an option to detection with craft_hw_ocr versus craft_text_detector

app.py

@@ -2,9 +2,6 @@ import PIL.Image
 import gradio as gr
 import torch
 import numpy as np
-from craft_text_detector import Craft
-
-craft = Craft(output_dir='output', crop_type="box", cuda=torch.cuda.is_available(), export_extra=True)
 
 dw=0.3
 dh=0.25
@@ -45,20 +42,31 @@ def is_signature(prediction_result) -> bool:
             return True
     return False
 
-def detect(image: PIL.Image.Image):
+def detect_with_craft_text_detector(image: PIL.Image.Image):
+    from craft_text_detector import Craft
+    craft = Craft(output_dir='output', crop_type="box", cuda=torch.cuda.is_available(), export_extra=True)
     result = craft.detect_text( np.asarray(image))
-    return result['boxes'], is_signature(result)
+    annotated = PIL.Image.open('output/image_text_detection.png')  # image with boxes displayed
+    return annotated, result['boxes'], is_signature(result)
+
+def detect_with_craft_hw_ocr(image: PIL.Image.Image):
+    image = np.asarray(image)
+    from craft_hw_ocr import OCR
+    ocr = OCR.load_models()
+    image, results = OCR.detection(image, ocr[2])
+    bboxes, _ = OCR.recoginition(image, results, ocr[0], ocr[1])
+    annotated = OCR.visualize(image, results)
+    return annotated, bboxes, False
 
-def process(image:PIL.Image.Image):
+def process(image:PIL.Image.Image, lib:str):
     if image is None:
         return None,0,False
-    boxes,signed = detect( image)
-    annotated = PIL.Image.open('output/image_text_detection.png') # image with boxes displayed
+    annotated, boxes, signed = detect_with_craft_text_detector(image) if lib=='craft_text_detector' else detect_with_craft_hw_ocr( image)
     return annotated, len(boxes), signed
 
 gr.Interface(
     fn = process,
-    inputs = [ gr.Image(type="pil", label="Input") ],
+    inputs = [ gr.Image(type="pil", label="Input"), gr.inputs.Radio(label='Model', choices=["craft_text_detector", "craft_hw_ocr"], default='craft_text_detector') ],
     outputs = [ gr.Image(type="pil", label="Output"), gr.Label(label="nb of text detections"), gr.Label(label="Has signature") ],
     title="Detect signature in image",
     description="Is the photo or image watermarked by a signature?",

requirements.txt

@@ -9,3 +9,4 @@ matplotlib
 scipy
 psutil
 craft_text_detector
+craft_hw_ocr