Update app.py

app.py

@@ -4,115 +4,247 @@ import numpy as np
 import cv2
 from PIL import Image
 from transformers import CLIPProcessor, CLIPModel
-from paddleocr import PaddleOCR
+from paddleocr import PaddleOCR, TextDetection
 from functools import lru_cache
+from spaces import GPU
 
-# Use GPU if available
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# Load CLIP globally
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14").to(device)
+MODEL_HUB_ID = "imperiusrex/printedpaddle"
+# Setup
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
 clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
 
-# Language detection candidates
-candidates = [
-    "This is English text",
-    "This is Telugu text",
-    "This is Chinese text",
-    "This is Korean text"
-]
-
-lang_map = {
-    "english": "en",
-    "telugu": "te",
-    "chinese": "ch",
-    "korean": "korean"
-}
-
-@lru_cache(maxsize=4)
-def get_ocr_model(lang_code):
-    return PaddleOCR(
-        use_doc_orientation_classify=False,
-        use_doc_unwarping=False,
-        use_textline_orientation=False,
-        lang=lang_code,
-        use_angle_cls=False,
-        use_gpu=torch.cuda.is_available()
-    )
-
-def get_box_center(box):
-    x_coords = [p[0] for p in box]
-    y_coords = [p[1] for p in box]
-    return sum(x_coords) / 4, sum(y_coords) / 4
+# Set device to CPU
+device = "cpu"
+clip_model.to(device)
 
+@GPU
 def process_image(img_path):
-    image = cv2.imread(img_path)
-    ocr = get_ocr_model("en")
-    result = ocr.ocr(image, cls=False)
-
-    if not result or not result[0]:
-        return "❌ No text detected."
+    """
+    Processes an image to detect, crop, and OCR text, returning it in reading order.
+    Args:
+        img_path: The path to the image file.
+    Returns:
+        A string containing the reconstructed text.
+    """
+    # Load CLIP model and processor
+    clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+    processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+    # Candidate language phrases for detection
+    candidates = [
+        "This is English text",
+        # "This is Hindi text",
+        # "This is Tamil text",
+        "This is Telugu text",
+        # "This is Bengali text",
+        # "This is Arabic text",
+        "This is Chinese text",
+        # "This is Japanese text",
+        "This is Korean text",
+        # "This is Russian text",
+        # "This is Kannada text",
+        # "This is Malayalam text",
+        # "This is Marathi text",
+        # "This is Urdu text",
+        # "This is French text",
+        # "This is Spanish text",
+        # "This is Italian text",
+        # "This is Portuguese text",
+        # "This is Romanian text",
+        # "This is Hungarian text",
+        # "This is Indonesian text",
+        # "This is Lithuanian text",
+        # "This is Chinese Traditional text",
+        # "This is Malay text",
+        # "This is Dutch text",
+        # "This is Norwegian text",
+        # "This is Bosnian text",
+        # "This is Polish text",
+        # "This is Czech text",
+        # "This is Slovak text",
+        # "This is Welsh text",
+        # "This is Slovenian text",
+        # "This is Danish text",
+        # "This is Albanian text",
+        # "This is Estonian text",
+        # "This is Swedish text",
+        # "This is Irish text",
+        # "This is Swahili text",
+        # "This is Croatian text",
+        # "This is Uzbek text",
+        # "This is Turkish text",
+        # "This is Latin text",
+        # "This is Belarusian text",
+        # "This is Ukrainian text"
+    ]
 
