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danmusetoiu/init_steps.ipynb

Created October 15, 2018 12:32
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init_steps.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Israel Directorate of Defense Research & Development - adica armata israeliana - a lansat o provocare interesanta in care am decis sa ma bag! <br>\n",
"Pe scurt, oamenii vor sa identifice automat, din imagini aeriene, particularitatile vehiculelor care circula liber pe strazile lor! Desi nu se spune explicit, banuiesc ca vor sa se aprinda un beculet pe undeva atunci cand pe strada apare un SUV care in loc de trapa are o mitraliera sau are parbrizul acoperit de blindaj. <br>\n",
"Ii inteleg - n-as vrea sa fiu in pielea lor!<br>\n",
"Baietii astia pun la dispozitie o baza de date destul de subtirica - o sa vedeti si voi de ce cred asta.<br>\n",
"Pun pe net, in limba romana, munca mea in speranta ca printre voi, mult-prea-intelegatorii mei cititori, se vor gasi cativa care sa puna umarul la demersul asta!<br>\n",
"Asadar:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['training imagery',\n",
" 'test.csv',\n",
" 'Readme.txt',\n",
" 'train.csv',\n",
" '.ipynb_checkpoints',\n",
" 'test imagery']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.listdir('V/Dataset for participants V2/')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zic sa incepem cu csv-ul train, sa vedem ce contine:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>tag_id</th>\n",
" <th>image_id</th>\n",
" <th>p1_x</th>\n",
" <th>p_1y</th>\n",
" <th>p2_x</th>\n",
" <th>p2_y</th>\n",
" <th>p3_x</th>\n",
" <th>p3_y</th>\n",
" <th>p4_x</th>\n",
" <th>p4_y</th>\n",
" <th>...</th>\n",
" <th>enclosed_cab</th>\n",
" <th>spare_wheel</th>\n",
" <th>wrecked</th>\n",
" <th>flatbed</th>\n",
" <th>ladder</th>\n",
" <th>enclosed_box</th>\n",
" <th>soft_shell_box</th>\n",
" <th>harnessed_to_a_cart</th>\n",
" <th>ac_vents</th>\n",
" <th>color</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>15036</td>\n",
" <td>16490</td>\n",
" <td>3988.399902</td>\n",
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" <td>black</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>31658</td>\n",
" <td>16490</td>\n",
" <td>3380.804152</td>\n",
" <td>33.370945</td>\n",
" <td>3329.811654</td>\n",
" <td>128.465062</td>\n",
" <td>3358.064254</td>\n",
" <td>142.935906</td>\n",
" <td>3409.056752</td>\n",
" <td>44.396350</td>\n",
" <td>...</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>red</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>26971</td>\n",
