changes image quality check

extract_tools.py

@@ -13,7 +13,7 @@ from utils import draw_panoptic_segmentation
 from tool_utils.clip_segmentation import CLIPSEG
 from tool_utils.object_extractor import create_object_extraction_chain
 from tool_utils.yolo_world import YoloWorld
-from tool_utils.image_metadata import image_brightness,variance_of_laplacian,get_signal_to_noise_ratio
+from tool_utils.image_qualitycheck import brightness_check,gaussian_noise_check,snr_check
 
 try:
     from transformers import BlipProcessor, BlipForConditionalGeneration
@@ -170,9 +170,9 @@ def get_image_quality(image_path:str)->str:
     image = cv2.imread(image_path)
     image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
     
-    brightness_text = image_brightness(image)
-    blurry_text = variance_of_laplacian(image)
-    snr_text = get_signal_to_noise_ratio(image)
+    brightness_text = brightness_check(image)
+    blurry_text = gaussian_noise_check(image)
+    snr_text = snr_check(image)
     final_text = "Image properties are :\n{}\n{}\n{}".format(blurry_text, brightness_text,snr_text)
     return final_text
 

tool_utils/{image_metadata.py → image_qualitycheck.py}

@@ -4,7 +4,7 @@ import cv2
 from skimage.restoration import estimate_sigma
 import logging
 
-def image_brightness(image,thresh=0.37):
+def brightness_check(image,thresh=0.37):
     L,A,B = cv2.split(cv2.cvtColor(image,cv2.COLOR_BGR2LAB))
     norm_L = L/np.max(L)
     L_mean = np.mean(norm_L)
@@ -13,7 +13,7 @@ def image_brightness(image,thresh=0.37):
     else:
         return "image is not bright enough to process object detection and segmentation task "
 
-def variance_of_laplacian(img,threshould=250):
+def gaussian_noise_check(img,threshould=250):
     # compute the Laplacian of the image and then return the focus
     # measure, which is simply the variance of the Laplacian
     gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
@@ -27,7 +27,7 @@ def variance_of_laplacian(img,threshould=250):
     elif laplacian_value >= 3*threshould:
         return "Image has high sharpness , no need to process futher." 
 
-def get_signal_to_noise_ratio(image):
+def snr_check(image):
     snr_text = None 
     snr_value = estimate_sigma(cv2.cvtColor(image,cv2.COLOR_RGB2GRAY), average_sigmas=False)
     logging.info(snr_value)