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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.8.0\n"
]
}
],
"source": [
"import cv2\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import numpy as np\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"from IPython.display import clear_output\n",
"import PIL.Image as Image\n",
"import tensorflow as tf\n",
"import tensorflow_hub as hub\n",
"from tensorflow.keras import layers\n",
"print(tf.version.VERSION)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"path = 'BilletesMexico'\n",
"data_path = path+\"/BilletesMexico_img\"\n",
"weights_path = path+'/weights'\n",
"numpy_path= path+'/np'\n",
"IMG_SIZE = (224,224)\n",
"latent_dim = 100\n",
"BATCH_SIZE = 128"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 17907 files belonging to 5 classes.\n",
"Using 14326 files for training.\n",
"Found 17907 files belonging to 5 classes.\n",
"Using 3581 files for validation.\n"
]
}
],
"source": [
"train_ds = tf.keras.utils.image_dataset_from_directory(\n",
" str(data_path),\n",
" validation_split=0.2,\n",
" subset=\"training\",\n",
" seed=123,\n",
" image_size=IMG_SIZE,\n",
" batch_size=BATCH_SIZE\n",
")\n",
"\n",
"val_ds = tf.keras.utils.image_dataset_from_directory(\n",
" str(data_path),\n",
" validation_split=0.2,\n",
" subset=\"validation\",\n",
" seed=123,\n",
" image_size=IMG_SIZE,\n",
" batch_size=BATCH_SIZE\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['100' '20' '200' '50' '500']\n"
]
}
],
"source": [
"class_names = np.array(train_ds.class_names)\n",
"print(class_names)\n",
"num_classes = len(class_names)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"normalization_layer = tf.keras.layers.Rescaling(1./255)\n",
"train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y)) # Where x—images, y—labels.\n",
"val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"movilenet =hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4')\n",
"movilenet.trainable=False\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# modelo 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" keras_layer (KerasLayer) (None, 1001) 23853833 \n",
" \n",
" dense (Dense) (None, 200) 200400 \n",
" \n",
" dense_1 (Dense) (None, 5) 1005 \n",
" \n",
"=================================================================\n",
"Total params: 24,055,238\n",
"Trainable params: 201,405\n",
"Non-trainable params: 23,853,833\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = tf.keras.Sequential([\n",
" #layers.RandomFlip(\"horizontal_and_vertical\"),\n",
" #layers.RandomRotation(0.2),\n",
" #layers.RandomContrast(.2),\n",
" #layers.RandomBrightness(.2),\n",
" #layers.RandomZoom(.2),\n",
" # hub.KerasLayer(\"https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\", output_shape=[1001],trainable=False),\n",
" hub.KerasLayer(\"https://tfhub.dev/google/imagenet/inception_v3/classification/5\",trainable=False),\n",
" #movilenet,\n",
" tf.keras.layers.Dense(int(1001/num_classes)),\n",
" tf.keras.layers.Dense(num_classes)\n",
" \n",
"\n",
" ])\n",
"\n",
"\n",
"model.build([None, 224, 224, 3])\n",
"model.summary()\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
" loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=['acc'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pretrained\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1f2f632d0d0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.load_weights((weights_path+'/weights2'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# train\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n",
"112/112 [==============================] - 51s 342ms/step - loss: 0.7923 - acc: 0.7865 - val_loss: 0.4684 - val_acc: 0.8472\n",
"Epoch 2/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.4040 - acc: 0.8689 - val_loss: 0.3798 - val_acc: 0.8741\n",
"Epoch 3/30\n",
"112/112 [==============================] - 33s 291ms/step - loss: 0.3405 - acc: 0.8910 - val_loss: 0.3805 - val_acc: 0.8735\n",
"Epoch 4/30\n",
"112/112 [==============================] - 33s 291ms/step - loss: 0.3149 - acc: 0.8969 - val_loss: 0.3886 - val_acc: 0.8799\n",
"Epoch 5/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.2916 - acc: 0.9032 - val_loss: 0.3701 - val_acc: 0.8802\n",
"Epoch 6/30\n",
"112/112 [==============================] - 33s 293ms/step - loss: 0.2735 - acc: 0.9118 - val_loss: 0.3271 - val_acc: 0.8992\n",
"Epoch 7/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.2507 - acc: 0.9169 - val_loss: 0.3618 - val_acc: 0.8897\n",
"Epoch 8/30\n",
"112/112 [==============================] - 34s 296ms/step - loss: 0.2326 - acc: 0.9220 - val_loss: 0.3089 - val_acc: 0.9034\n",