" <td>17122</td>\n",
" <td>3948.334473</td>\n",
" <td>1426.797241</td>\n",
" <td>3958.876221</td>\n",
" <td>1410.750122</td>\n",
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" <td>1438.202759</td>\n",
" <td>3990.123779</td>\n",
" <td>1454.249878</td>\n",
" <td>...</td>\n",
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" <td>-1</td>\n",
" <td>red</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19301</td>\n",
" <td>12193</td>\n",
" <td>1977.798007</td>\n",
" <td>1009.010733</td>\n",
" <td>1983.435998</td>\n",
" <td>992.096760</td>\n",
" <td>2034.177918</td>\n",
" <td>1009.010733</td>\n",
" <td>2027.913483</td>\n",
" <td>1027.177593</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>silver/grey</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>35906</td>\n",
" <td>12193</td>\n",
" <td>3196.620177</td>\n",
" <td>1284.240545</td>\n",
" <td>3193.174738</td>\n",
" <td>1311.804057</td>\n",
" <td>3288.957942</td>\n",
" <td>1321.451286</td>\n",
" <td>3292.403381</td>\n",
" <td>1293.198686</td>\n",
" <td>...</td>\n",
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" <td>-1</td>\n",
" <td>red</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" tag_id image_id p1_x p_1y p2_x p2_y \\\n",
"0 15036 16490 3988.399902 1227.588745 3986.710693 1213.288208 \n",
"1 31658 16490 3380.804152 33.370945 3329.811654 128.465062 \n",
"2 26971 17122 3948.334473 1426.797241 3958.876221 1410.750122 \n",
"3 19301 12193 1977.798007 1009.010733 1983.435998 992.096760 \n",
"4 35906 12193 3196.620177 1284.240545 3193.174738 1311.804057 \n",
"\n",
" p3_x p3_y p4_x p4_y ... \\\n",
"0 4002.600098 1211.411255 4004.289307 1225.711792 ... \n",
"1 3358.064254 142.935906 3409.056752 44.396350 ... \n",
"2 4000.665527 1438.202759 3990.123779 1454.249878 ... \n",
"3 2034.177918 1009.010733 2027.913483 1027.177593 ... \n",
"4 3288.957942 1321.451286 3292.403381 1293.198686 ... \n",
"\n",
" enclosed_cab spare_wheel wrecked flatbed ladder enclosed_box \\\n",
"0 0 0 0 0 0 0 \n",
"1 -1 -1 0 -1 0 1 \n",
"2 0 0 0 -1 -1 -1 \n",
"3 0 0 0 -1 -1 -1 \n",
"4 -1 -1 0 1 0 0 \n",
"\n",
" soft_shell_box harnessed_to_a_cart ac_vents color \n",
"0 0 0 -1 black \n",
"1 0 0 -1 red \n",
"2 -1 -1 -1 red \n",
"3 -1 -1 -1 silver/grey \n",
"4 0 0 -1 red \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df_train = pd.read_csv('V/Dataset for participants V2/train.csv')\n",
"df_train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"25 de coloane - nici nu-mi incap pe display! Hai cu axes:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[RangeIndex(start=0, stop=11617, step=1),\n",
