Initial setup

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+

README.md

@@ -1,13 +1,13 @@
 ---
 title: Eynollah Demo
-emoji: 🌖
-colorFrom: blue
+emoji: 👁
+colorFrom: green
 colorTo: red
 sdk: gradio
-sdk_version: 4.27.0
+sdk_version: 4.26.0
 app_file: app.py
 pinned: false
-license: apache-2.0
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,194 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+from huggingface_hub import from_pretrained_keras
+
+def resize_image(img_in,input_height,input_width):
+    return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST)
+
+def do_prediction(model_name, img):
+    model = from_pretrained_keras(model_name)
+
+    match model_name:
+        # numerical output
+        case "SBB/eynollah-column-classifier": 
+                      
+            num_col=model.layers[len(model.layers)-1].output_shape[1]
+            return "Found {} columns".format(num_col), None
+                            
+        # bitmap output
+        case "SBB/eynollah-binarization" | "SBB/eynollah-page-extraction" | "SBB/eynollah-textline" | "SBB/eynollah-textline_light" | "SBB/eynollah-enhancement" | "SBB/eynollah-tables" | "SBB/eynollah-main-regions" | "SBB/eynollah-main-regions-aug-rotation" | "SBB/eynollah-main-regions-aug-scaling" | "SBB/eynollah-main-regions-ensembled" | "SBB/eynollah-full-regions-1column" | "SBB/eynollah-full-regions-3pluscolumn": 
+            
+            img_height_model=model.layers[len(model.layers)-1].output_shape[1]
+            img_width_model=model.layers[len(model.layers)-1].output_shape[2]
+            n_classes=model.layers[len(model.layers)-1].output_shape[3]
+
+            if img.shape[0] < img_height_model:
+                img = resize_image(img, img_height_model, img.shape[1])
+
+            if img.shape[1] < img_width_model:
+                img = resize_image(img, img.shape[0], img_width_model)
+
+            marginal_of_patch_percent = 0.1
+            margin = int(marginal_of_patch_percent * img_height_model)
+            width_mid = img_width_model - 2 * margin
+            height_mid = img_height_model - 2 * margin
+            img = img / float(255.0)
+            img = img.astype(np.float16)
+            img_h = img.shape[0]
+            img_w = img.shape[1]
+            prediction_true = np.zeros((img_h, img_w, 3))
+            mask_true = np.zeros((img_h, img_w))
+            nxf = img_w / float(width_mid)
+            nyf = img_h / float(height_mid)
+            nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
+            nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
+
+            for i in range(nxf):
+                for j in range(nyf):
+                    if i == 0:
+                        index_x_d = i * width_mid
+                        index_x_u = index_x_d + img_width_model
+                    else:
+                        index_x_d = i * width_mid
+                        index_x_u = index_x_d + img_width_model
+                    if j == 0:
+                        index_y_d = j * height_mid
+                        index_y_u = index_y_d + img_height_model
+                    else:
+                        index_y_d = j * height_mid
+                        index_y_u = index_y_d + img_height_model
+                    if index_x_u > img_w:
+                        index_x_u = img_w
+                        index_x_d = img_w - img_width_model
+                    if index_y_u > img_h:
+                        index_y_u = img_h
+                        index_y_d = img_h - img_height_model
+
+                    img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
+                    label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
+                                                 verbose=0)
+
+                    seg = np.argmax(label_p_pred, axis=3)[0]
+
+                    seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
+
+                    if i == 0 and j == 0:
+                        seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
+                        #seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
+                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
+                        prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
+                    elif i == nxf - 1 and j == nyf - 1:
+                        seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
+                        #seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
+                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
+                        prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
+                    elif i == 0 and j == nyf - 1:
+                        seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
+                        #seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
+                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
+                        prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
+                    elif i == nxf - 1 and j == 0:
+                        seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
+                        #seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
+                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
+                        prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
