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Using Katna.image

Crop a single image

Step 1

Import the image module.

from Katna.image import Image

Step 2

Instantiate the image class.

img_module = Image()

Step 3

Call the crop_image method. This method accepts following parameters (Refer to API reference for further details):

file_path: image file path from which crop has to be extracted

crop_width: width of crop to extract

crop_height: height of crop to extract

no_of_crops_to_return: number of crops rectangles to be extracted

writer: Writer class instance to process image crop data for a file (use ImageCropDiskWriter from Katna.writer module to save data at a location).

filters: You can use this optional parameter to filter out unwanted crop rectangles according to some filtering criteria. At the moment only "text" detection filter is implemented and more filters will be added in future will be added in future. Passing on "text" detection filter ensures crop rectangle contains text, additionally it checks that detected "text" inside an image is not abruptly cropped by any crop_rectangle. By default, filters are not applied.

down_sample_factor: You can use this optional feature to specify the down sampling factor. For large images consider increasing this parameter for faster image cropping. By default input images are downsampled by factor of 8 before processing.

image_file_path = <Path where the image is stored>

# diskwriter to save crop data
diskwriter = ImageCropDiskWriter(location=<Path to save crops>)

img_module.crop_image(
    file_path=image_file_path,
    crop_width=<crop_width>,
    crop_height=<crop_height>,
    num_of_crops=<no_of_crops_to_return>,
    writer=diskwriter,
    filters=<filters>,
    down_sample_factor=<number_by_which_image_to_downsample>
)

Code below is a complete example for a single image.

import os.path
import cv2
from Katna.image import Image
from Katna.writer import ImageCropDiskWriter

# Extract specific number of key frames from video
img_module = Image()

# folder to save extracted images
output_folder_cropped_image = "selectedcrops"

# number of images to be returned
no_of_crops_to_returned = 3

# crop dimentions
crop_width = 1100
crop_height = 600

# Filters
filters = ["text"]
# Image file path
image_file_path = os.path.join(".", "tests", "data", "bird_img_for_crop.jpg")
print(f"image_file_path = {image_file_path}")

# diskwriter to save crop data
diskwriter = ImageCropDiskWriter(location=output_folder_cropped_image)

# crop the image and process data with diskwriter instance
img_module.crop_image(
    file_path=image_file_path,
    crop_width=crop_width,
    crop_height=crop_height,
    num_of_crops=no_of_crops_to_returned,
    writer=diskwriter,
    filters=filters,
    down_sample_factor=8
)

Crop all images in a directory

To run crop image for all images in a directory, call the crop_image_from_dir method. This method accepts following parameters and returns a dictionary containing file path as key and list of crop rectangles (in crop_rect data structure) as its values. Below are the six parameters of the function

dir_path: directory path where images from which crop has to be extracted

crop_width: width of crop to extract

crop_height: height of crop to extract

no_of_crops_to_return: number of crops rectangles to be extracted

writer: Writer class instance to process image crop data for a file (use ImageCropDiskWriter from Katna.writer module to save data at a location).

filters: You can use this optional parameter to filter out unwanted crop rectangles according to some filtering criteria. At the moment only "text" detection filter is implemented and more filters will be added in future will be added in future. Passing on "text" detection filter ensures crop rectangle contains text, additionally it checks that detected "text" inside an image is not abruptly cropped by any crop_rectangle. By default, filters are not applied.

down_sample_factor: You can use this optional feature to specify the down sampling factor. For large images consider increasing this parameter for faster image cropping. By default input images are downsampled by factor of 8 before processing.

input_dir_path = <Path to directory where images are stored>

# diskwriter to save crop data
diskwriter = ImageCropDiskWriter(location=<Path to save crops>)

img_module.crop_image_from_dir(
    dir_path=input_dir_path,
    crop_width=<crop_width>,
    crop_height=<crop_height>,
    num_of_crops=<no_of_crops_to_return>,
    writer=diskwriter,
    filters=<filters>,
    down_sample_factor=<number_by_which_image_to_downsample>
 )

Code below is a complete example for a directory containing images.

import os.path
import cv2
import ntpath
from Katna.image import Image
from Katna.writer import ImageCropDiskWriter

img_module = Image()

# folder to save extracted images
output_folder_cropped_image = "selectedcrops"

# number of images to be returned
no_of_crops_to_return = 3

# crop dimensions
crop_width = 300
crop_height = 400

# Filters
filters = ["text"]

# Directory containing images to be cropped
input_dir_path = os.path.join(".", "tests", "data")

# diskwriter to save crop data
diskwriter = ImageCropDiskWriter(location=output_folder_cropped_image)

img_module.crop_image_from_dir(
    dir_path=input_dir_path,
    crop_width=crop_width,
    crop_height=crop_height,
    num_of_crops=no_of_crops_to_return,
    writer=diskwriter,
    filters=filters,
    down_sample_factor=8
)

Note: You can create custom writers to process the data in a different way. Check the Katna.custom_writers section for details.

