. https://github.com/vikhyat/moondream
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