"Epoch 9/30\n",
"112/112 [==============================] - 34s 296ms/step - loss: 0.2302 - acc: 0.9229 - val_loss: 0.3106 - val_acc: 0.8989\n",
"Epoch 10/30\n",
"112/112 [==============================] - 34s 299ms/step - loss: 0.2245 - acc: 0.9245 - val_loss: 0.2983 - val_acc: 0.9056\n",
"Epoch 11/30\n",
"112/112 [==============================] - 33s 291ms/step - loss: 0.2203 - acc: 0.9250 - val_loss: 0.2885 - val_acc: 0.9084\n",
"Epoch 12/30\n",
"112/112 [==============================] - 33s 291ms/step - loss: 0.1971 - acc: 0.9315 - val_loss: 0.2875 - val_acc: 0.9053\n",
"Epoch 13/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.1951 - acc: 0.9338 - val_loss: 0.2849 - val_acc: 0.9101\n",
"Epoch 14/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.1906 - acc: 0.9368 - val_loss: 0.2891 - val_acc: 0.9078\n",
"Epoch 15/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.1937 - acc: 0.9338 - val_loss: 0.3313 - val_acc: 0.8953\n",
"Epoch 16/30\n",
"112/112 [==============================] - 33s 291ms/step - loss: 0.1865 - acc: 0.9349 - val_loss: 0.2739 - val_acc: 0.9134\n",
"Epoch 17/30\n",
"112/112 [==============================] - 33s 293ms/step - loss: 0.1842 - acc: 0.9357 - val_loss: 0.3166 - val_acc: 0.8995\n",
"Epoch 18/30\n",
"112/112 [==============================] - 35s 306ms/step - loss: 0.1843 - acc: 0.9374 - val_loss: 0.2798 - val_acc: 0.9078\n",
"Epoch 19/30\n",
"112/112 [==============================] - 34s 297ms/step - loss: 0.1736 - acc: 0.9395 - val_loss: 0.2665 - val_acc: 0.9140\n",
"Epoch 20/30\n",
"112/112 [==============================] - 33s 294ms/step - loss: 0.1759 - acc: 0.9396 - val_loss: 0.2782 - val_acc: 0.9065\n",
"Epoch 21/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.1713 - acc: 0.9423 - val_loss: 0.2938 - val_acc: 0.9073\n",
"Epoch 22/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.1726 - acc: 0.9402 - val_loss: 0.3162 - val_acc: 0.9067\n",
"Epoch 23/30\n",
"112/112 [==============================] - 33s 294ms/step - loss: 0.1714 - acc: 0.9430 - val_loss: 0.2765 - val_acc: 0.9123\n",
"Epoch 24/30\n",
"112/112 [==============================] - 33s 291ms/step - loss: 0.1597 - acc: 0.9452 - val_loss: 0.2709 - val_acc: 0.9143\n",
"Epoch 25/30\n",
"112/112 [==============================] - 33s 294ms/step - loss: 0.1550 - acc: 0.9451 - val_loss: 0.2921 - val_acc: 0.9025\n",
"Epoch 26/30\n",
"112/112 [==============================] - 33s 294ms/step - loss: 0.1589 - acc: 0.9429 - val_loss: 0.2790 - val_acc: 0.9145\n",
"Epoch 27/30\n",
"112/112 [==============================] - 33s 294ms/step - loss: 0.1605 - acc: 0.9449 - val_loss: 0.2705 - val_acc: 0.9162\n",
"Epoch 28/30\n",
"112/112 [==============================] - 33s 293ms/step - loss: 0.1653 - acc: 0.9418 - val_loss: 0.2837 - val_acc: 0.9137\n",
"Epoch 29/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.1657 - acc: 0.9423 - val_loss: 0.3002 - val_acc: 0.9059\n",
"Epoch 30/30\n",
"112/112 [==============================] - 33s 292ms/step - loss: 0.1650 - acc: 0.9437 - val_loss: 0.2658 - val_acc: 0.9165\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1899825f400>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(train_ds,\n",
" validation_data=val_ds,\n",
" epochs=30)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"model.save_weights(weights_path+'/weights2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# results"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(224, 224, 3)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1f2f8294910>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"img = Image.open('test.jpg').resize((224,224))\n",
"img = np.array(img)/255.0\n",
"print(img.shape)\n",
"plt.imshow(img)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'50'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result = model.predict(img[np.newaxis,...])\n",
"result.shape\n",
"predicted_class = tf.math.argmax(result[0], axis=-1)\n",
"class_names[int(predicted_class)]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def get_prediction(img):\n",
" img = np.array(img)/255.0\n",
" #print(img.shape)\n",
" #plt.imshow(img)\n",
" result = model.predict(img[np.newaxis,...])\n",
" result.shape\n",
" predicted_class = tf.math.argmax(result[0], axis=-1)\n",
" return class_names[int(predicted_class)]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(224, 224, 3)\n"
]
},
{
"data": {
"text/plain": [
"'50'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = Image.open('test.jpg').resize((224,224))\n",