-    boxes = result[0]
+    # Map detected languages to PaddleOCR language codes
+    lang_map = {
+        "english": "en",
+        # "hindi": "hi",
+        # "tamil": "ta",
+        "telugu": "te",
+        # "bengali": "bn",
+        # "arabic": "ar",
+        "chinese": "ch",
+        # "japanese": "japan",
+        "korean": "korean",
+        # "russian": "ru",
+        # "kannada": "kn",
+        # "malayalam": "ml",
+        # "marathi": "mr",
+        # "urdu": "ur",
+        # "french": "fr",
+        # "spanish": "es",
+        # "italian": "it",
+        # "portuguese": "pt",
+        # "romanian": "ro",
+        # "hungarian": "hu",
+        # "indonesian": "id",
+        # "lithuanian": "lt",
+        # "chinese traditional": "chinese_cht",
+        # "malay": "ms",
+        # "dutch": "nl",
+        # "norwegian": "no",
+        # "bosnian": "bs",
+        # "polish": "pl",
+        # "czech": "cs",
+        # "slovak": "sk",
+        # "welsh": "cy",
+        # "slovenian": "sl",
+        # "danish": "da",
+        # "albanian": "sq",
+        # "estonian": "et",
+        # "swedish": "sv",
+        # "irish": "ga",
+        # "swahili": "sw",
+        # "croatian": "hr",
+        # "uzbek": "uz",
+        # "turkish": "tr",
+        # "latin": "la",
+        # "belarusian": "be",
+        # "ukrainian": "uk"
+    }
+
+    # Text Detection
+    arr = []
+    model_det = TextDetection(model_name="PP-OCRv5_server_det")
+    output = model_det.predict(img_path, batch_size=1)
+    for res in output:
+        polys = res['dt_polys']
+        if polys is not None:
+            arr.extend(polys.tolist())
+    arr = sorted(arr, key=lambda box: (box[0][1], box[0][0]))
+
+    # Image Cropping and Warping
+    img = cv2.imread(img_path)
     cropped_images = []
-    centers = []
-    for line in boxes:
-        box, (_, _) = line
-        box = np.array(box).astype(np.float32)
-        x_min, y_min = box[:, 0].min(), box[:, 1].min()
-        x_max, y_max = box[:, 0].max(), box[:, 1].max()
-        warped = image[int(y_min):int(y_max), int(x_min):int(x_max)]
+    for i, box in enumerate(arr):
+        box = np.array(box, dtype=np.float32)
+        width_a = np.linalg.norm(box[0] - box[1])
+        width_b = np.linalg.norm(box[2] - box[3])
+        height_a = np.linalg.norm(box[0] - box[3])
+        height_b = np.linalg.norm(box[1] - box[2])
+        width = int(max(width_a, width_b))
+        height = int(max(height_a, height_b))
+        dst_rect = np.array([[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]], dtype=np.float32)
+        M = cv2.getPerspectiveTransform(box, dst_rect)
+        warped = cv2.warpPerspective(img, M, (width, height))
         cropped_images.append(warped)
-        centers.append(get_box_center(box))
 
