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Final_Sub #50

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169d061
Final_Sub
ElricPi Oct 27, 2021
01d93f7
Final_Sub
ElricPi Oct 28, 2021
1abea84
Update TrainingOI.ipynb
ElricPi Oct 29, 2021
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110 changes: 110 additions & 0 deletions .ipynb_checkpoints/FinalPrediction-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "bcbffb83",
"metadata": {},
"outputs": [],
"source": [
"from keras.models import model_from_json\n",
"import operator\n",
"import cv2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "75d163ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded model from disk\n"
]
}
],
"source": [
"json_file = open(\"model-bw.json\", \"r\")\n",
"model_json = json_file.read()\n",
"json_file.close()\n",
"loaded_model = model_from_json(model_json)\n",
"loaded_model.load_weights(\"model-bw.h5\")\n",
"print(\"Loaded model from disk\")\n",
"cap = cv2.VideoCapture(0)\n",
"categories = {0: 'ZERO', 1: 'ONE', 2: 'TWO', 3: 'THREE', 4: 'FOUR', 5: 'FIVE'}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "51692885",
"metadata": {},
"outputs": [],
"source": [
"while True:\n",
" _, frame = cap.read()\n",
" frame = cv2.flip(frame, 1)\n",
" x1 = int(0.5*frame.shape[1])\n",
" y1 = 10\n",
" x2 = frame.shape[1]-10\n",
" y2 = int(0.5*frame.shape[1])\n",
" cv2.putText(frame, \"ElricPi - TDoC 2021\", (175, 450), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (225,255,0), 3)\n",
" cv2.rectangle(frame, (x1-1, y1-1), (x2+1, y2+1), (255,255,255) ,3)\n",
" roi = frame[y1:y2, x1:x2]\n",
" roi = cv2.resize(roi, (64, 64))\n",
" roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n",
" cv2.putText(frame, \"R.O.I\", (440, 350), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,225,0), 3)\n",
" _, test_image = cv2.threshold(roi, 120, 255, cv2.THRESH_BINARY)\n",
" cv2.imshow(\"ROI\", test_image)\n",
" result = loaded_model.predict(test_image.reshape(1, 64, 64, 1))\n",
" prediction = {'ZERO': result[0][0],\n",
" 'ONE': result[0][1],\n",
" 'TWO': result[0][2],\n",
" 'THREE': result[0][3],\n",
" 'FOUR': result[0][4],\n",
" 'FIVE': result[0][5]}\n",
" prediction = sorted(prediction.items(), key=operator.itemgetter(1), reverse=True)\n",
" cv2.putText(frame, \"PREDICTION:\", (30, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\n",
" cv2.putText(frame, prediction[0][0], (80, 130), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\n",
" cv2.imshow(\"Frame\", frame)\n",
" interrupt = cv2.waitKey(10)\n",
" if interrupt & 0xFF == 27:\n",
" break\n",
"cap.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73bcac5d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
137 changes: 137 additions & 0 deletions .ipynb_checkpoints/TrainingOI-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "faae96f7",
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dense\n",
"from keras.preprocessing.image import ImageDataGenerator\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3c6dab91",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 865 images belonging to 6 classes.\n",
"Found 30 images belonging to 6 classes.\n",
"Epoch 1/24\n",
"145/145 [==============================] - 6s 33ms/step - loss: 1.8881 - accuracy: 0.3503 - val_loss: 2.0199 - val_accuracy: 0.3667\n",
"Epoch 2/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.7299 - accuracy: 0.7555 - val_loss: 1.9466 - val_accuracy: 0.4000\n",
"Epoch 3/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.4640 - accuracy: 0.8614 - val_loss: 1.6799 - val_accuracy: 0.3667\n",
"Epoch 4/24\n",
"145/145 [==============================] - 4s 25ms/step - loss: 0.4190 - accuracy: 0.8388 - val_loss: 2.0598 - val_accuracy: 0.4333\n",
"Epoch 5/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.2567 - accuracy: 0.9080 - val_loss: 2.2239 - val_accuracy: 0.4000\n",
"Epoch 6/24\n",
"145/145 [==============================] - 4s 25ms/step - loss: 0.2608 - accuracy: 0.9103 - val_loss: 2.7712 - val_accuracy: 0.4333\n",
"Epoch 7/24\n",
"145/145 [==============================] - 4s 25ms/step - loss: 0.2445 - accuracy: 0.9337 - val_loss: 2.1731 - val_accuracy: 0.4667\n",
"Epoch 8/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.1987 - accuracy: 0.9225 - val_loss: 1.8919 - val_accuracy: 0.3667\n",
"Epoch 9/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.1457 - accuracy: 0.9532 - val_loss: 2.4349 - val_accuracy: 0.4000\n",
"Epoch 10/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.0940 - accuracy: 0.9771 - val_loss: 2.3527 - val_accuracy: 0.4333\n",
"Epoch 11/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.1143 - accuracy: 0.9605 - val_loss: 2.4939 - val_accuracy: 0.4333\n",
"Epoch 12/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.1251 - accuracy: 0.9553 - val_loss: 3.1054 - val_accuracy: 0.4333\n",
"Epoch 13/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.1081 - accuracy: 0.9637 - val_loss: 2.7446 - val_accuracy: 0.4667\n",
"Epoch 14/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.1183 - accuracy: 0.9587 - val_loss: 3.0388 - val_accuracy: 0.4333\n",
"Epoch 15/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.1070 - accuracy: 0.9617 - val_loss: 3.1897 - val_accuracy: 0.4667\n",
