Update inference.py

inference.py

@@ -1,74 +1,137 @@
-import torch
-from PIL import Image
-import torchvision.transforms.functional as TF
-import cv2
-import matplotlib.pyplot as plt
-from huggingface_hub import hf_hub_download
-
-
-# --- Load model from HF ---
-model_path = hf_hub_download(repo_id="farbodpya/Persian-OCR", filename="pytorch_model.bin")
-model = CNN_Transformer_OCR(num_classes=len(idx_to_char)+1).to(device)
-model.load_state_dict(torch.load(model_path, map_location=device))
-model.eval()
-
-# --- Define transforms ---
-class OCRTestTransform:
-    def __init__(self, img_height=64, max_width=1600):
-        self.img_height = img_height
-        self.max_width = max_width
-    def __call__(self, img):
-        img = img.convert("L")
-        w, h = img.size
-        new_w = int(w * self.img_height / h)
-        img = img.resize((min(new_w, self.max_width), self.img_height), Image.BICUBIC)
-        new_img = Image.new("L", (self.max_width, self.img_height), 255)
-        new_img.paste(img, (0, 0))
-        img = TF.to_tensor(new_img)
-        img = TF.normalize(img, (0.5,), (0.5,))
-        return img
-
-transform_test = OCRTestTransform()
-
-# --- Line segmentation ---
-def segment_lines_precise(image_path, min_line_height=12, margin=6, visualize=False):
-    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
-    _, binary = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
-    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (img.shape[1]//30, 1))
-    morphed = cv2.dilate(binary, kernel, iterations=1)
-    contours, _ = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[1])
-    lines = []
-    for ctr in contours:
-        x, y, w, h = cv2.boundingRect(ctr)
-        if h < min_line_height: continue
-        y1 = max(0, y - margin)
-        y2 = min(img.shape[0], y + h + margin)
-        line_img = img[y1:y2, x:x+w]
-        lines.append(Image.fromarray(line_img))
-    if visualize:
-        for i, line_img in enumerate(lines):
-            plt.figure(figsize=(12,2))
-            plt.imshow(line_img, cmap='gray')
-            plt.axis('off')
-            plt.title(f"Line {i+1}")
-            plt.show()
-    return lines
-
-# --- OCR function ---
-def ocr_page(image_path, visualize=False):
-    lines = segment_lines_precise(image_path, visualize=visualize)
-    all_texts = []
-    for idx, line_img in enumerate(lines, 1):
-        img_tensor = transform_test(line_img).unsqueeze(0).to(device)
-        with torch.no_grad():
-            outputs = model(img_tensor)
-        pred_text = greedy_decode(outputs, idx_to_char)[0]
-        all_texts.append(pred_text)
-        print(f"Line {idx}: {pred_text}")
-    return "\n".join(all_texts)
-
-# --- Example ---
-img_path = "example.png"  
-final_text = ocr_page(img_path, visualize=True)
-print("\n=== Final OCR Page ===\n", final_text)
+import torch
+from PIL import Image
+import torchvision.transforms.functional as TF
+import cv2
+import matplotlib.pyplot as plt
+from huggingface_hub import hf_hub_download
+import torch.nn as nn
+
+# -----------------------------
+# Device
+# -----------------------------
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# -----------------------------
+# Dummy idx_to_char
+# -----------------------------
+
+import json
+vocab_path = hf_hub_download(repo_id="farbodpya/Persian-OCR", filename="vocab.json")
+with open(vocab_path, "r", encoding="utf-8") as f:
+    vocab = json.load(f)
+char_to_idx = vocab["char_to_idx"]
+idx_to_char = {int(k): v for k, v in vocab["idx_to_char"].items()}
+
+# -----------------------------
+# Model
+# -----------------------------
+class CNN_Transformer_OCR(nn.Module):
+    # این کلاس همان مدل خودت است
+    def __init__(self, num_classes, d_model=1280, nhead=16, num_layers=8, dropout=0.2):
+        super().__init__()
+        # برای کوتاهی، همان CNN+Transformer خودت
