diff --git a/Func/ClassifierModule.py b/Func/ClassifierModule.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b784d87b0788d0c1f7bdc2a1ac753fff5c08d68
--- /dev/null
+++ b/Func/ClassifierModule.py
@@ -0,0 +1,66 @@
+import keras
+import numpy as np
+import cv2
+
+
+class Classifier:
+
+ def __init__(self, model_path, label_path):
+ self.model_path = model_path
+
+ np.set_printoptions(suppress=True)
+
+ self.model = keras.models.load_model(self.model_path)
+ self.data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+ self.labels_path = label_path
+
+ if self.labels_path:
+ with open(self.labels_path, "r") as label_file:
+ self.list_labels = [line.strip() for line in label_file]
+ else:
+ print("No Labels Found")
+
+ def getPrediction(self, img, draw=True, pos=(50, 50), scale=2, color=(0, 255, 0)):
+ img_resized = cv2.resize(img, (224, 224))
+ image_array = np.asarray(img_resized)
+ normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+
+ self.data[0] = normalized_image_array
+
+ prediction = self.model.predict(self.data)
+ index_val = np.argmax(prediction)
+
+ if draw and self.labels_path:
+ label_text = str(self.list_labels[index_val])
+ cv2.putText(img, label_text, pos, cv2.FONT_HERSHEY_COMPLEX, scale, color, 2)
+
+ return list(prediction[0]), index_val
+
+
+def main():
+ cap = cv2.VideoCapture(0)
+
+ mask_classifier = Classifier('Model/keras_model.h5', 'Model/labels.txt')
+
+ while True:
+ ret, frame = cap.read()
+
+ if not ret:
+ print("Failed to capture frame")
+ break
+
+ prediction = mask_classifier.getPrediction(frame)
+
+ print("Prediction:", prediction)
+
+ cv2.imshow("Image", frame)
+
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+
+ cap.release()
+ cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/Func/CloseProgram.py b/Func/CloseProgram.py
index 3109b6e62a86acfbc5006bcd528acac050df6d5c..c0718008506f92b145b947c79237c71e001569ce 100644
--- a/Func/CloseProgram.py
+++ b/Func/CloseProgram.py
@@ -5,5 +5,3 @@ def Close(cap):
cap.release()
cv2.destroyAllWindows()
print('closing')
-
-
diff --git a/Func/getSubFolders.py b/Func/getSubFolders.py
index 217d43f512c4ea586a2c48bc63d5f1a77551e810..a2dabc591b56c183ab80df6f1df2f0e2c3e44443 100644
--- a/Func/getSubFolders.py
+++ b/Func/getSubFolders.py
@@ -9,4 +9,4 @@ def count_sub_folders(folder_path):
sub_folders = [f for f in os.listdir(folder_path) if os.path.isdir(os.path.join(folder_path, f))]
num_sub_folders = len(sub_folders)
- return num_sub_folders
\ No newline at end of file
+ return num_sub_folders
diff --git a/Model/keras_model.h5 b/Model/keras_model.h5
index 0169f5a80825b3b126671903fdf5fa711e782af3..cb2d0ba86927f0ce886987e6222fa846a1adb344 100644
Binary files a/Model/keras_model.h5 and b/Model/keras_model.h5 differ
diff --git a/Model/labels.txt b/Model/labels.txt
index ca427253d6f818ccda5af95a99305e035f85b407..5fbcfab644bb75991252a4d2f6ce35c41d649e4d 100644
--- a/Model/labels.txt
+++ b/Model/labels.txt
@@ -2,4 +2,5 @@
1 B
2 C
3 D
-4 E
\ No newline at end of file
+4 E
+5 F
\ No newline at end of file
diff --git a/main.py b/main.py
index 4df15358d2aac4a09d2c385e88f66bf55dd86edf..4a6a59adc32d640bb1ba9d0c6222dffa6ad68a5f 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,6 @@
-import cv2
import numpy as np
from cvzone.HandTrackingModule import HandDetector
-from cvzone.ClassificationModule import Classifier
+from Func.ClassifierModule import Classifier
from Func.CloseProgram import Close
from Func.Helpers import *
@@ -11,10 +10,11 @@ size = 300
capture = cv2.VideoCapture(0)
detector = HandDetector(maxHands=1)
-classifier = Classifier("Model/Model.h5", "Model/labels.txt")
-# classifier = Classifier("Model/keras_model.h5", "Model/labels.txt")
-letters = GetLabels("Model/labels.txt")
+model_path = "Model/keras_model.h5"
+label_path = "Model/labels.txt"
+classifier = Classifier(model_path, label_path)
+letters = GetLabels(label_path)
if not capture.isOpened():
print("Failed to open video capture.")
@@ -47,8 +47,8 @@ while True:
CalculateHeight(size, h, w, imgCropped, imgFixed)
prediction, index = GetPrediction(classifier, imgFixed)
- print(prediction)
- print(index)
+ print('pred', prediction)
+ print('index', index)
cv2.putText(imgOutput, letters[index], (x, y), cv2.FONT_HERSHEY_TRIPLEX, 2, (255, 0, 255), 2)
diff --git a/trainModel.py b/trainModel.py
index 47a1da4ae475bf3331f5a1dc506ade22cc1e5014..2fad7ff56726e65fa07342266458dee1605e47f9 100644
--- a/trainModel.py
+++ b/trainModel.py
@@ -1,55 +1,67 @@
+import time
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from keras.losses import CategoricalCrossentropy
+from keras.src.optimizers import Adam
from Func.getSubFolders import count_sub_folders
-path = 'Data_test'
-output = 'Model/pruned.h5'
+path = 'Data'
+output = 'Model/keras_model.h5'
+start_time = time.time()
# Step 1: Load and Preprocess Images
-train_datagen = ImageDataGenerator(
+datagen = ImageDataGenerator(
rescale=1. / 255,
+ validation_split=0.2,
+ width_shift_range=0.2,
+ height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
- horizontal_flip=True
)
test_datagen = ImageDataGenerator(rescale=1. / 255)
# Step 2: Label the Data
-train_set = train_datagen.flow_from_directory(
+train_set = datagen.flow_from_directory(
path,
target_size=(224, 224),
batch_size=32,
- class_mode='categorical'
+ class_mode='categorical',
+ subset='training'
)
-test_set = test_datagen.flow_from_directory(
+test_set = datagen.flow_from_directory(
path,
target_size=(224, 224),
batch_size=32,
- class_mode='categorical'
+ class_mode='categorical',
+ subset='validation'
)
# Step 4: Build the Model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(224, 224, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D((2, 2)))
+model.add(Conv2D(128, (3, 3), activation='relu'))
+model.add(MaxPooling2D((2, 2)))
+model.add(Conv2D(256, (3, 3), activation='relu'))
+model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
+model.add(Dense(units=256, activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=count_sub_folders(path), activation='softmax'))
-
# Compile the Model after pruning
-model.compile(optimizer='adam',
+model.compile(optimizer=Adam(learning_rate=0.001),
loss=CategoricalCrossentropy(from_logits=False),
metrics=['accuracy'])
# Step 6: Train the Model
-model.fit(train_set, epochs=10, validation_data=test_set)
-
+model.fit(train_set, epochs=20, validation_data=test_set)
# Step 7: Evaluate the Model
loss, accuracy = model.evaluate(test_set)
@@ -57,3 +69,9 @@ print(f'Test loss: {loss}, Test accuracy: {accuracy}')
# Save the trained model
model.save(output)
+end_time = time.time()
+
+execute_time = (end_time - start_time) / 60
+
+# Print the result
+print(f"It took: {execute_time:0.2f} seconds")