"\n",
"get_prediction(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# camera input"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"\n",
"\n",
"def funcion(img):\n",
" gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
" gray = cv2.GaussianBlur(gray, (21, 21), 0)\n",
"\n",
" ret, imgt = cv2.threshold(gray, 138, 255, cv2.THRESH_BINARY_INV)\n",
"\n",
" cv2.imshow(\"Image threshold\", imgt)\n",
" countours, hierarchy = cv2.findContours(imgt.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
" rectangles = [cv2.boundingRect(countour) for countour in countours]\n",
" c = 0\n",
" for i , rect in enumerate(rectangles):\n",
" if rect[2] > 100 and rect[3] > 100:\n",
" imgn = img[rect[1]:rect[1] + rect[3], rect[0]:rect[0] + rect[2]]\n",
" imgn = cv2.resize(imgn, (100, 100))\n",
" c += 1 \n",
"\n",
" cv2.rectangle(img, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (255, 0, 0), 2)\n",
" cv2.putText(img, str(get_prediction(imgn)), (rect[0], rect[1]), cv2.FONT_HERSHEY_SIMPLEX, 1, (200, 0, 0), 3, cv2.LINE_AA)\n",
" return img\n",
"\n",
"cam = cv2.VideoCapture(0)\n",
"while True:\n",
" val, img = cam.read()\n",
" img = funcion(img)\n",
" cv2.imshow(\"Image funcion\",img)\n",
" if cv2.waitKey(1) & 0xFF == ord('q'):\n",
" break\n",
"cam.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# imgae augmentation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialised with 1782 image(s) found.\n",
"Output directory set to BilletesMexico/BilletesMexico_img\\output."
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Processing <PIL.Image.Image image mode=RGB size=640x480 at 0x18999F2B910>: 26%|██▌ | 1564/6000 [00:18<00:53, 83.09 Samples/s] \n"
]
},
{
"ename": "ValueError",
"evalue": "image has wrong mode",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mc:\\Users\\franz\\Billdetector\\billetes.ipynb Cell 22'\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/franz/Billdetector/billetes.ipynb#ch0000028?line=12'>13</a>\u001b[0m p\u001b[39m.\u001b[39mrandom_color(\u001b[39m.3\u001b[39m,min_factor\u001b[39m=\u001b[39m\u001b[39m.5\u001b[39m,max_factor\u001b[39m=\u001b[39m\u001b[39m.99\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/franz/Billdetector/billetes.ipynb#ch0000028?line=14'>15</a>\u001b[0m \u001b[39m#p.random_erasing(.1,rectangle_area=.2)\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/franz/Billdetector/billetes.ipynb#ch0000028?line=15'>16</a>\u001b[0m \u001b[39m#p.rotate_without_crop(.2,max_left_rotation=10,max_right_rotation=10)\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/franz/Billdetector/billetes.ipynb#ch0000028?line=16'>17</a>\u001b[0m \u001b[39m#p.zoom_random(.2,percentage_area=.5)\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/franz/Billdetector/billetes.ipynb#ch0000028?line=17'>18</a>\u001b[0m p\u001b[39m.\u001b[39;49msample(\u001b[39m6000\u001b[39;49m)\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\site-packages\\Augmentor\\Pipeline.py:364\u001b[0m, in \u001b[0;36mPipeline.sample\u001b[1;34m(self, n, multi_threaded)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=361'>362</a>\u001b[0m \u001b[39mwith\u001b[39;00m tqdm(total\u001b[39m=\u001b[39m\u001b[39mlen\u001b[39m(augmentor_images), desc\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mExecuting Pipeline\u001b[39m\u001b[39m\"\u001b[39m, unit\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m Samples\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m progress_bar:\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=362'>363</a>\u001b[0m \u001b[39mwith\u001b[39;00m ThreadPoolExecutor(max_workers\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m) \u001b[39mas\u001b[39;00m executor:\n\u001b[1;32m--> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=363'>364</a>\u001b[0m \u001b[39mfor\u001b[39;00m result \u001b[39min\u001b[39;00m executor\u001b[39m.\u001b[39mmap(\u001b[39mself\u001b[39m, augmentor_images):\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=364'>365</a>\u001b[0m progress_bar\u001b[39m.\u001b[39mset_description(\u001b[39m\"\u001b[39m\u001b[39mProcessing \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m result)\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=365'>366</a>\u001b[0m progress_bar\u001b[39m.\u001b[39mupdate(\u001b[39m1\u001b[39m)\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\concurrent\\futures\\_base.py:608\u001b[0m, in \u001b[0;36mExecutor.map.