196 lines
6.7 KiB
Plaintext
196 lines
6.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import matplotlib.pyplot as plt\n",
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"import keras\n",
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"import numpy as np\n",
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"import threading\n",
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"import tensorflow as tf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def wrap_frozen_graph(graph_def, inputs, outputs, print_graph=False):\n",
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" def _imports_graph_def():\n",
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" tf.compat.v1.import_graph_def(graph_def, name=\"\")\n",
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"\n",
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" wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, [])\n",
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" import_graph = wrapped_import.graph\n",
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"\n",
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" if print_graph == True:\n",
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" print(\"-\" * 50)\n",
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" print(\"Frozen model layers: \")\n",
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" layers = [op.name for op in import_graph.get_operations()]\n",
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" for layer in layers:\n",
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" print(layer)\n",
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" print(\"-\" * 50)\n",
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"\n",
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" return wrapped_import.prune(\n",
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" tf.nest.map_structure(import_graph.as_graph_element, inputs),\n",
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" tf.nest.map_structure(import_graph.as_graph_element, outputs))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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" # Load frozen graph using TensorFlow 1.x functions\n",
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"with tf.io.gfile.GFile(\"./frozen_models/frozen_graph_best_vgg.pb\", \"rb\") as f:\n",
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" graph_def = tf.compat.v1.GraphDef()\n",
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" loaded = graph_def.ParseFromString(f.read())\n",
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"\n",
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"# Wrap frozen graph to ConcreteFunctions\n",
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"frozen_func = wrap_frozen_graph(graph_def=graph_def,\n",
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" inputs=[\"x:0\"],\n",
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" outputs=[\"Identity:0\"],\n",
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" print_graph=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Jellyfish'"
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]
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},
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class_names=sorted(['Fish', \"Jellyfish\", 'Lionfish', 'Shark', 'Stingray', 'Turtle'])\n",
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"a = cv2.imread('test.PNG')\n",
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"# a.shape\n",
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"a = cv2.resize(a,(227,227))\n",
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"pred = frozen_func(x=tf.convert_to_tensor(a[None, :], dtype='float32'))\n",
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"label = class_names[np.argmax(pred)]\n",
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"label"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[51], line 42\u001b[0m\n\u001b[0;32m 39\u001b[0m roi \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39mresize(roi, (\u001b[39m960\u001b[39m, \u001b[39m540\u001b[39m)) \n\u001b[0;32m 40\u001b[0m cv2\u001b[39m.\u001b[39mimshow(\u001b[39m\"\u001b[39m\u001b[39mroi\u001b[39m\u001b[39m\"\u001b[39m, roi)\n\u001b[1;32m---> 42\u001b[0m key \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39;49mwaitKey(\u001b[39m30\u001b[39;49m)\n\u001b[0;32m 43\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39m==\u001b[39m \u001b[39m27\u001b[39m:\n\u001b[0;32m 44\u001b[0m \u001b[39mbreak\u001b[39;00m\n",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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],
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"source": [
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"cap = cv2.VideoCapture(\"rybki4.mp4\")\n",
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"# cap.set(cv2.CAP_PROP_FPS, 60)\n",
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"\n",
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"class_names=sorted(['Fish', \"Jellyfish\", 'Lionfish', 'Shark', 'Stingray', 'Turtle'])\n",
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"object_detector = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=50)\n",
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"\n",
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"\n",
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"# width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) \n",
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"# height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
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"# fps = cap.get(cv2.CAP_PROP_FPS)\n",
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"# out = cv2.VideoWriter('track_fish.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (width, height))\n",
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"\n",
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"while True:\n",
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" ret, frame = cap.read()\n",
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" if(ret):\n",
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" roi = frame[100: 900,330:1900]\n",
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" mask = object_detector.apply(roi)\n",
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" _, mask = cv2.threshold(mask,254,255, cv2.THRESH_BINARY)\n",
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" conturs, _ =cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
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"\n",
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" images = []\n",
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" for cnt in conturs:\n",
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" area = cv2.contourArea(cnt)\n",
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" if area > 300:\n",
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" #cv2.drawContours(roi,[cnt],-1,(0,255,0),2)\n",
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" x,y,w,h = cv2.boundingRect(cnt)\n",
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" rectangle = cv2.rectangle(roi,(x,y),(x+w,y+h),(0,255,0),3)\n",
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" # images.append((x,y,rectangle,np.expand_dims(image_to_predict,axis=0)))\n",
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" # image_to_predict = roi[y:y+h,x:x+w]\n",
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" # image_to_predict = cv2.resize(image_to_predict,(227,227))\n",
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" # pred = frozen_func(x=tf.convert_to_tensor(image_to_predict[None, :], dtype='float32'))\n",
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" # label = class_names[np.argmax(pred)]\n",
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" # cv2.putText(rectangle, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 1)\n",
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" \n",
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" # if images:\n",
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" # pred = model.predict(np.vstack([image[3] for image in images]))\n",
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" # labels = [class_names[np.argmax(pre)] for pre in pred]\n",
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" # for i,image in enumerate(images):\n",
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" roi = cv2.resize(roi, (960, 540)) \n",
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" cv2.imshow(\"roi\", roi)\n",
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"\n",
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" key = cv2.waitKey(30)\n",
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" if key == 27:\n",
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" break\n",
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"\n",
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" #out.write(frame)\n",
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" else:\n",
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" break\n",
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"\n",
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"\n",
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"#out.release()\n",
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"cap.release()\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "um",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.15"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "876e189cbbe99a9a838ece62aae1013186c4bb7e0254a10cfa2f9b2381853efb"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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