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import cv2
import numpy as np
import os
import logging
from utils import read_yaml, file_exists
logging_str = "[%(asctime)s: %(levelname)s: %(module)s]: %(message)s"
log_dir = "logs"
os.makedirs(log_dir, exist_ok=True)
logging.basicConfig(filename=os.path.join(log_dir,"ekyc_logs.log"), level=logging.INFO, format=logging_str, filemode="a")
config_path = "config.yaml"
config = read_yaml(config_path)
artifacts = config['artifacts']
intermediate_dir_path = artifacts['INTERMIDEIATE_DIR']
conour_file_name = artifacts['CONTOUR_FILE']
def read_image(image_path, is_uploaded=False):
if is_uploaded:
try:
# Read image using OpenCV
image_bytes = image_path.read()
img = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), cv2.IMREAD_COLOR)
if img is None:
logging.info("Failed to read image: {}".format(image_path))
raise Exception("Failed to read image: {}".format(image_path))
return img
except Exception as e:
logging.info(f"Error reading image: {e}")
print("Error reading image:", e)
return None
else:
try:
img = cv2.imread(image_path)
if img is None:
logging.info("Failed to read image: {}".format(image_path))
raise Exception("Failed to read image: {}".format(image_path))
return img
except Exception as e:
logging.info(f"Error reading image: {e}")
print("Error reading image:", e)
return None
def extract_id_card(img):
"""
Extracts the ID card from an image containing other backgrounds.
Args:
img (np.ndarray): The input image.
Returns:
np.ndarray: The cropped image containing the ID card, or None if no ID card is detected.
"""
# Convert image to grayscale
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Noise reduction
blur = cv2.GaussianBlur(gray_img, (5, 5), 0)
# Adaptive thresholding
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
# Find contours
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Select the largest contour (assuming the ID card is the largest object)
largest_contour = None
largest_area = 0
for cnt in contours:
area = cv2.contourArea(cnt)
if area > largest_area:
largest_contour = cnt
largest_area = area
# If no large contour is found, assume no ID card is present
if not largest_contour.any():
return None
# Get bounding rectangle of the largest contour
x, y, w, h = cv2.boundingRect(largest_contour)
logging.info(f"contours are found at, {(x, y, w, h)}")
# logging.info("Area largest_area)
# Apply additional filtering (optional):
# - Apply bilateral filtering for noise reduction
# filtered_img = cv2.bilateralFiltering(img[y:y+h, x:x+w], 9, 75, 75)
# - Morphological operations (e.g., erosion, dilation) for shape refinement
current_wd = os.getcwd()
filename = os.path.join(current_wd,intermediate_dir_path, conour_file_name)
contour_id = img[y:y+h, x:x+w]
is_exists = file_exists(filename)
if is_exists:
# Remove the existing file
os.remove(filename)
cv2.imwrite(filename, contour_id)
return contour_id, filename
def save_image(image, filename, path="."):
"""
Saves an image to a specified path with the given filename.
Args:
image (np.ndarray): The image data (NumPy array).
filename (str): The desired filename for the saved image.
path (str, optional): The directory path to save the image. Defaults to "." (current directory).
"""
# Construct the full path
full_path = os.path.join(path, filename)
is_exists = file_exists(full_path)
if is_exists:
# Remove the existing file
os.remove(full_path)
# Save the image using cv2.imwrite
cv2.imwrite(full_path, image)
logging.info(f"Image saved successfully: {full_path}")
return full_path
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