<locals>.result_iterator\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=604'>605</a>\u001b[0m \u001b[39mwhile\u001b[39;00m fs:\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=605'>606</a>\u001b[0m \u001b[39m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=606'>607</a>\u001b[0m \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=607'>608</a>\u001b[0m \u001b[39myield\u001b[39;00m fs\u001b[39m.\u001b[39;49mpop()\u001b[39m.\u001b[39;49mresult()\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=608'>609</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=609'>610</a>\u001b[0m \u001b[39myield\u001b[39;00m fs\u001b[39m.\u001b[39mpop()\u001b[39m.\u001b[39mresult(end_time \u001b[39m-\u001b[39m time\u001b[39m.\u001b[39mmonotonic())\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\concurrent\\futures\\_base.py:438\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=435'>436</a>\u001b[0m \u001b[39mraise\u001b[39;00m CancelledError()\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=436'>437</a>\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m--> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=437'>438</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__get_result()\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=439'>440</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_condition\u001b[39m.\u001b[39mwait(timeout)\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=441'>442</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\concurrent\\futures\\_base.py:390\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=387'>388</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exception:\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=388'>389</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=389'>390</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exception\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=390'>391</a>\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=391'>392</a>\u001b[0m \u001b[39m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/_base.py?line=392'>393</a>\u001b[0m \u001b[39mself\u001b[39m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\concurrent\\futures\\thread.py:52\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/thread.py?line=48'>49</a>\u001b[0m \u001b[39mreturn\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/thread.py?line=50'>51</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/thread.py?line=51'>52</a>\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfn(\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkwargs)\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/thread.py?line=52'>53</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m exc:\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/concurrent/futures/thread.py?line=53'>54</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfuture\u001b[39m.\u001b[39mset_exception(exc)\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\site-packages\\Augmentor\\Pipeline.py:105\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[1;34m(self, augmentor_image)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=91'>92</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, augmentor_image):\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=92'>93</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=93'>94</a>\u001b[0m \u001b[39m Function used by the ThreadPoolExecutor to process the pipeline\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=94'>95</a>\u001b[0m \u001b[39m using multiple threads. Do not call directly.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=102'>103</a>\u001b[0m \u001b[39m :return: None\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=103'>104</a>\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=104'>105</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_execute(augmentor_image)\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\site-packages\\Augmentor\\Pipeline.py:233\u001b[0m, in \u001b[0;36mPipeline._execute\u001b[1;34m(self, augmentor_image, save_to_disk, multi_threaded)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=230'>231</a>\u001b[0m r \u001b[39m=\u001b[39m \u001b[39mround\u001b[39m(random\u001b[39m.\u001b[39muniform(\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m), \u001b[39m1\u001b[39m)\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=231'>232</a>\u001b[0m \u001b[39mif\u001b[39;00m r \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m operation\u001b[39m.\u001b[39mprobability:\n\u001b[1;32m--> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=232'>233</a>\u001b[0m images \u001b[39m=\u001b[39m operation\u001b[39m.