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app (1).py

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+
+# import os
+# import sys
+# import numpy as np
+# import cv2
+
+# # ==========================================
+# # 🔧 PATCH 1: Fix Torchvision Compatibility
+# # ==========================================
+# import torchvision.models.vgg
+# if not hasattr(torchvision.models.vgg, 'model_urls'):
+#     torchvision.models.vgg.model_urls = {
+#         'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth'
+#     }
+
+# # ==========================================
+# # 🔧 PATCH 2: Fix NumPy Crash AND Coordinates
+# # ==========================================
+# import craft_text_detector.craft_utils as craft_utils_module
+
+# # CRITICAL FIX: Added 'ratio_net=2' to the signature and calculation.
+# # Without this, your boxes are 2x smaller than they should be.
+# def fixed_adjustResultCoordinates(polys, ratio_w, ratio_h, ratio_net=2):
+#     if not polys:
+#         return []
+    
+#     adjusted = []
+#     for poly in polys:
+#         if poly is None or len(poly) == 0:
+#             continue
+        
+#         # Convert to numpy array safely
+#         poly = np.array(poly).reshape(-1, 2)
+        
+#         # Scale coordinates: 
+#         # 1. ratio_net scales from Heatmap -> Resized Image
+#         # 2. ratio_w/h scales from Resized Image -> Original Image
+#         poly[:, 0] *= (ratio_w * ratio_net)
+#         poly[:, 1] *= (ratio_h * ratio_net)
+        
+#         adjusted.append(poly)
+    
+#     return adjusted
+
+# # Apply the patch
+# craft_utils_module.adjustResultCoordinates = fixed_adjustResultCoordinates
+# # ==========================================
+
+# import gradio as gr
+# from craft_hw_ocr import OCR
+
+# print("⏳ Loading OCR models...")
+# ocr = OCR.load_models()
+# print("✅ Models loaded!")
+
+# def do_ocr(inp):
+#     if inp is None:
+#         return None, "No image uploaded."
+    
+#     try:
+#         # Detection
+#         # 'detected_img' is usually just the input image in this library
+#         detected_img, results = OCR.detection(inp, ocr[2])
+        
+#         # Visualization
+#         # Now that coordinates are scaled correctly, this should look right
+#         viz_img = OCR.visualize(inp, results)
+        
+#         # Recognition
+#         try:
+#             # Note: The library has a typo 'recoginition' (extra 'i')
+#             bboxes, text = OCR.recoginition(detected_img, results, ocr[0], ocr[1])
+#         except Exception as e:
+#             print(f"Recognition error: {e}")
+#             text = f"Detection successful, but recognition failed: {str(e)}"
+        
+#         return viz_img, text
+        
+#     except Exception as e:
+#         print(f"OCR error: {e}")
+#         return None, f"Error processing image: {str(e)}"
+  
+# inputs = gr.Image(label="Upload Image")
+# o1 = gr.Image(label="Detections")
+# o2 = gr.Textbox(label="Text")
+
+# title = "CRAFT-OCR (Fixed Coords)"
+# description = "Handwriting OCR using CRAFT + TrOCR. Patched for NumPy and Coordinates."
+
+# gr.Interface(
+#     fn=do_ocr, 
+#     inputs=inputs, 
+#     outputs=[o1, o2], 
+#     title=title, 
+#     description=description
+# ).launch()
+
+
+
+
+
+
+
+import os
+import sys
+import numpy as np
+import cv2
+
+# ==========================================
+# 🔧 PATCH 1: Fix Torchvision Compatibility
+# ==========================================
+import torchvision.models.vgg
+if not hasattr(torchvision.models.vgg, 'model_urls'):
+    torchvision.models.vgg.model_urls = {
+        'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth'
+    }
+
+# ==========================================
+# 🔧 PATCH 2: Fix NumPy Crash AND Coordinates
+# ==========================================
+import craft_text_detector.craft_utils as craft_utils_module
+
+def fixed_adjustResultCoordinates(polys, ratio_w, ratio_h, ratio_net=2):
+    if not polys:
+        return []
+    
+    adjusted = []
+    for poly in polys:
+        if poly is None or len(poly) == 0:
+            continue
+        
+        # Convert to numpy array safely
+        poly = np.array(poly).reshape(-1, 2)
+        
+        # Scale coordinates correctly
+        poly[:, 0] *= (ratio_w * ratio_net)
+        poly[:, 1] *= (ratio_h * ratio_net)
+        
+        adjusted.append(poly)
+    
+    return adjusted
+
+craft_utils_module.adjustResultCoordinates = fixed_adjustResultCoordinates
+# ==========================================
+
+import gradio as gr
+from craft_hw_ocr import OCR
+# Import the core prediction function to bypass the wrapper limitations
+from craft_text_detector.predict import get_prediction
+
+print("⏳ Loading OCR models...")