+        self.cnn = nn.Sequential(
+            nn.Conv2d(1, 32, 3, 1, 1), nn.ReLU(), nn.MaxPool2d(2,2),
+            nn.Conv2d(32, 64, 3, 1, 1), nn.ReLU(), nn.MaxPool2d(2,2)
+        )
+        self.proj = nn.Linear(64*16, d_model)
+        encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, batch_first=True, dropout=dropout)
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.fc = nn.Linear(d_model, num_classes)
+    def forward(self, x):
+        f = self.cnn(x)
+        B, C, H, W = f.size()
+        f = f.permute(0,3,1,2).reshape(B,W,C*H)
+        out = self.transformer(self.proj(f))
+        return self.fc(out).log_softmax(2)
+
+# Load model weights
+model_path = hf_hub_download(repo_id="farbodpya/Persian-OCR", filename="pytorch_model.bin")
+model = CNN_Transformer_OCR(num_classes=len(idx_to_char)+1).to(device)
+model.load_state_dict(torch.load(model_path, map_location=device))
+model.eval()
+
+# -----------------------------
+# Decode function
+# -----------------------------
+def greedy_decode(output, idx_to_char):
+    output = output.argmax(2)
+    texts = []
+    for seq in output:
+        prev = -1
+        chars = []
+        for idx in seq.cpu().numpy():
+            if idx != prev and idx != 0:
+                chars.append(idx_to_char.get(idx,""))
+            prev = idx
+        texts.append("".join(chars))
+    return texts
+
+# -----------------------------
+# Transform
+# -----------------------------
+class OCRTestTransform:
+    def __init__(self, img_height=64, max_width=1600):
+        self.img_height = img_height
+        self.max_width = max_width
+    def __call__(self, img):
+        img = img.convert("L")
+        w, h = img.size
+        new_w = int(w * self.img_height / h)
+        img = img.resize((min(new_w,self.max_width), self.img_height), Image.BICUBIC)
+        new_img = Image.new("L", (self.max_width, self.img_height), 255)
+        new_img.paste(img, (0,0))
+        img = TF.to_tensor(new_img)
+        img = TF.normalize(img, (0.5,), (0.5,))
+        return img
+
+transform_test = OCRTestTransform()
+
+# -----------------------------
+# Line segmentation
+# -----------------------------
+def segment_lines_precise(image_path, min_line_height=12, margin=6, visualize=False):
+    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+    _, binary = cv2.threshold(img,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(img.shape[1]//30,1))
+    morphed = cv2.dilate(binary,kernel,iterations=1)
+    contours, _ = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[1])
+    lines = []
+    for ctr in contours:
+        x, y, w, h = cv2.boundingRect(ctr)
+        if h < min_line_height: continue
+        y1 = max(0, y-margin)
+        y2 = min(img.shape[0], y+h+margin)
+        line_img = img[y1:y2, x:x+w]
+        lines.append(Image.fromarray(line_img))
+    if visualize:
+        for i, line_img in enumerate(lines):
+            plt.figure(figsize=(12,2))
+            plt.imshow(line_img, cmap='gray')
+            plt.axis('off')
+            plt.title(f"Line {i+1}")
+            plt.show()
+    return lines
+
+# -----------------------------
+# OCR page function
+# -----------------------------
+def ocr_page(image_path, visualize=False):
+    lines = segment_lines_precise(image_path, visualize=visualize)
+    all_texts = []
+    for idx, line_img in enumerate(lines,1):
+        img_tensor = transform_test(line_img).unsqueeze(0).to(device)
+        with torch.no_grad():
+            outputs = model(img_tensor)
+        pred_text = greedy_decode(outputs, idx_to_char)[0]
+        all_texts.append(pred_text)
+        print(f"Line {idx}: {pred_text}")
+    return "\n".join(all_texts)
+
+# -----------------------------
+# Example usage
+# -----------------------------
+img_path = "example.png"   
+final_text = ocr_page(img_path, visualize=True)
+print("\n=== Final OCR Page ===\n", final_text)