"Epoch 16/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.0689 - accuracy: 0.9797 - val_loss: 2.7554 - val_accuracy: 0.4333\n",
"Epoch 17/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.1332 - accuracy: 0.9504 - val_loss: 3.6933 - val_accuracy: 0.3333\n",
"Epoch 18/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.0743 - accuracy: 0.9757 - val_loss: 2.9300 - val_accuracy: 0.5333\n",
"Epoch 19/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.0304 - accuracy: 0.9944 - val_loss: 2.6740 - val_accuracy: 0.4333\n",
"Epoch 20/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.0336 - accuracy: 0.9927 - val_loss: 3.8067 - val_accuracy: 0.5000\n",
"Epoch 21/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.0506 - accuracy: 0.9823 - val_loss: 3.4033 - val_accuracy: 0.4333\n",
"Epoch 22/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.0354 - accuracy: 0.9879 - val_loss: 2.9072 - val_accuracy: 0.5000\n",
"Epoch 23/24\n",
"145/145 [==============================] - 4s 26ms/step - loss: 0.0413 - accuracy: 0.9833 - val_loss: 3.5864 - val_accuracy: 0.4667\n",
"Epoch 24/24\n",
"145/145 [==============================] - 4s 27ms/step - loss: 0.0105 - accuracy: 0.9997 - val_loss: 3.6084 - val_accuracy: 0.4333\n"
]
}
],
"source": [
"classifier = Sequential()\n",
"classifier.add(Convolution2D(32, (3, 3), input_shape=(64, 64, 1), activation='tanh'))\n",
"classifier.add(MaxPooling2D(pool_size=(2, 2)))\n",
"classifier.add(Convolution2D(32, (3, 3), activation='tanh'))\n",
"classifier.add(MaxPooling2D(pool_size=(2, 2)))\n",
"classifier.add(Flatten())\n",
"classifier.add(Dense(units=128, activation='tanh'))\n",
"classifier.add(Dense(units=6, activation='softmax'))\n",
"classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
"\n",
"train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)\n",
"\n",
"test_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"training_set = train_datagen.flow_from_directory(r\"C:\\Users\\TISYAGUPTA PYNE\\tdoc21\\data\\train\", target_size=(64, 64), batch_size=6, color_mode='grayscale', class_mode='categorical')\n",
"\n",
"test_set = test_datagen.flow_from_directory(r\"C:\\Users\\TISYAGUPTA PYNE\\tdoc21\\data\\test\", target_size=(64, 64), batch_size=6, color_mode='grayscale', class_mode='categorical')\n",
"\n",
"\n",
"classifier.fit_generator(training_set, epochs=24, validation_data=test_set)\n",
"\n",
"model_json = classifier.to_json()\n",
"\n",
"with open(\"model-bw.json1\", \"w\") as json_file:\n",
" json_file.write(model_json)\n",
"classifier.save_weights('model-bw1.h5')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1ec4a6ae",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
110 changes: 110 additions & 0 deletions FinalPrediction.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,110 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "bcbffb83",
"metadata": {},
"outputs": [],
"source": [
"from keras.models import model_from_json\n",
"import operator\n",
"import cv2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "75d163ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded model from disk\n"
]
}
],
"source": [
"json_file = open(\"model-bw1.json\", \"r\")\n",
"model_json = json_file.read()\n",
"json_file.close()\n",
"loaded_model = model_from_json(model_json)\n",
"loaded_model.load_weights(\"model-bw1.h5\")\n",
"print(\"Loaded model from disk\")\n",
"cap = cv2.VideoCapture(0)\n",
"categories = {0: 'ZERO', 1: 'ONE', 2: 'TWO', 3: 'THREE', 4: 'FOUR', 5: 'FIVE'}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "51692885",
"metadata": {},
"outputs": [],
"source": [
"while True:\n",
" _, frame = cap.read()\n",
" frame = cv2.flip(frame, 1)\n",
" x1 = int(0.5*frame.shape[1])\n",
" y1 = 10\n",
" x2 = frame.shape[1]-10\n",
" y2 = int(0.5*frame.shape[1])\n",
" cv2.putText(frame, \"ElricPi - TDoC 2021\", (175, 450), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (225,255,0), 3)\n",
" cv2.rectangle(frame, (x1-1, y1-1), (x2+1, y2+1), (255,255,255) ,3)\n",
" roi = frame[y1:y2, x1:x2]\n",
" roi = cv2.resize(roi, (64, 64))\n",
" roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n",
" cv2.putText(frame, \"R.O.I\", (440, 350), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,225,0), 3)\n",
" _, test_image = cv2.threshold(roi, 120, 255, cv2.THRESH_BINARY)\n",
" cv2.imshow(\"ROI\", test_image)\n",
" result = loaded_model.predict(test_image.reshape(1, 64, 64, 1))\n",
" prediction = {'ZERO': result[0][0],\n",
" 'ONE': result[0][1],\n",
" 'TWO': result[0][2],\n",
" 'THREE': result[0][3],\n",
" 'FOUR': result[0][4],\n",
" 'FIVE': result[0][5]}\n",
" prediction = sorted(prediction.items(), key=operator.itemgetter(1), reverse=True)\n",
" cv2.putText(frame, \"PREDICTION:\", (30, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\n",
" cv2.putText(frame, prediction[0][0], (80, 130), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\n",
" cv2.imshow(\"Frame\", frame)\n",
" interrupt = cv2.waitKey(10)\n",
" if interrupt & 0xFF == 27:\n",
" break\n",
"cap.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73bcac5d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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