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app.py

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+import os
+import io
+import base64
+import cv2
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from fastapi import FastAPI, Security, HTTPException, status
+from fastapi.security.api_key import APIKeyHeader
+from pydantic import BaseModel
+
+# ─────────────────────────────────────────
+# 1. DEFINISI CUSTOM LAYER CORNERSTONES
+# ─────────────────────────────────────────
+class ChannelAttention(layers.Layer):
+    def __init__(self, reduction_ratio=8, **kwargs):
+        super().__init__(**kwargs)
+        self.reduction_ratio = reduction_ratio
+
+    def build(self, input_shape):
+        channels = input_shape[-1]
+        self.gap = layers.GlobalAveragePooling2D()
+        self.fc1 = layers.Dense(max(1, channels // self.reduction_ratio), activation="relu")
+        self.fc2 = layers.Dense(channels, activation="sigmoid")
+        self.reshape = layers.Reshape((1, 1, channels))
+
+    def call(self, x):
+        scale = self.gap(x)
+        scale = self.fc1(scale)
+        scale = self.fc2(scale)
+        scale = self.reshape(scale)
+        return x * scale
+
+    def get_config(self):
+        cfg = super().get_config()
+        cfg.update({"reduction_ratio": self.reduction_ratio})
+        return cfg
+
+class LabelSmoothingCrossEntropy(keras.losses.Loss):
+    def __init__(self, smoothing=0.1, **kwargs):
+        super().__init__(**kwargs)
+        self.smoothing = smoothing
+
+    def call(self, y_true, y_pred):
+        num_classes = tf.cast(tf.shape(y_pred)[-1], tf.float32)
+        y_true = tf.cast(y_true, tf.float32)
+        if len(y_true.shape) == 1 or (len(y_true.shape) == 2 and y_true.shape[-1] == 1):
+            y_true = tf.one_hot(tf.cast(tf.reshape(y_true, [-1]), tf.int32), tf.cast(num_classes, tf.int32))
+        smooth_labels = y_true * (1.0 - self.smoothing) + self.smoothing / num_classes
+        log_probs = tf.nn.log_softmax(y_pred, axis=-1)
+        loss = -tf.reduce_sum(smooth_labels * log_probs, axis=-1)
+        return tf.reduce_mean(loss)
+
+    def get_config(self):
+        cfg = super().get_config()
+        cfg.update({"smoothing": self.smoothing})
+        return cfg
+
+def mish(x):
+    return x * tf.math.tanh(tf.math.softplus(x))
+
+# Map index EMNIST Anda ke Karakter Nyata (Sesuaikan urutan ini dengan label training Anda!)
+EMNIST_MAPPING = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" 
+
+# ─────────────────────────────────────────
+# 2. INITIALIZE FASTAPI & LOAD MODEL
+# ─────────────────────────────────────────
+app = FastAPI(title="EMNIST Complete Grid Sheet OCR API")
+
+API_KEY_NAME = "X-API-Key"
+api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)
+API_KEY = os.environ.get("MY_SECRET_API_KEY", "KunciRahasia123!")
+
+model = keras.models.load_model(
+    "best_model.keras",
+    custom_objects={
+        "ChannelAttention": ChannelAttention,
+        "LabelSmoothingCrossEntropy": LabelSmoothingCrossEntropy,
+        "mish": mish
+    }
+)
+
+async def get_api_key(api_key_header: str = Security(api_key_header)):
+    if api_key_header == API_KEY: return api_key_header
+    raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail="Invalid API Key")
+
+# Skema Request Input untuk menerima gambar Base64 dari Web Frontend
+class ImageInput(BaseModel):
+    image_base64: str
+
+@app.get("/")
+def home():
+    return {"status": "OCR Engine Active"}
+
+# ─────────────────────────────────────────
+# 3. ENDPOINT INFERENCE DENGAN LOGIKA GRID
+# ─────────────────────────────────────────
+@app.post("/predict-sheet", dependencies=[Security(get_api_key)])
+def predict_sheet(payload: ImageInput):
+    try:
+        # Decode gambar base64 yang dikirim frontend ke format OpenCV Mat
+        encoded_data = payload.image_base64.split(",")[-1]
+        nparr = np.frombuffer(base64.b64decode(encoded_data), np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        
+        if img is None:
+            raise HTTPException(status_code=400, detail="Format gambar tidak valid.")
+
+        # Preprocessing ke Grayscale dan Thresholding biner
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
+
+        # --- LOGIKA SEPARASI GRID ANDA ---
+        scale = 28 
+        horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (scale, 1))
+        vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, scale))