" Index(['tag_id', 'image_id', 'p1_x', 'p_1y', ' p2_x', ' p2_y', ' p3_x',\n",
" ' p3_y', ' p4_x', ' p4_y', 'general_class', 'sub_class', 'sunroof',\n",
" 'luggage_carrier', 'open_cargo_area', 'enclosed_cab', 'spare_wheel',\n",
" 'wrecked', 'flatbed', 'ladder', 'enclosed_box', 'soft_shell_box',\n",
" 'harnessed_to_a_cart', 'ac_vents', 'color'],\n",
" dtype='object')]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.axes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cateva explicatii:<br>\n",
"1. p1_x ... p4_y sunt coordonatele x si y ale unui dreptunghi care, teoretic, ar trebui sa decupeze vehiculul in imaginea de ansamblu (fiecare imagine contine mai multe vehicule, de mai multe feluri)\n",
"2. general_class ... color sunt labels-urile vehiculelor marcate manual (x si y de mai sus) pe imaginile de ansamblu (munca de chinez batran pentru care am toata admiratia)\n",
"3. image_id este id-ul imaginii din folderul imagery\n",
"4. tag_id nu stiu deocamdata ce e si, in principiu, nici nu ma intereseaza in aceasta etapa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hai sa vedem si cateva imagini:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"path_im = 'V/Dataset for participants V2/training imagery/'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11302f7f0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"im = plt.imread(path_im + os.listdir(path_im)[0])\n",
"plt.title(os.listdir(path_im)[0])\n",
"plt.imshow(im)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vad trei vehicule: doua albe, unul negru. Masina alba din stanga are trapa (sunroof)! Pe aia neagra n-o vad bine, k nu mi-am schimbat ink okelarii :))))<br>\n",
"Hai sa vedem si \"fisa\" imaginii din csv-ul pe care, mai devreme, l-am facut DataFrame!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>tag_id</th>\n",
" <th>image_id</th>\n",
" <th>p1_x</th>\n",
" <th>p_1y</th>\n",
" <th>p2_x</th>\n",
" <th>p2_y</th>\n",
" <th>p3_x</th>\n",
" <th>p3_y</th>\n",
" <th>p4_x</th>\n",
" <th>...</th>\n",
" <th>enclosed_cab</th>\n",
" <th>spare_wheel</th>\n",
" <th>wrecked</th>\n",
" <th>flatbed</th>\n",
" <th>ladder</th>\n",
" <th>enclosed_box</th>\n",
" <th>soft_shell_box</th>\n",
" <th>harnessed_to_a_cart</th>\n",
" <th>ac_vents</th>\n",
" <th>color</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5880</td>\n",
" <td>10026</td>\n",
" <td>11759</td>\n",
" <td>194.527649</td>\n",
" <td>162.832169</td>\n",
" <td>231.645416</td>\n",
" <td>179.770630</td>\n",
" <td>210.472351</td>\n",