+    # Perform language detection for each cropped image and then OCR
     predicted_texts = []
-    for crop in cropped_images:
-        inputs = clip_processor(text=candidates, images=Image.fromarray(crop), return_tensors="pt", padding=True).to(device)
+    for i, cropped_img in enumerate(cropped_images):
+        # Get probabilities
+        inputs = processor(text=candidates, images=cropped_img, return_tensors="pt", padding=True)
         with torch.no_grad():
-            probs = clip_model(**inputs).logits_per_image.softmax(dim=1)
-        lang = candidates[probs.argmax()].split()[-2].lower()
-        lang_code = lang_map.get(lang, "en")
-
-        ocr = get_ocr_model(lang_code)
-        ocr_result = ocr.ocr(crop, cls=False)
-
-        text = ""
-        if ocr_result and ocr_result[0]:
-            text = " ".join([line[1][0] for line in ocr_result[0]])
-        predicted_texts.append(text)
-
-    # Reconstruct text based on y then x sorting
-    text_blocks = [
-        {"text": text, "center": center}
-        for text, center in zip(predicted_texts, centers) if text.strip()
-    ]
-    text_blocks.sort(key=lambda b: (b["center"][1], b["center"][0]))
-
-    lines = []
-    line = []
-    last_y = None
-    for block in text_blocks:
-        y = block["center"][1]
-        if last_y is None or abs(y - last_y) < 40:
-            line.append(block)
-        else:
-            lines.append(line)
-            line = [block]
-        last_y = y
-    if line:
-        lines.append(line)
-
-    final_text = "\n".join(" ".join([b["text"] for b in sorted(l, key=lambda b: b["center"][0])]) for l in lines)
-    return final_text or "❌ No text reconstructed."
-
-# Gradio app
+            logits_per_image = clip_model(**inputs).logits_per_image
+            probs = logits_per_image.softmax(dim=1)
+
+        # Get best language match
+        best = probs.argmax().item()
+        detected_lang_phrase = candidates[best]
+        detected_lang = detected_lang_phrase.split()[-2].lower()
+        lang_code = lang_map.get(detected_lang, "en")
+
+        # Perform OCR for the current cropped image with the detected language
+        ocr = PaddleOCR(
+            use_doc_orientation_classify=False,
+            use_doc_unwarping=False,
+            use_textline_orientation=False,
+            lang=lang_code,
+            device="cpu"
+        )
+
+        result = ocr.predict(cropped_img)
+
+        text_for_this_image = ""
+        if result and result[0] and 'rec_texts' in result[0]:
+             text_for_this_image = " ".join(result[0]['rec_texts'])
+
+        predicted_texts.append(text_for_this_image)
+
+
+    def get_box_center(box):
+      """Calculates the center of a bounding box."""
+      x_coords = [p[0] for p in box]
+      y_coords = [p[1] for p in box]
+      center_x = sum(x_coords) / len(x_coords)
+      center_y = sum(y_coords) / len(y_coords)
+      return center_x, center_y
+
+    # --- Step 1: Read all text and their centroid coordinates ---
+    all_text_blocks = []
+    for i, box in enumerate(arr):
+        # Use the predicted text from the list
+        text = predicted_texts[i]
+        if text: # Only add if text is not empty
+            center_x, center_y = get_box_center(box)
+            all_text_blocks.append({
+                "text": text,
+                "center_x": center_x,
+                "center_y": center_y
+            })
+
+
+    # --- Step 2: Sort by y-coordinate, then by x-coordinate, and group into lines ---
+    reconstructed_text = ""
+    if all_text_blocks:
+        # Sort by center_y, then by center_x
+        sorted_blocks = sorted(all_text_blocks, key=lambda item: (item["center_y"], item["center_x"]))
+
+        lines = []
+        if sorted_blocks:
+            current_line = [sorted_blocks[0]]
+            for block in sorted_blocks[1:]:
+                # Check if the vertical centers are close enough to be on the same line
+                if abs(block["center_y"] - current_line[-1]["center_y"]) < 40: # Y-threshold
+                    current_line.append(block)
+                else:
+                    # Sort the current line by x-coordinate and add it to the lines list
+                    current_line.sort(key=lambda item: item["center_x"])
+                    lines.append(" ".join([item["text"] for item in current_line]))
+                    current_line = [block]
+
+            # Add the last line
+            if current_line:
+                current_line.sort(key=lambda item: item["center_x"])
+                lines.append(" ".join([item["text"] for item in current_line]))
+
+        # --- Step 3: Join the lines into a single string ---
+        reconstructed_text = "\n".join(lines)
+
+    return reconstructed_text
+
 iface = gr.Interface(
     fn=process_image,
-    inputs=gr.Image(type="filepath", label="Upload Image"),
-    outputs=gr.Text(label="Extracted Text"),
-    title="Multilingual OCR + Text Reconstruction",
-    description="Uses PaddleOCR + CLIP to detect language, perform OCR, and reconstruct reading order text using GPU."
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.Text(),
+    title="Image OCR and Text Reconstruction",
+    description="Upload an image to perform text detection, cropping, language detection, OCR, and reconstruct the text in reading order."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+if __name__== "__main__":
+    iface.launch(debug=True)