Testing things

.gitignore

@@ -9,4 +9,6 @@ venv/
 .vscode/c_cpp_properties.json
 .vscode/launch.json
 .vscode/ipch
-.DS_Store
+.DS_Store
+
+input/cleanDataset

.vscode/settings.json

@@ -1,5 +1,7 @@
 {
     "files.associations": {
         "vector": "cpp"
-    }
+    },
+    "python.analysis.typeCheckingMode": "basic",
+    "python.analysis.autoImportCompletions": false
 }

ai/predict.py

@@ -1,20 +1,18 @@
 import sys
 import os
 import numpy as np
-from tensorflow.keras.preprocessing import image
+import cv2
+# from tensorflow.keras.preprocessing import image
 from tensorflow.keras.models import load_model
 
 class_labels = ["cardboard", "glass", "metal", "paper", "plastic", "trash"]
 
 
 def get_class(img_path: str, model) -> str:
-    img = image.load_img(img_path, target_size=(512, 384))
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array /= 255.0  # Normalisation des pixels
+    img = cv2.imread(img_path)
+    img = cv2.resize(img, (256, 256))
 
-    # Prédiction de la classe
-    prediction = model.predict(img_array, verbose=0)
+    prediction = model.predict(np.expand_dims(img, axis=0), verbose=0)
     predicted_class = class_labels[np.argmax(prediction)]
     if predicted_class in ["plastic", "cardboard", "paper"]:
         return "recyclable"
@@ -30,8 +28,14 @@ def main():
         print("No such image found on the arguments list")
         return -1
 
+<<<<<<< HEAD
+    model = load_model(os.path.dirname(
+        os.path.abspath(__file__)) + "/alex.keras",
+        compile=False
+=======
     model = load_model(
         os.path.dirname(os.path.abspath(__file__)) + "/trash_detection_model.keras"
+>>>>>>> develop
     )
     print(get_class(sys.argv[1], model))
     return 0

ai/requirements.txt

@@ -1,3 +1,5 @@
 numpy
-tensorflow
-pillow
+tensorflow==2.15.1
+pillow
+opencv-python
+matplotlib

mobile/ios/.xcode.env.local

@@ -0,0 +1,2 @@
+export NODE_BINARY=/Users/alexandrebedel/.nvm/versions/node/v20.9.0/bin/node
+