Update app.py

app.py

@@ -1,87 +1,84 @@
-import gradio as gr
-import sys
-sys.path.append('../src/')
-from utils.helpers import OCRD
-
-
-
-def run_ocrd_pipeline(img_path, status=gr.Progress(), binarize_mode='detailed', min_pixel_sum=30, median_bounds=(None, None), font_size=30):
-    """
-    Executes the OCRD pipeline on an image from file loading to text overlay creation. This function orchestrates
-    the calling of various OCRD class methods to process the image, extract and recognize text, and then overlay
-    this text on the original image.
-
-    Parameters:
-        img_path (str): Path to the image file.
-        binarize_mode (str): Mode to be used for image binarization. Can be 'detailed', 'fast', or 'no'.
-        min_pixel_sum (int, optional): Minimum sum of pixels to consider a text line segmentation for extraction. 
-            If 'default', default values are applied.
-        median_bounds (tuple, optional): Bounds to filter text line segmentations based on size relative to the median. 
-            If 'default', default values are applied.
-        font_size (int, optional): Font size to be used in text overlay. If 'default', a default size or scaling logic is applied.
-
-    Returns:
-        Image: An image with overlay text, where text is extracted and recognized from the original image.
-
-    This function handles:
-    - Image binarization.
-    - Text line segmentation.
-    - Text line extraction and deskewing.
-    - Optical character recognition on text lines.
-    - Creating an image overlay with recognized text.
-    """
-    
-    # prepare kwargs
-    efadt_kwargs = {}
-    if min_pixel_sum != 'default':
-        efadt_kwargs['min_pixel_sum'] = min_pixel_sum
-    if median_bounds != 'default':
-        efadt_kwargs['median_bounds'] = median_bounds
-
-    ctoi_kwargs = {}
-    if font_size != 'default':
-        ctoi_kwargs['font_size'] = font_size
-
-    # run pipeline
-    #status(0, desc="\nReading image...\n")
-    ocrd = OCRD(img_path)
-    status(0, desc='\nStep 1/5: Binarizing image...\n')
-    binarized = ocrd.binarize_image(ocrd.image, binarize_mode)
-    status(0, desc='\nStep 2/5: Segmenting textlines...\n')
-    textline_segments = ocrd.segment_textlines(binarized)
-    status(0, desc='\nStep 3/5: Extracting, filtering and de-skewing textlines...\n')
-    image_scaled = ocrd.scale_image(ocrd.image)  # textline_segments were predicted on rescaled image
-    textline_images, _ = ocrd.extract_filter_and_deskew_textlines(image_scaled, textline_segments[...,0], **efadt_kwargs)
-    status(0, desc='\nStep 4/5: OCR on textlines...\n')
-    textline_preds = ocrd.ocr_on_textlines(textline_images)
-    status(0, desc='\nStep 5/5: Creating output overlay image...')
-    img_gen = ocrd.create_text_overlay_image(textline_images, textline_preds, (image_scaled.shape[0], image_scaled.shape[1]), **ctoi_kwargs)
-    status(1, desc='\nJOB COMPLETED\n')
-
-    return img_gen
-
-
-demo_data = [
-    '../src/demo_data/act_image.jpg',
-    '../src/demo_data/newjersey1_image.jpg',
-    '../src/demo_data/newjersey2_image.jpg',
-    '../src/demo_data/notes_image.jpg',
-    '../src/demo_data/washington_image.jpg'
-]
-
-
-iface = gr.Interface(run_ocrd_pipeline,
-                     title="OCRD Pipeline",
-                     description="<ul><li>This interactive demo showcases an 'Optical Character Recognition Digitization' pipeline that processes \
-                                  images to recognize text.</li> \
-                                  <li>Steps include binarization, text line segmentation, extraction, filtering and deskewing as well as OCR. \
-                                  Results are displayed as a generated overlay image.</li> \
-                                  <li>Optimized for English; other languages (e.g. German) may require OCR model fine-tuning.</li> \
-                                  <li>Uses free CPU-based compute, which is rather slow. A pipeline run will take up to 10 minutes. \
-                                  For lengthy waits, pre-computed demo results are available for download: https://github.com/pluniak/ocrd/tree/main/src/demo_data.</li> \
-                                  <li>Note: The demo is just a first version! OCR performance and computation speed can be optimized.</li> \
-                                  <li>The demo is based on code from my GitHub repository: https://github.com/pluniak/ocrd/tree/main</li></ul>",
-                     inputs=[gr.Image(type='filepath', label='Input image')],
-                     outputs=gr.Image(label='Output image: overlay with recognized text', type='pil', format='jpeg'),
-                     examples=demo_data)
+import gradio as gr
+from helpers import OCRD
+
+
+def run_ocrd_pipeline(img_path, status=gr.Progress(), binarize_mode='detailed', min_pixel_sum=30, median_bounds=(None, None), font_size=30):
+    """
+    Executes the OCRD pipeline on an image from file loading to text overlay creation. This function orchestrates
+    the calling of various OCRD class methods to process the image, extract and recognize text, and then overlay
+    this text on the original image.
