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This commit is contained in:
Michał Kozłowski 2023-01-31 20:05:31 +01:00
parent 6ca0c8d4cc
commit 0aed49a28b
3 changed files with 204 additions and 15 deletions
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2
.gitignore vendored
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@ -2,3 +2,5 @@ data
new_data
*.zip
model
*avi
*pb

32
graph.ipynb
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@ -282,6 +282,38 @@
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"fishes = [\n",
" tf.convert_to_tensor(cv.resize(cv.imread('./new_data/train/Shark/D3U6ZGZZCQTF.jpg'), (227,227),interpolation=cv.INTER_AREA)[None, :], dtype='float32'),\n",
" tf.convert_to_tensor(cv.resize(cv.imread('./new_data/train/Shark/08XY6WGTVFYN.jpg'), (227,227), interpolation=cv.INTER_AREA)[None, :], dtype='float32')\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<tf.Tensor: shape=(1, 10), dtype=float32, numpy=array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)>]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frozen_func(x=fishes[0])"
]
},
{
"cell_type": "code",
"execution_count": 13,

183
rybki.ipynb
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@ -10,7 +10,8 @@
"import matplotlib.pyplot as plt\n",
"import keras\n",
"import numpy as np\n",
"import threading"
"import threading\n",
"import tensorflow as tf"
]
},
{
@ -19,12 +20,166 @@
"metadata": {},
"outputs": [],
"source": [
"model = keras.models.load_model('./model')"
"def wrap_frozen_graph(graph_def, inputs, outputs, print_graph=False):\n",
" def _imports_graph_def():\n",
" tf.compat.v1.import_graph_def(graph_def, name=\"\")\n",
"\n",
" wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, [])\n",
" import_graph = wrapped_import.graph\n",
"\n",
" if print_graph == True:\n",
" print(\"-\" * 50)\n",
" print(\"Frozen model layers: \")\n",
" layers = [op.name for op in import_graph.get_operations()]\n",
" for layer in layers:\n",
" print(layer)\n",
" print(\"-\" * 50)\n",
"\n",
" return wrapped_import.prune(\n",
" tf.nest.map_structure(import_graph.as_graph_element, inputs),\n",
" tf.nest.map_structure(import_graph.as_graph_element, outputs))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------------------------------------\n",
"Frozen model layers: \n",
"x\n",
"sequential/conv2d/Conv2D/ReadVariableOp/resource\n",
"sequential/conv2d/Conv2D/ReadVariableOp\n",
"sequential/conv2d/Conv2D\n",
"sequential/conv2d/BiasAdd/ReadVariableOp/resource\n",
"sequential/conv2d/BiasAdd/ReadVariableOp\n",
"sequential/conv2d/BiasAdd\n",
"sequential/conv2d/Relu\n",
"sequential/batch_normalization/ReadVariableOp/resource\n",
"sequential/batch_normalization/ReadVariableOp\n",
"sequential/batch_normalization/ReadVariableOp_1/resource\n",
"sequential/batch_normalization/ReadVariableOp_1\n",
"sequential/batch_normalization/FusedBatchNormV3/ReadVariableOp/resource\n",
"sequential/batch_normalization/FusedBatchNormV3/ReadVariableOp\n",
"sequential/batch_normalization/FusedBatchNormV3/ReadVariableOp_1/resource\n",
"sequential/batch_normalization/FusedBatchNormV3/ReadVariableOp_1\n",
"sequential/batch_normalization/FusedBatchNormV3\n",
"sequential/max_pooling2d/MaxPool\n",
"sequential/conv2d_1/Conv2D/ReadVariableOp/resource\n",
"sequential/conv2d_1/Conv2D/ReadVariableOp\n",
"sequential/conv2d_1/Conv2D\n",