Resize a single image

Step 1

Import the image module.

from Katna.image import Image

Step 2

Instantiate the image class.

img_module = Image()

Step 3

Call the resize_image method. This method accepts following parameters and returns a in memory resized image in opencv format. Refer to API reference for further details. Below are the four parameters of the function

file_path: image file path from which crop has to be extracted

target_width: width of target image

target_height: height of target image

down_sample_factor: You can use this optional feature to specify the down sampling factor. For large images consider increasing this parameter for faster image resize and crop. By default input images are downsampled by factor of 8 before processing.

image_file_path = <Path where the image is stored>

crop_list = img_module.resize_image(
   file_path=image_file_path,
   target_width=<target_width>,
   target_height=<target_height>,
   down_sample_factor=<number_by_which_image_to_downsample>
)

Step 4

To save the extracted resized image call save_image_to_disk method. The method accepts following parameters and doesn't returns anything. Refer to API reference for further details.

  1. image: output image to be saved
  2. file_path: Folder location where files needs to be saved
  3. file_name: File name for the crop image to be saved.
  4. file_ext: File extension indicating the file type for example - ‘.jpg’
img_module.save_image_to_disk(image=<image>, file_path=<output_folder_cropped_image>,
       file_name=<file_name>, 
       file_ext=<file_ext>,
   )

Code below is a complete example for a single image.

import os.path
import cv2
from Katna.image import Image

def main():

    # Extract specific number of key frames from video
    img_module = Image()

    # folder to save extracted images
    output_folder_cropped_image = "resizedimages"

    if not os.path.isdir(os.path.join(".", output_folder_cropped_image)):
        os.mkdir(os.path.join(".", output_folder_cropped_image))

    # crop dimentions
    resize_width = 500
    resize_height = 600

    # Image file path
    image_file_path = os.path.join(".", "tests", "data", "bird_img_for_crop.jpg")
    print(f"image_file_path = {image_file_path}")

    resized_image = img_module.resize_image(
        file_path=image_file_path,
        target_width=resize_width,
        target_height=resize_height,
        down_sample_factor=8,
    )
    # cv2.imshow("resizedImage", resized_image)
    # cv2.waitKey(0)

    img_module.save_image_to_disk(
        resized_image,
        file_path=output_folder_cropped_image,
        file_name="resized_image",
        file_ext=".jpeg",
    )

main()

Resize all images in a directory

To run resize image for all images in a directory, call the resize_image_from_dir method. This method accepts following parameters and returns a dictionary containing file path as key and resized image (in opencv numpy format) as its values. Below are the six parameters of the function

dir_path: directory path where images from which crop has to be extracted

target_width: width of output resized image

target_height: height of output resized image

down_sample_factor: You can use this optional feature to specify the down sampling factor. For large images consider increasing this parameter for faster image cropping and resizing. By default input images are downsampled by factor of 8 before processing.

input_dir_path = <Path to directory where images are stored>

crop_list = img_module.resize_image_from_dir(
   dir_path=input_dir_path,
   target_width=<target_width>,
   target_height=<target_height>,
   down_sample_factor=<number_by_which_image_to_downsample>
)

Code below is a complete example for a directory containing images.

import os.path
from Katna.image import Image
import os
import ntpath


def main():

    img_module = Image()

    # folder to save resized images
    output_folder_resized_image = "resizedimages"

    if not os.path.isdir(os.path.join(".", output_folder_resized_image)):
        os.mkdir(os.path.join(".", output_folder_resized_image))

    # resized image dimensions
    resize_width = 500
    resize_height = 600

    # Input folder file path
    input_folder_path = os.path.join(".", "tests", "data")
    print(f"input_folder_path = {input_folder_path}")

    resized_images = img_module.resize_image_from_dir(
        dir_path=input_folder_path,
        target_width=resize_width,
        target_height=resize_height,
        down_sample_factor=8,
    )

    for filepath, resized_image in resized_images.items():
        # name of the image file
        filename = ntpath.basename(filepath)
        name = filename.split(".")[0]
        # folder path where the images will be stored
        img_module.save_image_to_disk(
            resized_image, output_folder_resized_image, name + "_resized" + "_", ".jpeg"
        )


main()

In addition, image module also has some additional features:

  1. Crop Image using cv: check Katna.image_crop_cv
  2. Crop Image maintaining asepct ratio: check Katna.image_crop_aspect_ratio