+        detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
+        detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
+
+        clean_grid_mask = cv2.addWeighted(detect_horizontal, 0.5, detect_vertical, 0.5, 0)
+        clean_grid_mask = cv2.threshold(clean_grid_mask, 10, 255, cv2.THRESH_BINARY)[1]
+
+        contours, _ = cv2.findContours(clean_grid_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        valid_boxes = []
+        for contour in contours:
+            x, y, w, h = cv2.boundingRect(contour)
+            if 20 < w < 200 and 20 < h < 200:
+                valid_boxes.append((x, y, w, h))
+
+        if not valid_boxes:
+            return {"status": "success", "text": ""}
+
+        # Sort & Grouping kotak ke baris-baris (Row-by-Row)
+        valid_boxes = sorted(valid_boxes, key=lambda b: b[1])
+        rows = []
+        current_row = []
+        prev_y = valid_boxes[0][1]
+        cell_height_threshold = 20  
+
+        for box in valid_boxes:
+            if abs(box[1] - prev_y) > cell_height_threshold:
+                rows.append(sorted(current_row, key=lambda b: b[0]))
+                current_row = [box]
+                prev_y = box[1]
+            else:
+                current_row.append(box)
+        if current_row:
+            rows.append(sorted(current_row, key=lambda b: b[0]))
+
+        # --- PROSES EVALUASI KOTAK BLANK VS MODEL PREDICT ---
+        extracted_text = []
+
+        for row in rows:
+            space_allocated = False
+            has_content_yet = False
+            row_text = ""
+            
+            for x, y, w, h in row:
+                padding = 5
+                cropped_thresh_cell = thresh[y+padding : y+h-padding, x+padding : x+w-padding]
+                
+                # Proteksi ukuran jika cropping di tepi gambar bermasalah
+                if cropped_thresh_cell.size == 0:
+                    continue
+                    
+                text_pixel_count = cv2.countNonZero(cropped_thresh_cell)
+                total_pixels = cropped_thresh_cell.shape[0] * cropped_thresh_cell.shape[1]
+                text_ratio = (text_pixel_count / total_pixels) * 100
+                
+                is_blank = text_ratio <= 1.5
+                
+                if not is_blank:
+                    # ADA ISI -> Resize kotak ke (28, 28) untuk dimasukkan ke model Keras
+                    cell_resized = cv2.resize(cropped_thresh_cell, (28, 28), interpolation=cv2.INTER_AREA)
+                    
+                    # Normalisasi data pixel 0.0 - 1.0
+                    cell_input = cell_resized.astype(np.float32) / 255.0
+                    cell_input = np.expand_dims(cell_input, axis=(0, -1)) # Shape menjadi (1, 28, 28, 1)
+                    
+                    # Prediksi menggunakan model
+                    logits = model.predict(cell_input, verbose=0)
+                    pred_idx = np.argmax(logits, axis=-1)[0]
+                    
+                    # Terjemahkan index angka ke karakter asli string
+                    char_result = EMNIST_MAPPING[pred_idx] if pred_idx < len(EMNIST_MAPPING) else "?"
+                    row_text += char_result
+                    
+                    has_content_yet = True
+                    space_allocated = False 
+                else:
+                    # KOTAK BLANK -> Simpan spasi hanya untuk space pertama pasca konten teks
+                    if has_content_yet and not space_allocated:
+                        row_text += " "  # <-- DI SINI JADI SPASI
+                        space_allocated = True
+            
+            if row_text.strip():  # Masukkan baris jika tidak sepenuhnya kosong
+                extracted_text.append(row_text.rstrip())
+
+        # Gabungkan semua baris teks dengan pemisah baris baru (\n)
+        final_output_string = "\n".join(extracted_text)
+
+        return {
+            "status": "success",
+            "result_text": final_output_string
+        }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Gagal memproses kertas grid: {str(e)}")

events.out.tfevents.1780063842.1a82ff0eb631.57.0.v2

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events.out.tfevents.1780063881.1a82ff0eb631.57.1.v2

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requirement.txt

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+fastapi==0.111.0
+uvicorn==0.30.1
+tensorflow-cpu==2.16.1
+numpy==1.26.4
+scikit-learn==1.5.0