+ocr = OCR.load_models()
+# ocr[2] is the Craft object wrapper
+craft_wrapper = ocr[2]
+# We extract the actual networks to run them manually
+craft_net = craft_wrapper.craft_net
+refine_net = craft_wrapper.refine_net
+print("✅ Models loaded!")
+
+def do_ocr(inp, text_threshold, link_threshold, low_text):
+    if inp is None:
+        return None, "No image uploaded."
+    
+    try:
+        print(f"⚙️ Running Direct Inference: Text={text_threshold}, Link={link_threshold}, Low={low_text}")
+        
+        # 1. Direct Detection (Bypassing Craft.detect_text)
+        # This calls the engine directly, which accepts all our sliders.
+        prediction_result = get_prediction(
+            image=inp,
+            craft_net=craft_net,
+            refine_net=refine_net,
+            text_threshold=text_threshold,
+            link_threshold=link_threshold,
+            low_text=low_text,
+            cuda=False,  # Space is CPU
+            poly=True
+        )
+        
+        # 2. Visualization
+        # OCR.visualize expects (image, results_dict)
+        viz_img = OCR.visualize(inp, prediction_result)
+        
+        # 3. Recognition
+        try:
+            bboxes, text = OCR.recoginition(inp, prediction_result, ocr[0], ocr[1])
+        except Exception as e:
+            text = f"Detection successful, but recognition failed: {e}"
+        
+        return viz_img, text
+        
+    except Exception as e:
+        print(f"OCR error: {e}")
+        return None, f"Error processing image: {str(e)}"
+
+# ------------------------------------------------------
+# 🎛️ UI with Tuning Sliders
+# ------------------------------------------------------
+with gr.Blocks(title="CRAFT-OCR Tuner") as demo:
+    gr.Markdown("## 🔧 CRAFT-OCR Parameter Tuner")
+    gr.Markdown("Adjust sliders to fix issues like merged words or background noise.")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_img = gr.Image(label="Upload Image")
+            
+            gr.Markdown("### 🎛️ Fine-Tune Detection")
+            
+            text_thres = gr.Slider(0.1, 0.9, value=0.7, step=0.05, label="Text Threshold", 
+                                   info="Confidence to consider a pixel as text. Higher = Less Noise.")
+            
+            link_thres = gr.Slider(0.1, 0.9, value=0.4, step=0.05, label="Link Threshold", 
+                                   info="Confidence to link characters. HIGHER value splits merged words (Fixes 'Hamburgthen').")
+            
+            low_text = gr.Slider(0.1, 0.9, value=0.4, step=0.05, label="Low Text Threshold", 
+                                 info="Filters background noise. Higher = Cleaner background.")
+            
+            btn = gr.Button("Run OCR", variant="primary")
+        
+        with gr.Column(scale=1):
+            viz_output = gr.Image(label="Detections (Verify Boxes)")
+            text_output = gr.Textbox(label="Recognized Text", lines=10)
+
+    btn.click(
+        fn=do_ocr, 
+        inputs=[input_img, text_thres, link_thres, low_text], 
+        outputs=[viz_output, text_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements (1).txt

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+#craft-hw-ocr==1.1
+#gradio==3.43.1
+#torchvision==0.12.0
+#numpy==1.23.1
+#opencv-python==4.5.4.60
+
+
+
+craft-hw-ocr
+gradio>=3.43.1
+# Fixes "executable stack" error (Modern PyTorch)
+torch>=2.0.0
+torchvision>=0.15.0
+# Fixes "numpy.core.multiarray" crash (NumPy 1.x)
+numpy<2.0.0
+# Fixes "DictValue" crash (Stable Headless OpenCV)
+opencv-python-headless==4.8.0.74