" <td>226.167831</td>\n",
" <td>173.354584</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>black</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5881</td>\n",
" <td>35797</td>\n",
" <td>11759</td>\n",
" <td>399.828064</td>\n",
" <td>468.871216</td>\n",
" <td>416.045471</td>\n",
" <td>451.179504</td>\n",
" <td>466.171936</td>\n",
" <td>497.128784</td>\n",
" <td>449.954529</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>white</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5882</td>\n",
" <td>32838</td>\n",
" <td>11759</td>\n",
" <td>41.066010</td>\n",
" <td>373.705048</td>\n",
" <td>40.009590</td>\n",
" <td>349.728333</td>\n",
" <td>117.933991</td>\n",
" <td>346.294952</td>\n",
" <td>118.990410</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>white</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" index tag_id image_id p1_x p_1y p2_x p2_y \\\n",
"0 5880 10026 11759 194.527649 162.832169 231.645416 179.770630 \n",
"1 5881 35797 11759 399.828064 468.871216 416.045471 451.179504 \n",
"2 5882 32838 11759 41.066010 373.705048 40.009590 349.728333 \n",
"\n",
" p3_x p3_y p4_x ... enclosed_cab spare_wheel \\\n",
"0 210.472351 226.167831 173.354584 ... 0 0 \n",
"1 466.171936 497.128784 449.954529 ... 0 0 \n",
"2 117.933991 346.294952 118.990410 ... 0 0 \n",
"\n",
" wrecked flatbed ladder enclosed_box soft_shell_box harnessed_to_a_cart \\\n",
"0 0 -1 -1 -1 -1 -1 \n",
"1 0 -1 -1 -1 -1 -1 \n",
"2 0 -1 -1 -1 -1 -1 \n",
"\n",
" ac_vents color \n",
"0 -1 black \n",
"1 -1 white \n",
"2 -1 white \n",
"\n",
"[3 rows x 26 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im_11759 = pd.DataFrame([df_train.iloc[i] for i in range (0, len(df_train)) if df_train['image_id'][i] == 11759])\n",
"im_11759.reset_index(inplace = True)\n",
"im_11759"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mdap, csv-ul zice cam acelasi lucru!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ma gandesc sa \"decupez\" vehiculele si sa le servesc pe tava modelelor deja consacrate pe piata: vgg16, inception, resnet, mobilenet..., sa vedem ce iese!<br>\n",
"Asadar, hai cu foarfeca!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_min = [173.35458369999998, 399.82806400000004, 40.00959015]\n",
"x_max = [231.64541630000002, 466.171936, 118.99040990000002]\n",
"y_min = [162.8321686, 451.1795044, 346.2949524]\n",
"y_max = [226.1678314, 514.8204956000001, 373.7050476]\n"
]
}
],
"source": [
"import skimage\n",
"from skimage import io\n",
"\n",
"x_min = []\n",
"x_max = []\n",
"y_min = []\n",
"y_max = []\n",
"\n",
"for i in range (0, len(im_11759)):\n",
