0.41

image_details.py

@@ -0,0 +1,98 @@
+import torch
+import numpy as np
+from PIL import Image
+import io
+
+class ImageDetails:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image_input": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("INT", "INT", "BOOL", "STRING", "STRING", "STRING")
+    RETURN_NAMES = ("WIDTH", "HEIGHT", "HAS_TRANSPARENCY", "ORIENTATION", "TYPE", "ALL")
+    FUNCTION = "show_image_details"
+    OUTPUT_NODE = True
+    CATEGORY = "Bjornulf"
+
+    def show_image_details(self, image_input):
+        if isinstance(image_input, torch.Tensor):
+            is_tensor = True
+            input_type = "tensor"
+            # Ensure the tensor is on CPU and convert to numpy
+            image_input = image_input.cpu().numpy()
+        elif isinstance(image_input, (bytes, bytearray)):
+            is_tensor = False
+            input_type = "bytes"
+            image_input = [image_input]  # Wrap single bytes object in a list
+        else:
+            is_tensor = False
+            input_type = "bytes"
+        
+        all_widths, all_heights, all_transparencies, all_details, all_orientations = [], [], [], [], []
+
+        if is_tensor:
+            # Handle tensor images
+            if len(image_input.shape) == 5:  # (batch, 1, channels, height, width)
+                image_input = np.squeeze(image_input, axis=1)
+            
+            batch_size = image_input.shape[0]
+            for i in range(batch_size):
+                image = image_input[i]
+                
+                # Ensure the image is in HxWxC format
+                if image.shape[0] == 3 or image.shape[0] == 4:  # If it's in CxHxW format
+                    image = np.transpose(image, (1, 2, 0))  # Change to HxWxC
+                
+                # Normalize to 0-255 range if necessary
+                if image.max() <= 1:
+                    image = (image * 255).astype('uint8')
+                else:
+                    image = image.astype('uint8')
+                
+                pil_image = Image.fromarray(image)
+                self.process_image(pil_image, input_type, all_widths, all_heights, all_transparencies, all_details, all_orientations)
+        else:
+            # Handle bytes-like objects
+            batch_size = len(image_input)
+            for i in range(batch_size):
+                pil_image = Image.open(io.BytesIO(image_input[i]))
+                self.process_image(pil_image, input_type, all_widths, all_heights, all_transparencies, all_details, all_orientations)
+
+        # Combine all details into a single string
+        combined_details = "\n".join(all_details)
+
+        # Return the details of the first image, plus the combined details string
+        return (all_widths[0], all_heights[0], all_transparencies[0], all_orientations[0], 
+                input_type, combined_details)
+
+    def process_image(self, pil_image, input_type, all_widths, all_heights, all_transparencies, all_details, all_orientations):
+        # Get image details
+        width, height = pil_image.size
+        has_transparency = pil_image.mode in ('RGBA', 'LA') or \
+                           (pil_image.mode == 'P' and 'transparency' in pil_image.info)
+
+        # Determine orientation
+        if width > height:
+            orientation = "landscape"
+        elif height > width:
+            orientation = "portrait"
+        else:
+            orientation = "square"
+
+        # Prepare the ALL string
+        details = f"\nType: {input_type}"
+        details += f"\nWidth: {width}"
+        details += f"\nHeight: {height}"
+        details += f"\nLoaded with transparency: {has_transparency}"
+        details += f"\nImage Mode: {pil_image.mode}"
+        details += f"\nOrientation: {orientation}\n"
+
+        all_widths.append(width)
+        all_heights.append(height)
+        all_transparencies.append(has_transparency)
+        all_details.append(details)
+        all_orientations.append(orientation)