Update process_img.py

process_img.py

@@ -1,48 +1,48 @@
-import numpy as np
-from PIL import Image, ImageOps
-import logging
-
-class Image_Processor:
-    def __init__(self):
-        pass
-    def is_image_white_by_percentage(self,image_path, white_threshold):
-        image = image_path.convert('RGB')
-        image_np = np.array(image)
-        white_pixel = np.array([255, 255, 255])
-        white_pixels_count = np.sum(np.all(image_np == white_pixel, axis=-1))
-        total_pixels = image_np.shape[0] * image_np.shape[1]
-        white_pixel_percentage = (white_pixels_count / total_pixels) * 100
-        return white_pixel_percentage > white_threshold
-
-    def padding_white(self,image, output_size=(224, 224)):
-        # Ensure image is in RGB mode before padding
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
-        new_image = ImageOps.pad(image, output_size, method=Image.Resampling.LANCZOS, color=(255, 255, 255))
-        return new_image
-
-    def resize_image_with_aspect_ratio(self,img):
-        target_size=224
-        width, height = img.size
-        original_aspect_ratio = width / height
-        if width > height:
-            new_width = target_size
-            new_height = int(target_size / original_aspect_ratio)
-        else:
-            new_height = target_size
-            new_width = int(target_size * original_aspect_ratio)
-        resized_img = img.resize((new_width, new_height))
-        return resized_img
-    
-    def get_processed_img(self,image):
-        white_thresh = self.is_image_white_by_percentage(image,50)
-        if white_thresh == True:
-            resized_image = self.resize_image_with_aspect_ratio(image)
-            final_image = self.padding_white(resized_image)
-            logging.info('Resized and Padded Image')
-        else:
-            final_image = self.resize_image_with_aspect_ratio(image)
-            logging.info('Resized Image')
-        
-        final_image = final_image.convert('L') if final_image.mode != 'L' else final_image
+import numpy as np
+from PIL import Image, ImageOps
+import logging
+
+class Image_Processor:
+    def __init__(self):
+        pass
+    def is_image_white_by_percentage(self,image_path, white_threshold):
+        image = image_path.convert('RGB')
+        image_np = np.array(image)
+        white_pixel = np.array([255, 255, 255])
+        white_pixels_count = np.sum(np.all(image_np == white_pixel, axis=-1))
+        total_pixels = image_np.shape[0] * image_np.shape[1]
+        white_pixel_percentage = (white_pixels_count / total_pixels) * 100
+        return white_pixel_percentage > white_threshold
+
+    def padding_white(self,image, output_size=(224, 224)):
+        # Ensure image is in RGB mode before padding
+        if image.mode != 'RGB':
+            image = image.convert('RGB')
+        new_image = ImageOps.pad(image, output_size, method=Image.Resampling.LANCZOS, color=(255, 255, 255))
+        return new_image
+
+    def resize_image_with_aspect_ratio(self,img):
+        target_size=336
+        width, height = img.size
+        original_aspect_ratio = width / height
+        if width > height:
+            new_width = target_size
+            new_height = int(target_size / original_aspect_ratio)
+        else:
+            new_height = target_size
+            new_width = int(target_size * original_aspect_ratio)
+        resized_img = img.resize((new_width, new_height))
+        return resized_img
+    
+    def get_processed_img(self,image):
+        white_thresh = self.is_image_white_by_percentage(image,50)
+        if white_thresh == True:
+            resized_image = self.resize_image_with_aspect_ratio(image)
+            final_image = self.padding_white(resized_image)
+            logging.info('Resized and Padded Image')
+        else:
+            final_image = self.resize_image_with_aspect_ratio(image)
+            logging.info('Resized Image')
+        
+        final_image = final_image.convert('L') if final_image.mode != 'L' else final_image
         return final_image