" \n",
" x_min.append(min(im_11759['p1_x'][i], im_11759[' p2_x'][i], im_11759[' p3_x'][i],im_11759[' p4_x'][i])) \n",
" x_max.append(max(im_11759['p1_x'][i], im_11759[' p2_x'][i], im_11759[' p3_x'][i],im_11759[' p4_x'][i]))\n",
" y_min.append(min(im_11759['p_1y'][i], im_11759[' p2_y'][i], im_11759[' p3_y'][i],im_11759[' p4_y'][i]))\n",
" y_max.append(max(im_11759['p_1y'][i], im_11759[' p2_y'][i], im_11759[' p3_y'][i],im_11759[' p4_y'][i]))\n",
" \n",
"print('x_min = {0}'.format(x_min))\n",
"print('x_max = {0}'.format(x_max))\n",
"print('y_min = {0}'.format(y_min))\n",
"print('y_max = {0}'.format(y_max))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"im = io.imread(path_im + str(im_11759['image_id'][0]) + '.jpg')\n",
"\n",
"masina_0 = im[int(y_min[0]-10):int(y_max[0]+10), int(x_min[0]-10):int(x_max[0]+10)]\n",
"masina_1 = im[int(y_min[1]-10):int(y_max[1]+10), int(x_min[1]-10):int(x_max[1]+10)]\n",
"masina_2 = im[int(y_min[2]-10):int(y_max[2]+10), int(x_min[2]-10):int(x_max[2]+10)]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/skimage/io/_plugins/matplotlib_plugin.py:51: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n",
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/axes/_base.py:1428: MatplotlibDeprecationWarning: The 'box-forced' keyword argument is deprecated since 2.2.\n",
" \" since 2.2.\", cbook.mplDeprecation)\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"io.imshow(masina_0)\n",
"io.imsave('masina_0.png', masina_0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/skimage/io/_plugins/matplotlib_plugin.py:51: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n",
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/axes/_base.py:1428: MatplotlibDeprecationWarning: The 'box-forced' keyword argument is deprecated since 2.2.\n",
" \" since 2.2.\", cbook.mplDeprecation)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"io.imshow(masina_1)\n",
"io.imsave('masina_1.png', masina_1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/skimage/io/_plugins/matplotlib_plugin.py:51: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n",
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/axes/_base.py:1428: MatplotlibDeprecationWarning: The 'box-forced' keyword argument is deprecated since 2.2.\n",
" \" since 2.2.\", cbook.mplDeprecation)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"io.imshow(masina_2)\n",
"io.imsave('masina_2.png', masina_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Asadar, le-am decupat, acum e timpul sa le arat modelelor \"consacrate\" sa vad ce iese!\n",