\u001b[39;49mperform_operation(images)\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=234'>235</a>\u001b[0m \u001b[39m# TEMP FOR TESTING\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=235'>236</a>\u001b[0m \u001b[39m# save_to_disk = False\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Pipeline.py?line=237'>238</a>\u001b[0m \u001b[39mif\u001b[39;00m save_to_disk:\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\site-packages\\Augmentor\\Operations.py:417\u001b[0m, in \u001b[0;36mRandomContrast.perform_operation\u001b[1;34m(self, images)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Operations.py?line=413'>414</a>\u001b[0m augmented_images \u001b[39m=\u001b[39m []\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Operations.py?line=415'>416</a>\u001b[0m \u001b[39mfor\u001b[39;00m image \u001b[39min\u001b[39;00m images:\n\u001b[1;32m--> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Operations.py?line=416'>417</a>\u001b[0m augmented_images\u001b[39m.\u001b[39mappend(do(image))\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Operations.py?line=418'>419</a>\u001b[0m \u001b[39mreturn\u001b[39;00m augmented_images\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\site-packages\\Augmentor\\Operations.py:412\u001b[0m, in \u001b[0;36mRandomContrast.perform_operation.<locals>.do\u001b[1;34m(image)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Operations.py?line=408'>409</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdo\u001b[39m(image):\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Operations.py?line=410'>411</a>\u001b[0m image_enhancer_contrast \u001b[39m=\u001b[39m ImageEnhance\u001b[39m.\u001b[39mContrast(image)\n\u001b[1;32m--> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/Augmentor/Operations.py?line=411'>412</a>\u001b[0m \u001b[39mreturn\u001b[39;00m image_enhancer_contrast\u001b[39m.\u001b[39;49menhance(factor)\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\site-packages\\PIL\\ImageEnhance.py:36\u001b[0m, in \u001b[0;36m_Enhance.enhance\u001b[1;34m(self, factor)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/ImageEnhance.py?line=24'>25</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39menhance\u001b[39m(\u001b[39mself\u001b[39m, factor):\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/ImageEnhance.py?line=25'>26</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/ImageEnhance.py?line=26'>27</a>\u001b[0m \u001b[39m Returns an enhanced image.\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/ImageEnhance.py?line=27'>28</a>\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/ImageEnhance.py?line=33'>34</a>\u001b[0m \u001b[39m :rtype: :py:class:`~PIL.Image.Image`\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/ImageEnhance.py?line=34'>35</a>\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m---> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/ImageEnhance.py?line=35'>36</a>\u001b[0m \u001b[39mreturn\u001b[39;00m Image\u001b[39m.\u001b[39;49mblend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdegenerate, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mimage, factor)\n",
"File \u001b[1;32m~\\.conda\\envs\\tf-gpu\\lib\\site-packages\\PIL\\Image.py:3052\u001b[0m, in \u001b[0;36mblend\u001b[1;34m(im1, im2, alpha)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/Image.py?line=3049'>3050</a>\u001b[0m im1\u001b[39m.\u001b[39mload()\n\u001b[0;32m <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/Image.py?line=3050'>3051</a>\u001b[0m im2\u001b[39m.\u001b[39mload()\n\u001b[1;32m-> <a href='file:///c%3A/Users/franz/.conda/envs/tf-gpu/lib/site-packages/PIL/Image.py?line=3051'>3052</a>\u001b[0m \u001b[39mreturn\u001b[39;00m im1\u001b[39m.\u001b[39m_new(core\u001b[39m.\u001b[39;49mblend(im1\u001b[39m.\u001b[39;49mim, im2\u001b[39m.\u001b[39;49mim, alpha))\n",
"\u001b[1;31mValueError\u001b[0m: image has wrong mode"
]
}
],
"source": [
"import Augmentor\n",
"# Passing the path of the image directory\n",
"p = Augmentor.Pipeline(data_path)\n",
" \n",
"# Defining augmentation parameters and generating 5 samples\n",
"p.flip_left_right(0.5)\n",
"#p.black_and_white(0.1)\n",
"p.rotate(0.3, 10, 10)\n",
"p.skew(0.4, 0.5)\n",
"p.zoom(probability = 0.2, min_factor = .5, max_factor = 1.5)\n",
"\n",
"p.random_contrast(0.2,min_factor=0.3,max_factor=.9)\n",
"p.random_color(.3,min_factor=.5,max_factor=.99)\n",
"\n",
"#p.random_erasing(.1,rectangle_area=.2)\n",
"#p.rotate_without_crop(.2,max_left_rotation=10,max_right_rotation=10)\n",
"#p.zoom_random(.2,percentage_area=.5)\n",
"p.sample(6000)"
]
}
],
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|