feat: braiprediction logic

app/routes.py

@@ -6,13 +6,15 @@
 import cv2
 from PIL import Image
 import torch.nn.functional as F
+import os
+dirname = os.path.dirname(__file__)
 
 
 class BraitCnn(nn.Module):
     def __init__(self):
-        super(BraitCnn,self).__init__()
+        super(BraitCnn, self).__init__()
         self.brait1 = nn.Sequential(
-            nn.Conv2d(in_channels=3,out_channels=16,kernel_size=5,stride=1,padding=2),
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=5, stride=1, padding=2),
             nn.MaxPool2d(kernel_size=2),
             nn.Dropout(p=0.01)
         )
@@ -26,10 +28,50 @@ def __init__(self):
             nn.Linear(100, 26)
         )
 
+    def forward(self, x):
+        y = F.relu(self.brait1(x))
+        y = F.relu(self.brait2(y))
 
+        # flatten
+        y = y.view(-1, 32 * 7 * 7)
+        y = F.relu(self.brait3(y))
+
+        return y
+
+
+def BraitPrediction(img_path):
+    model = BraitCnn()
+    model_path = filename = os.path.join(dirname, 'ml-model/BRAIT_PYTORCH.pth')
+    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+    #prepocess image inputan convert jadi RGB
+    image = Image.open(img_path)
+    image = image.convert('RGB')
+
+    #segmentasi image inputan
+    width, height = image.size #mengambil ukuran size image
+    jumlah_segment = round(width/height/0.78) #menentukan jumlah segment huruf braille
+    print(jumlah_segment)
+    segment = width/jumlah_segment
+    print(segment)
+
+    tamp = []
+    for i in range(0, jumlah_segment):
+        cropped = image.crop((i * segment, 0, (i + 1) * segment, height))
+        cropped = np.array(cropped)
+        cropped = cv2.resize(cropped, (28, 28))
+        cropped = cropped.astype(np.float32) / 255.0
+        cropped = torch.from_numpy(cropped[None, :, :, :])
+        cropped = cropped.permute(0, 3, 1, 2)
+        predicted_tensor = model(cropped)
+        _, predicted_letter = torch.max(predicted_tensor, 1)
+        tamp.append(chr(97 + predicted_letter))
+
+    return tamp
 
 
 @app.route('/')
 @app.route('/index')
 def index():
-    return "Hello, World!"
+    filename = os.path.join(dirname, 'image/imageTest1.jpeg')
+
+    return BraitPrediction(filename)