+                    elif i == 0 and j != 0 and j != nyf - 1:
+                        seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
+                        #seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
+                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
+                        prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
+                    elif i == nxf - 1 and j != 0 and j != nyf - 1:
+                        seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
+                        #seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
+                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
+                        prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
+                    elif i != 0 and i != nxf - 1 and j == 0:
+                        seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
+                        #seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
+                        #mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
+                        prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
+                    elif i != 0 and i != nxf - 1 and j == nyf - 1:
+                        seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
+                        #seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
+                        #mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
+                        prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
+                    else:
+                        seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
+                        #seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
+                        #mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
+                        prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
+
+            prediction_true = prediction_true.astype(np.uint8)            
+
+            '''
+            img = img / float(255.0)
+            image = resize_image(image, 224,448)
+            prediction = model.predict(image.reshape(1,224,448,image.shape[2]))
+            prediction = tf.squeeze(tf.round(prediction))
+
+            prediction = np.argmax(prediction,axis=2)
+
+            prediction = np.repeat(prediction[:, :, np.newaxis]*255, 3, axis=2)
+            print(prediction.shape)
+            
+            '''
+            prediction_true = prediction_true * -1
+            prediction_true = prediction_true + 1
+            return "No numerical output", prediction_true * 255
+        
+        # catch-all (we should not reach this)
+        case _:
+            return None, None
+
+title = "Welcome to the Eynollah Demo page! 👁️"
+description = """
+ <div class="row" style="display: flex">
+  <div class="column" style="flex: 50%; font-size: 17px">
+        This Space demonstrates the functionality of various Eynollah models developed at <a rel="nofollow" href="https://huggingface.co/SBB">SBB</a>.
+        <br><br>
+        The Eynollah suite introduces an <u>end-to-end pipeline</u> to extract layout, text lines and reading order for historic documents, where the output can be used as an input for OCR engines.
+        Please keep in mind that with this demo you can just use <u>one of the 13 sub-modules</u> of the whole Eynollah system <u>at a time</u>.
+  </div>
+  <div class="column" style="flex: 5%; font-size: 17px"></div>
+  <div class="column" style="flex: 45%; font-size: 17px">
+    <strong style="font-size: 19px">Resources for more information:</strong>
+        <ul>
+            <li>The GitHub Repo can be found <a rel="nofollow" href="https://github.com/qurator-spk/eynollah">here</a></li>
+            <li>Associated Paper: <a rel="nofollow" href="https://doi.org/10.1145/3604951.3605513">Document Layout Analysis with Deep Learning and Heuristics</a></li>
+            <li>The full Eynollah pipeline can be viewed <a rel="nofollow" href="https://huggingface.co/spaces/SBB/eynollah-demo-test/blob/main/eynollah-flow.png">here</a></li>
+        </ul>
+    </li>
+  </div>
+</div> 
+"""
+iface = gr.Interface(
+            title=title,
+            description=description,
+            fn=do_prediction, 
+            inputs=[
+                gr.Dropdown([
+                    "SBB/eynollah-binarization", 
+                    "SBB/eynollah-enhancement",
+                    "SBB/eynollah-page-extraction", 
+                    "SBB/eynollah-column-classifier",
+                    "SBB/eynollah-tables",
+                    "SBB/eynollah-textline",
+                    "SBB/eynollah-textline_light",
+                    "SBB/eynollah-main-regions",
+                    "SBB/eynollah-main-regions-aug-rotation",
+                    "SBB/eynollah-main-regions-aug-scaling",
+                    "SBB/eynollah-main-regions-ensembled",
+                    "SBB/eynollah-full-regions-1column",
+                    "SBB/eynollah-full-regions-3pluscolumn"
+                ], label="Select one model of the Eynollah suite 👇", info=""),
+                gr.Image()
+            ], 
+            outputs=[
+              gr.Textbox(label="Output of model (numerical or bitmap) ⬇️"),
+              gr.Image()
+            ],
+            #examples=[['example-1.jpg']]
+        )
+iface.launch()

requirements.txt

@@ -0,0 +1,6 @@
+tensorflow == 2.12
+opencv-python
+tqdm
+pandas
+seaborn
+huggingface_hub