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Image Features Extractor

This project used to extract features from input folder images , these features are mainly used for ad clicking predictions . These features were used in https://www.kaggle.com/c/avito-demand-prediction contest.

Prerequisites

Install all packages in the requirements.txt

pip install -r requirements.txt

Getting Started

Run example.py to get a results.csv file including all features extracted from input test_data Folder.

Available functions

image = cv2.imread(image_path)

calculate image simplicity

Used to calculate simplicity of input image.

calculate_image_simplicity(image,c_threshold = 0.01,nchannels=3,nbins =8)

image basic segment stats

Used to extract basic image segmentation statistics (tuple of 10 features).

image_basic_segment_stats(image)

image face feats

Used to extract number of faces from input image using pretrained HaarCascade from opencv.

image_face_feats(image)

image sift feats

number of sift keypoints extracted from input image

image_sift_feats(image)

image rgb simplicity

get image simplicity feature from RGB image

image_rgb_simplicity(image)

image hsv simplicity

get image simplicity features from hsv image

image_hsv_simplicity(image)

image hue histogram

image features from histogram of HSV images

image_hue_histogram(image)

image grayscale simplicity

used for simplicity features on grayscale images

image_grayscale_simplicity(image)

image sharpness

used to calculate image sharpness score

image_sharpness(image)

image contrast

used to calculate image contrast score

image_contrast(image)

image saturation

used to calculate image saturation

image_saturation(image)

image brightness

used to calculate image brightness score

image_brightness(image)

image colorfulness

used to calculate colorfulness score based on the paper

image_colorfulness(image)

Extract image feats

used to calculate all previous features and put it in a dataframe saved to csv

extract_image_feats(out file name , input file list of images, number of parallel jobs)

Results

Results scored on https://www.kaggle.com/c/avito-demand-prediction contest These features with simple LightGBM model it got me (Root Mean Squared Error (RMSE):

  • 0.2207 on public leaderboard
  • 0.2246 on private leaderboard

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