"sequential/conv2d_1/BiasAdd/ReadVariableOp/resource\n",
"sequential/conv2d_1/BiasAdd/ReadVariableOp\n",
"sequential/conv2d_1/BiasAdd\n",
"sequential/conv2d_1/Relu\n",
"sequential/batch_normalization_1/ReadVariableOp/resource\n",
"sequential/batch_normalization_1/ReadVariableOp\n",
"sequential/batch_normalization_1/ReadVariableOp_1/resource\n",
"sequential/batch_normalization_1/ReadVariableOp_1\n",
"sequential/batch_normalization_1/FusedBatchNormV3/ReadVariableOp/resource\n",
"sequential/batch_normalization_1/FusedBatchNormV3/ReadVariableOp\n",
"sequential/batch_normalization_1/FusedBatchNormV3/ReadVariableOp_1/resource\n",
"sequential/batch_normalization_1/FusedBatchNormV3/ReadVariableOp_1\n",
"sequential/batch_normalization_1/FusedBatchNormV3\n",
"sequential/max_pooling2d_1/MaxPool\n",
"sequential/conv2d_2/Conv2D/ReadVariableOp/resource\n",
"sequential/conv2d_2/Conv2D/ReadVariableOp\n",
"sequential/conv2d_2/Conv2D\n",
"sequential/conv2d_2/BiasAdd/ReadVariableOp/resource\n",
"sequential/conv2d_2/BiasAdd/ReadVariableOp\n",
"sequential/conv2d_2/BiasAdd\n",
"sequential/conv2d_2/Relu\n",
"sequential/batch_normalization_2/ReadVariableOp/resource\n",
"sequential/batch_normalization_2/ReadVariableOp\n",
"sequential/batch_normalization_2/ReadVariableOp_1/resource\n",
"sequential/batch_normalization_2/ReadVariableOp_1\n",
"sequential/batch_normalization_2/FusedBatchNormV3/ReadVariableOp/resource\n",
"sequential/batch_normalization_2/FusedBatchNormV3/ReadVariableOp\n",
"sequential/batch_normalization_2/FusedBatchNormV3/ReadVariableOp_1/resource\n",
"sequential/batch_normalization_2/FusedBatchNormV3/ReadVariableOp_1\n",
"sequential/batch_normalization_2/FusedBatchNormV3\n",
"sequential/conv2d_3/Conv2D/ReadVariableOp/resource\n",
"sequential/conv2d_3/Conv2D/ReadVariableOp\n",
"sequential/conv2d_3/Conv2D\n",
"sequential/conv2d_3/BiasAdd/ReadVariableOp/resource\n",
"sequential/conv2d_3/BiasAdd/ReadVariableOp\n",
"sequential/conv2d_3/BiasAdd\n",
"sequential/conv2d_3/Relu\n",
"sequential/batch_normalization_3/ReadVariableOp/resource\n",
"sequential/batch_normalization_3/ReadVariableOp\n",
"sequential/batch_normalization_3/ReadVariableOp_1/resource\n",
"sequential/batch_normalization_3/ReadVariableOp_1\n",
"sequential/batch_normalization_3/FusedBatchNormV3/ReadVariableOp/resource\n",
"sequential/batch_normalization_3/FusedBatchNormV3/ReadVariableOp\n",
"sequential/batch_normalization_3/FusedBatchNormV3/ReadVariableOp_1/resource\n",
"sequential/batch_normalization_3/FusedBatchNormV3/ReadVariableOp_1\n",
"sequential/batch_normalization_3/FusedBatchNormV3\n",
"sequential/conv2d_4/Conv2D/ReadVariableOp/resource\n",
"sequential/conv2d_4/Conv2D/ReadVariableOp\n",
"sequential/conv2d_4/Conv2D\n",
"sequential/conv2d_4/BiasAdd/ReadVariableOp/resource\n",
"sequential/conv2d_4/BiasAdd/ReadVariableOp\n",
"sequential/conv2d_4/BiasAdd\n",
"sequential/conv2d_4/Relu\n",
"sequential/batch_normalization_4/ReadVariableOp/resource\n",
"sequential/batch_normalization_4/ReadVariableOp\n",
"sequential/batch_normalization_4/ReadVariableOp_1/resource\n",
"sequential/batch_normalization_4/ReadVariableOp_1\n",
"sequential/batch_normalization_4/FusedBatchNormV3/ReadVariableOp/resource\n",
"sequential/batch_normalization_4/FusedBatchNormV3/ReadVariableOp\n",
"sequential/batch_normalization_4/FusedBatchNormV3/ReadVariableOp_1/resource\n",