"Folosesc Keras ca sunt lenes deci fan! "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n",
"Using TensorFlow backend.\n",
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"import keras\n",
"import numpy as np\n",
"from keras.applications import vgg16, inception_v3, resnet50, mobilenet\n",
"\n",
"vgg_model = vgg16.VGG16(weights='imagenet')\n",
"\n",
"inception_model = inception_v3.InceptionV3(weights='imagenet')\n",
"\n",
"resnet_model = resnet50.ResNet50(weights='imagenet')\n",
"\n",
"mobilenet_model = mobilenet.MobileNet(weights='imagenet')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PIL image size (224, 224)\n"
]
},
{
"data": {
"image/png": 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2d9x5RPBF1YebAHy0HsQAwH9R1f8uIp8F8GEReROAbwF43YLncRznGrHQS0FVvwng7yX6nwHwykWOXXj+mZ/Pk7mp3eYQ6ag/OtGMY7JXIn22z3kQKdYisiNQBqjeejMTDjarGbK/0fTt7afDnVNtjqNIVZPqakew4gBy26amMWtb1rlzHo2WzcQKGZ+1DMrH3aB+M4OS5u0ZqWNbnpe58+WqbB2KTcFxnL9d+EvBcZyIv3UBUfOmiI+DkgzPNSMhTiTaRSndx9BRehkrWnpkUZaOOxhQ6DG1R/U5dvZp6XCv2dFKxR7UBi4dbxWjnVxDx0SqKXHVWjqU0fTSYsn5UgZTa1urwpWlPsw8r6RF+DjFOyuJ08e0rtVSH1IlCMzyAonjwtUHx3EW5bqWFHK5AZdhaIRlXDT2jYSMVhiuNbuwZCLR0tu0cQ1ojJG7u81sJ/vNtpahMcyOVoEXHlPqvNYs18VvPzrGeNpIVhK3kAoBt74L1vLrLGOqdZ06Tqe1jw2Ns8O9mZK8mqnz5ZzvFsUlBcdxIvyl4DhOxMqoD6XiTypFuIUl1ubWbnuazlzEngZDEkV7ZIDq85BGw0hkFVIThmQkZLe7PmfuGTbbX73QGAQh05WV2L+BYfXgwoULAIDnnm1SW3AORtVgDEunBR9HMeU0HEprzuXVkagVOaL25lq/Ph+J6/3mWLEBd1pdGZJVlo3A4+G0wbQ+4KQ5oEvp0XPt6RD99K3E3iitPliZrJhgSGTjcaqmKdD2Qwj3iO/LtNGVv+qxYTd9LbNwScFxnAh/KTiOE7Ey6kPOirrMzDI5hmRBXiexTXqcIpxFRgpiGnJ0zaiVfDWdSSmin35PjxIrFyzNW+vY7NIc/BPsMvLT4nxJtaJcenHLym7dghyTbEPG5+YKRhT5RM1ITRmba/uWapAaW/u4qc+7hJ9fS1xScBwnYmUkhVUimnXpDg2Mt/yYDF5RuXLVaP/9q+QTwDNALz3Tsncke0Du7Q2nxhkVWSFvPfZYDEZFNj7yMVKFcWJjbT6sOSUpmB6LmcAmRhLn5nsdh1anJZ3xyAhxbqW5V8NAzfc18swsCA5LeTRahkamNKjK/RQcxzkw/KXgOE7E3OqDiPwdVLUdAi8C8O8AnATw8wCeqvvfpqqfKD3uvGm9l4o0ouGQRUtaDOdsS+ubTQakjTUSH0dj7A8bQx8b/dgXYGykeNcxu+hy/oVKjI1E4yGrF416wOpDaLOhMXc3raxIJbkMQq1LU33AtL8FLN+TlETfS7uBD7g2JauCdGg2JrP6p7BdmC3XaEuVSF23Vf/yoAyN8+w/90tBVb8K4E4AkMpsfRpVjsafA/C7qvrb8x7bcZzDY1nqwysBfENVv7Wk4zmOc0gsa/Xh9QA+QH+/RUTeCOBhAL+SKxmn0JXyU+hTGrQhucwOyQdhk1KhHT1+bNLe3moKvY72d3H58sXJ35e5rDurCST679M5hkoqAZWxH9VqBbu+5pKxVuOfVjuQsJZbpeotcn4KlvrQS7hrs0pgjXOyfS8tirP6EEej0moN+/+O2XdCTJWq5F5YPhJhpSGVVq7d7lIlayVXH0RkHcA/AfBf6653AXgxKtXiDIB3GvtNisHsXPFiMI6zKixDUng1gM+r6pMAEH4DgIi8G8Afp3biYjA3vfAWpf4lDGlBeJ17TG92DuY5sjVpHztxomlvN/0XL56H7lxKH5dns6imIM34JDXsUnJXaHWcAb3Sc8laq2NXx0v5CjA8u/LMho6zUW72SiVg1QI/hcl+xqwbCyvdMkdNnZclm0H6XrBnqdBzZT+Ewdra9DF6kmzzv8A44xUZzmf913D90lKWYVN4A0h1CEVgal4L4NElnMNxnGvEMorB/DiAX6Du3xKRO1G9vB5vfeY4zoqzaN2HywCe1+r7mYVGlD/npL1M4wrDwUdB7ANi4+LxE8cnbTY09vvxGvnV3XQKsDiFF6kSrD5QO6raFPwoaH2dfSC4nar2FOcvYMPe9DhzNQ5n9adIPbOSCkvsIzExShvHHY3SSVmt9qxKTvz3gAzQ1v5WMtb19apWhxVs1sXAfhD1Ixn3aHQcJ8JfCo7jRHiUZALKgoZNEhm3jhydtI8ca9SHtfWmoNiYC8cOR1GNhcgHYZQWWUeI1Y9JP6cCkDqfAoX3W7UechmaefVhNJoutmv5ClhYmbBTn6fEXEt94CzJYfzWsUpUhpnqgyF+W6qUJa6n6l3k8i20t8ltm8PLxjmOszArIykkZ43MW47f3JNsREZiVyvRZupYsbGoKXd+gvwRbrzxxmZnkg6+8/RTk/a5c+ciYyHD42EfhFEU89/ARqtQTHZnt5FCrPLx/UzmzlS+BMvTThO+Au12dB/rthn4kzBsjsnrkPfb3Gw8RcO9oFsVHSOqkWFUhZpVR6FnGAt3+XvD25AxOhgUAWBto5Eg+/U2lqSwywl+2Q+hvgc9Q0oLeTms/5V+JrlxCpcUHMeJ8JeC4zgRK6M+zEOXwBEm5e5qiao33HBDss1i5cVLTdDTpUuNW/PVq1ejPAgsvsYlz1ikHCf7ee09GBKttGo5A1YqaKn+Y/rzxP6zSBkarQCr4LpsGTOzhWc7GOrmJcqbYOVvoO/CGqkSqXRrXe/tQfnizMIlBcdxIvyl4DhOxHWtPiyj+m4QAyOX1K3GH4FVBl592CX/g2epDNtzzz03ae9c2cV4yBGLXCuCXFytSDbqH1I+haA2sDtzfwnqg8js7MpdV7yz1ahnjHFWO/t5gSo5V5oyjnqkNqubvPqQUh8sf4ou48y5Ppf4UMzCJQXHcSKua0khRYmXG5OSFI4dawKcjm41SVk5AOnS+QuT9rPPNNLB5YtNwpjhcBhV/rTWyaPxKK2xj9Nr7KG47f5u0ydGuaXUbLoMQ2NJboJUJqfoeInzWXJe6rlak+AsH4Tc9oDtg8EeJywRbG9vT9pbW00+jWjf2rDMz7FLgJa1bWqfrlJTG5cUHMeJuK4lhdRsZUkHlqdjeOPzG/4FL3jBpL2x1uiLly83UsC5c+cm7YsXmyXJyLNwPMaYbAHcthiTHUEpViI+zvTswTED1kyXynTURVJI5kksbE/GGc1cCd3Y2DY5O0p6TiuZgU3pRcT00BxTWPzaRiMpHDnaSAosNfD1BzsQLyNbtSlT3rfR9yoVgm/ZkUZ5u0WbIklBRB4QkbMi8ij1nRKRh0Tk6/XvG+p+EZHfE5HHROQLIvLSzqNyHOfQKFUf3gvgVa2++wB8UlVfAuCT9d9AlbPxJfXPvagSuTqOc51QpD6o6qdE5PZW990AfqxuPwjgfwL41br/fVrJQJ8WkZMicrOqnlnGgJkuy48plQEAjh6tlh9Pnjw56bvxVJNMilWGC882BsWLZGgcUjALxzjraBwtPcIQWUfsuTic9lwEgNFeIgFr8mitZKOGKjEZo3GMHPOqD9ExEuqKFbimaO7L5Dp66UCrruHSPMperxeni+dlyC1SGY4cSbZZDY29VmcvLaaMkkCjNvDnqdD4kkxQpSxiaLyJ/tG/A+Cmun0LgG/Tdk/UfY7jXAcsxdCoqioinSYdEbkXlXqBY8dOZLY2z5s7x6TNb3x+mwcJgUOh+biXLzRGxGeeeWbS5hgHK/fiaDSKHJOst/mIsrrskbPT3u50IZdqh+lU7Tzl5yQFe1ZBMbmaidUf08lQ4jHPNkRGhVxIyJokLOlP97X3M5fyDElhlqFx+zjl5TzaOLix85IVth2ui7+HVl1JHj8bJtvHardTfd3+KysWkRSeDOnc699n6/7TAG6j7W6t+yJU9X5VvUtV79o6st3+2HGcQ2KRl8LHAdxTt+8B8DHqf2O9CvFyAOcPwp7gOM7BUKQ+iMgHUBkVbxSRJwD8ewDvAPBhEXkTgG8BeF29+ScAvAbAYwCuoKpCfeDk/MGt0OggBh4n0fBZMi5yXMN5imvg3IvKPgQt0bVfYADjGpVsUGSDUiyKVr97hsrQJWYACVHUMvz1et2Mi7lciilTKX8aifuJ6+gbdSdLfFVmGWkt46zlucgqgWkorfv5e8gGb+5PeT1yX2p8XfI95ihdfXiD8dErE9sqgDd3HonjOCuBuzk7jhOxGm7Oml7CH2Paqh2t11IQ0GCtWiuO06U37cFWE+R04sbvmrSPnHh+dSw0oty5//u1SfvSs00i1v2dxjcBQ7YKU2ahVqLTq5xtCSQi0qrEHomZu1FZ+nTwTJMtigvWNklCyWsCpJlMxH8h9+AeZQpKBkwZWZEoNiwqihtRZ4uyyq/v7VfHjrIV0TlYfQKdY60u0joQdv0mP47dK9TeaYZPQ1sfNOdUCkJTHUX35CiFy69L8+/So0se76afk+41G4VVqDW6F2v0fdgYNM9vZ58KC9dtoRUqHsegrjK8r