Upload 6 files

app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import os
+import cv2
+from streamlit_webrtc import VideoTransformerBase, webrtc_streamer
+
+# Model dosyasının yolu
+model_path = 'german.h5'
+
+# Modeli yükleme
+if os.path.isfile(model_path):
+    try:
+        model = load_model(model_path)
+        st.write("Model başarıyla yüklendi.")
+    except Exception as e:
+        st.error(f"Model yüklenirken bir hata oluştu: {e}")
+else:
+    st.error(f"Model dosyası bulunamadı: {model_path}")
+
+def process_img(img):
+    img = img.convert('RGB')  # RGBA'dan RGB'ye dönüştürme
+    img = img.resize((32, 32), Image.LANCZOS)  # 32x32 piksel boyutuna dönüştürme
+    img = np.array(img) / 255.0  # Normalize etme
+    img = np.expand_dims(img, axis=0)  # Resme boyut ekleme
+    return img
+
+class VideoTransformer(VideoTransformerBase):
+    def __init__(self):
+        self.model = model
+        self.latest_frame = None
+
+    def transform(self, frame):
+        img = frame.to_ndarray(format="bgr24")
+        self.latest_frame = img
+        return img
+
+    def get_latest_frame(self):
+        if self.latest_frame is not None:
+            return Image.fromarray(cv2.cvtColor(self.latest_frame, cv2.COLOR_BGR2RGB))
+        return None
+
+st.title("Almanya Trafik İşaretleri Sınıflandırması / German Traffic Sign Classification :traffic_light:")
+st.write('Kamera kullanarak modelimizi trafik işaretinizi sınıflandırsın.\nUse the camera to predict your traffic sign.')
+
+# WebRTC video akışını başlatma
+video_transformer = webrtc_streamer(key="example", video_transformer_factory=VideoTransformer)
+
+# Fotoğraf çekme butonu
+if st.button("Fotoğraf Çek"):
+    if video_transformer.video_transformer:
+        frame = video_transformer.video_transformer.get_latest_frame()
+        if frame is not None:
+            st.image(frame, caption="Çekilen Resim", use_column_width=True)
+            img = process_img(frame)
+            prediction = model.predict(img)
+            prediction_class = np.argmax(prediction)
+            
+            # Sınıf isimleri
+            classes = {
+                0: 'Speed limit (20km/h)', 1: 'Speed limit (30km/h)', 2: 'Speed limit (50km/h)', 3: 'Speed limit (60km/h)',
+                4: 'Speed limit (70km/h)', 5: 'Speed limit (80km/h)', 6: 'End of speed limit (80km/h)', 7: 'Speed limit (100km/h)',
+                8: 'Speed limit (120km/h)', 9: 'No passing', 10: 'No passing veh over 3.5 tons', 11: 'Right-of-way at intersection',
+                12: 'Priority road', 13: 'Yield', 14: 'Stop', 15: 'No vehicles', 16: 'Veh > 3.5 tons prohibited', 17: 'No entry',
+                18: 'General caution', 19: 'Dangerous curve left', 20: 'Dangerous curve right', 21: 'Double curve', 22: 'Bumpy road',
+                23: 'Slippery road', 24: 'Road narrows on the right', 25: 'Road work', 26: 'Traffic signals', 27: 'Pedestrians',
+                28: 'Children crossing', 29: 'Bicycles crossing', 30: 'Beware of ice/snow', 31: 'Wild animals crossing',
+                32: 'End speed + passing limits', 33: 'Turn right ahead', 34: 'Turn left ahead', 35: 'Ahead only',
+                36: 'Go straight or right', 37: 'Go straight or left', 38: 'Keep right', 39: 'Keep left', 40: 'Roundabout mandatory',
+                41: 'End of no passing', 42: 'End no passing veh > 3.5 tons'
+            }
+
+            st.write("Sonuç: ", classes[prediction_class])
+        else:
+            st.error("Kamera görüntüsü alınamadı.")
+    else:
+        st.error("Kamera başlatılmadı.")

german.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e37bb275f32787b984ef56cc6fa209c3e4a4960907fbfdaa8dd9650555736cfa
+size 2232516

requirements.txt

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+tensorflow
+streamlit