+
+    Parameters:
+        img_path (str): Path to the image file.
+        binarize_mode (str): Mode to be used for image binarization. Can be 'detailed', 'fast', or 'no'.
+        min_pixel_sum (int, optional): Minimum sum of pixels to consider a text line segmentation for extraction. 
+            If 'default', default values are applied.
+        median_bounds (tuple, optional): Bounds to filter text line segmentations based on size relative to the median. 
+            If 'default', default values are applied.
+        font_size (int, optional): Font size to be used in text overlay. If 'default', a default size or scaling logic is applied.
+
+    Returns:
+        Image: An image with overlay text, where text is extracted and recognized from the original image.
+
+    This function handles:
+    - Image binarization.
+    - Text line segmentation.
+    - Text line extraction and deskewing.
+    - Optical character recognition on text lines.
+    - Creating an image overlay with recognized text.
+    """
+    
+    # prepare kwargs
+    efadt_kwargs = {}
+    if min_pixel_sum != 'default':
+        efadt_kwargs['min_pixel_sum'] = min_pixel_sum
+    if median_bounds != 'default':
+        efadt_kwargs['median_bounds'] = median_bounds
+
+    ctoi_kwargs = {}
+    if font_size != 'default':
+        ctoi_kwargs['font_size'] = font_size
+
+    # run pipeline
+    #status(0, desc="\nReading image...\n")
+    ocrd = OCRD(img_path)
+    status(0, desc='\nStep 1/5: Binarizing image...\n')
+    binarized = ocrd.binarize_image(ocrd.image, binarize_mode)
+    status(0, desc='\nStep 2/5: Segmenting textlines...\n')
+    textline_segments = ocrd.segment_textlines(binarized)
+    status(0, desc='\nStep 3/5: Extracting, filtering and de-skewing textlines...\n')
+    image_scaled = ocrd.scale_image(ocrd.image)  # textline_segments were predicted on rescaled image
+    textline_images, _ = ocrd.extract_filter_and_deskew_textlines(image_scaled, textline_segments[...,0], **efadt_kwargs)
+    status(0, desc='\nStep 4/5: OCR on textlines...\n')
+    textline_preds = ocrd.ocr_on_textlines(textline_images)
+    status(0, desc='\nStep 5/5: Creating output overlay image...')
+    img_gen = ocrd.create_text_overlay_image(textline_images, textline_preds, (image_scaled.shape[0], image_scaled.shape[1]), **ctoi_kwargs)
+    status(1, desc='\nJOB COMPLETED\n')
+
+    return img_gen
+
+
+demo_data = [
+    '../src/demo_data/act_image.jpg',
+    '../src/demo_data/newjersey1_image.jpg',
+    '../src/demo_data/newjersey2_image.jpg',
+    '../src/demo_data/notes_image.jpg',
+    '../src/demo_data/washington_image.jpg'
+]
+
+
+iface = gr.Interface(run_ocrd_pipeline,
+                     title="OCRD Pipeline",
+                     description="<ul><li>This interactive demo showcases an 'Optical Character Recognition Digitization' pipeline that processes \
+                                  images to recognize text.</li> \
+                                  <li>Steps include binarization, text line segmentation, extraction, filtering and deskewing as well as OCR. \
+                                  Results are displayed as a generated overlay image.</li> \
+                                  <li>Optimized for English; other languages (e.g. German) may require OCR model fine-tuning.</li> \
+                                  <li>Uses free CPU-based compute, which is rather slow. A pipeline run will take up to 10 minutes. \
+                                  For lengthy waits, pre-computed demo results are available for download: https://github.com/pluniak/ocrd/tree/main/src/demo_data.</li> \
+                                  <li>Note: The demo is just a first version! OCR performance and computation speed can be optimized.</li> \
+                                  <li>The demo is based on code from my GitHub repository: https://github.com/pluniak/ocrd/tree/main</li></ul>",
+                     inputs=[gr.Image(type='filepath', label='Input image')],
+                     outputs=gr.Image(label='Output image: overlay with recognized text', type='pil', format='jpeg'),
+                     examples=demo_data)
 iface.launch()