pisahkan_ktp is a Python function that extracts province, NIK, and personal information from an image of an Indonesian National Identity Card (KTP). It utilizes image processing techniques to locate and isolate relevant sections of the KTP image, then extracts text data accurately. The extracted information is returned in a structured format, facilitating further processing or integration into other applications.

• Python 3.7 or Higher
• numpy
• opencv-python
• opencv-contrib-python
• pythonRLSA

• Extracts province, NIK, and personal information from Indonesian National Identity Card (KTP) images.
• Utilizes image processing techniques to locate and isolate relevant sections accurately.
• Returns extracted information in a structured format for easy integration and further processing.

1. Clone Repository

   git clone https://github.com/hanifabd/pisahkan-ktp
   

2. Installation

   cd pisahkan-ktp && pip install dist/pisahkan_ktp-0.2.11-py3-none-any.whl
   

1. Installation

   pip install pisahkan-ktp

1. Usage

   • Text Area

     • Standard Segmenter

       # Input ==> Image Path
       from pisahkan_ktp.ktp_segmenter import segmenter
       
       image_path = "./tests/sample.jpg"
       result = segmenter(image_path)
       print(result)
       
       # Input ==> Numpy Array Image ==> cv2.imread(image_path)
       from pisahkan_ktp.ktp_segmenter import segmenter_ndarray
       
       image_path = "./tests/sample.jpg"
       image = cv2.imread(image_path)
       result = segmenter_ndarray(image)
       print(result)

     • Adaptive Segmenter (Adjust contrast level in preprocessing)

       # Input ==> Image Path
       from pisahkan_ktp.ktp_segmenter import adaptive_segmenter
       
       image_path = "./tests/sample.jpg"
       result = adaptive_segmenter(image_path, contrast_factor=1.7, delta_contrast=0.7, gamma_factor=1.0)
       print(result)
       
       # Input ==> Numpy Array Image ==> cv2.imread(image_path)
       from pisahkan_ktp.ktp_segmenter import adaptive_segmenter_ndarray
       
       image_path = "./tests/sample.jpg"
       image = cv2.imread(image_path)
       result = adaptive_segmenter_ndarray(image, contrast_factor=1.7, delta_contrast=0.7, gamma_factor=1.0)
       print(result)

   • Pass-Photo & Signature

     # Input ==> Image Path
     from pisahkan_ktp.ktp_segmenter import getPassPhoto, getSignature
     
     image_path = "./tests/sample.jpg"
     result = getPassPhoto(image_path)
     # Output Image Numpy Array
     
     # Input ==> Numpy Array Image ==> cv2.imread(image_path)
     from pisahkan_ktp.ktp_segmenter import getPassPhotoNdarray, getSignatureNdarray
     
     image_path = "./tests/sample.jpg"
     image = cv2.imread(image_path)
     result = getPassPhotoNdarray(image)
     # Output Image Numpy Array

   NOTE!!! Input image must be a clear Indonesian ID Card (KTP) no/less background noise for optimal performance

2. Result Text Area

   {
       "image": [originalImage],
       "provinsiArea": [segmented_provinsi_img_matrix_list],
       "nikArea": [segmented_nik_img_matrix_list],
       "detailArea": [segmented_detail_img_matrix_list],
   }

3. Preview

   • Original Image

     

   • Provinsi Area Cropped

     

   • NIK Area Cropped

     

   • Detail Area Cropped

     

from pisahkan_ktp.ktp_segmenter import segmenter
import matplotlib.pyplot as plt
import cv2

def show_result(result_dict):
    num_boxes = len(result_dict)
    fig, axes = plt.subplots(num_boxes, 1)
    if num_boxes == 1:
        axes = [axes]
    for i, bbox in enumerate(result_dict):
        ax = axes[i]
        if bbox.size:
            ax.imshow(cv2.cvtColor(bbox, cv2.COLOR_BGR2RGB))
            ax.axis('off')
    plt.tight_layout()
    plt.show()

image_path = "./tests/sample.jpg"
result = segmenter(image_path)

# Close pop up window first to see other result -> VSCODE
show_result(result["provinsiArea"])
show_result(result["nikArea"])
show_result(result["detailArea"])

from pisahkan_ktp.ktp_segmenter import segmenter_ndarray
import matplotlib.pyplot as plt
import cv2

def show_result(result_dict):
    num_boxes = len(result_dict)
    fig, axes = plt.subplots(num_boxes, 1)
    if num_boxes == 1:
        axes = [axes]
    for i, bbox in enumerate(result_dict):
        ax = axes[i]
        if bbox.size:
            ax.imshow(cv2.cvtColor(bbox, cv2.COLOR_BGR2RGB))
            ax.axis('off')
    plt.tight_layout()
    plt.show()

image_path = "./tests/sample.jpg"
image = cv2.imread(image_path)
result = segmenter_ndarray(image)

# Close pop up window first to see other result
show_result(result["provinsiArea"])
show_result(result["nikArea"])
show_result(result["detailArea"])