"sequential/batch_normalization_4/FusedBatchNormV3/ReadVariableOp_1\n",
"sequential/batch_normalization_4/FusedBatchNormV3\n",
"sequential/max_pooling2d_2/MaxPool\n",
"sequential/flatten/Const\n",
"sequential/flatten/Reshape\n",
"sequential/dense/MatMul/ReadVariableOp/resource\n",
"sequential/dense/MatMul/ReadVariableOp\n",
"sequential/dense/MatMul\n",
"sequential/dense/BiasAdd/ReadVariableOp/resource\n",
"sequential/dense/BiasAdd/ReadVariableOp\n",
"sequential/dense/BiasAdd\n",
"sequential/dense/Relu\n",
"sequential/dense_1/MatMul/ReadVariableOp/resource\n",
"sequential/dense_1/MatMul/ReadVariableOp\n",
"sequential/dense_1/MatMul\n",
"sequential/dense_1/BiasAdd/ReadVariableOp/resource\n",
"sequential/dense_1/BiasAdd/ReadVariableOp\n",
"sequential/dense_1/BiasAdd\n",
"sequential/dense_1/Relu\n",
"sequential/dense_2/MatMul/ReadVariableOp/resource\n",
"sequential/dense_2/MatMul/ReadVariableOp\n",
"sequential/dense_2/MatMul\n",
"sequential/dense_2/BiasAdd/ReadVariableOp/resource\n",
"sequential/dense_2/BiasAdd/ReadVariableOp\n",
"sequential/dense_2/BiasAdd\n",
"sequential/dense_2/Softmax\n",
"NoOp\n",
"Identity\n",
"--------------------------------------------------\n"
]
}
],
"source": [
" # Load frozen graph using TensorFlow 1.x functions\n",
"with tf.io.gfile.GFile(\"./frozen_models/frozen_graph2.pb\", \"rb\") as f:\n",
" graph_def = tf.compat.v1.GraphDef()\n",
" loaded = graph_def.ParseFromString(f.read())\n",
"\n",
"# Wrap frozen graph to ConcreteFunctions\n",
"frozen_func = wrap_frozen_graph(graph_def=graph_def,\n",
" inputs=[\"x:0\"],\n",
" outputs=[\"Identity:0\"],\n",
" print_graph=False)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@ -58,15 +213,15 @@
" rectangle = cv2.rectangle(roi,(x,y),(x+w,y+h),(0,255,0),3)\n",
" image_to_predict = roi[y:y+h,x:x+w]\n",
" image_to_predict = cv2.resize(image_to_predict,(227,227))\n",
" images.append((x,y,rectangle,np.expand_dims(image_to_predict,axis=0)))\n",
" # images.append((x,y,rectangle,np.expand_dims(image_to_predict,axis=0)))\n",
" \n",
" # pred = model.predict(np.expand_dims(image_to_predict, axis=0))\n",
" # label = class_names[np.argmax(pred)]\n",
" if images:\n",
" pred = model.predict(np.vstack([image[3] for image in images]))\n",
" labels = [class_names[np.argmax(pre)] for pre in pred]\n",
" for i,image in enumerate(images):\n",
" cv2.putText(image[2], labels[i], (image[0], image[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 1)\n",
" pred = frozen_func(x=tf.convert_to_tensor(image_to_predict[None, :], dtype='float32'))\n",
" label = class_names[np.argmax(pred)]\n",
" cv2.putText(rectangle, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 1)\n",
" # if images:\n",
" # pred = model.predict(np.vstack([image[3] for image in images]))\n",
" # labels = [class_names[np.argmax(pre)] for pre in pred]\n",
" # for i,image in enumerate(images):\n",
" roi = cv2.resize(roi, (960, 540)) \n",
" cv2.imshow(\"roi\", roi)\n",
"\n",
@ -94,7 +249,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "um",
"language": "python",
"name": "python3"
},
@ -108,12 +263,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.2"
"version": "3.9.15"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "393784674bcf6e74f2fc9b1b5fb3713f9bd5fc6f8172c594e5cfa8e8c12849bc"
"hash": "876e189cbbe99a9a838ece62aae1013186c4bb7e0254a10cfa2f9b2381853efb"
}
}
},