2lVs0/XUopLCo7jRKyGpCDN7BQH+UxjrXmH2SgyjNHUwEYiDo0OcIDThQuNRHDlSjPrWAVXYqNhnIeRxxMF1xh5Cy3mTTiSMvx1MUpa6dmtMacMe5bhb1DPjiXeiH0xfCESWD4UpTk9reOz4TCSYgqOE4yRlm+CVcAnFbiVur5UQpd5cUnBcZwIfyk4jhOxGuqDQUrEi8Tu/vQ7jUWyja3GH+HUqVOTNqsPOzuVIYpdmDkIisU6ay08csdtrU1bvhJWsEyX/HpWXoRO+RSI1PksI6Hly8CEZ2XXWpxWGfm+WPeu358dSGVWuLLup1CdSumZ98fKhcAqY86N2RpPiWo6GW4mm1Yq8K0LLik4jhPhLwXHcSJWQ33QtKU6m3OSNggiF68ysMrA+RKiKMhaVWC3ZRa/NqhIKO8XuTkbqkSv1ysqkT5L/SglW00JaVEzF2VntXMrHCWknrlVeWlzvVlvD6K4VVSXA3bNFRNJr/70+31TxLcqfHH/mlFsNnXvrWN0+Q6kjruoKuGSguM4EashKRjk3pIa+TRUb1d+O7N0wP3nz5+ftEPSVTYKcSYdK2jFmkHas5flw2AFO1lp4Of1U2BKpYJcUM+ssaX6LSlmrNOGT55pjxxpDMWb643EFsYUjMQAMByRoW7XqF0ZtSfNKemEnzNT4udg5syox2zN3Dl/itz9tq7TupZZuKTgOE6EvxQcx4lYGfUhJyYlg3WovblRiZqcJouNhGwYZPUhiKAstkoiSAhIp8Zqj7ndz+oA72+tS+fcuNv9s/raxyupXdjGUh/M8vKZZxat3cvaVN/WVvPM+FkOKHdCeGaK9NisMccGSLuUfBRopizK530zLPUvPGPrObGa2iXdWkodiZ7BwAOiHMdZkOxLQdKFYP6DiPx1XezloyJysu6/XUR2ROSR+ucPDnLwjuMsnxL14b0Afh/A+6jvIQBvVdWhiPwmgLeiqvkAAN9Q1TuXOkoiEkVJ7As+CVw6nkU5dl3mEu4BFjn5plhiqVUotC0+82fWioMl1lv1B8I5LFWjpO6BNd52H1OifuQiV5mQC4DFdVbjoqhSSud8tc6XwKsPdjk2cmE23J/5/INB37TYl9RTsO53diXG+A6lXNNTKoOVgXqeiMmspKCqnwJwrtX3p6oavtWfRpWx2XGcvwUsw9D4LwB8iP6+Q0T+EsAFAP9WVf93aiehug9Hj51IrremahmkYtQB4ESdIYeNUxcuNTUbuE4DT1zhrcrSA7+ILc/DXGn7cGyr8k9JRZ/c2z8y/CVHk565cufLVZWaOncmN4Tl35Ebb2SkpQxKQerjXBdxXYx0klPG8lPo9/um34Umvo/ttnUtqeuOslCRpJMLpEqd28rT0N20vOBLQUR+DcAQwPvrrjMAvltVnxGRHwTwRyLy/ap6ob2vqt4P4H4AeMFNt8xRssJxnINg7tUHEflZAP8YwE/XGZyhqruq+kzd/hyAbwD43iWM03Gca8RckoKIvArAvwHw91X1CvU/H8A5VR2JyItQVZ7+ZskxU3nxU2I+r+d+1y1NicpgoOJUahcvNypDlMyy3xizgljGBq69HSoqy8e72KgjVjAOqyE7OzuRcZFFRGs9m8m5FceGqvTafCofQi5Iq6uaZKk8od9y437ufHWf2Z9kc7N5vpHrLye3re9xfN84zVv6a93rp92pud3r9eyUcMaav50vYjqwylJHrdoRKf+GVFo/65mhuhJKAAAWmklEQVSvH5lOPZgj+1KQdCGYtwLYAPBQPdhPq+ovAngFgF8XkX1UT+kXVfVc8sCO46wk2ZeCUQjmPca2HwHwka6DEBjFTzFtVLTKfof9eWa2kmGKTBu+eAbbJe9Ha3a3wp3bs4w1M5TMLiXGrC7bprxGc593Db1NLeFaYxuPq/vJOVBV05mXxonlYN7WgqUDyxgXS1PZQ9bnzi9Pdgl9XzT7lpmFypBoZ+EejY7jRKxM7EOAZ9begMNoKwnhBBXn4JqQw7poBuv03I5nv+nZm2Mjdmipi/stycNyatrf3zf3YbosQ7bbk22NuopMSq/N5TC0dF2rvWh8hWnPSNgPrKVTCmswdfHgOFX1x/e/RAroSk5Ks5YvS6UNy8kOg3S8zixcUnAcJ8JfCo7jRKyE+qBoxKcojJaMikFtYPWBCWI6qwxWunDuD4ZEy4++pHLPrPBoK123nb48n1I9HTo91QUgLX5aInEXb8QSo2lO9O31pmMjIkOtNveVop4Nz8v0NVnqmHVvZ1GSbaoLy9gvpa5ZqeNLcUnBcZwIfyk4jhOxEuoDw74HHAYdqjqxRyN7LO5ercT0kixGkU9CrW7wKsPYCGu22lZC1vbqQ0kVoy5ibfx5XhTtokqkxtNl1YKPbQYMaSIEvEf3iErE9zCtPkWxc9ExrHs7PbapMbXFceEVkLQqtQw/hS6rHF3C3feH3VeDXFJwHCdiJSSFnsjE54ANicePH5+0g4RgJU4JfgrWOnk8mzdGsmBgjEqLz/BQTB1vVur2dnGYXLvEgJUsAJLcMj2jdcmDWdLOpS23ru/q3m49djKcjfKza+o6It//KFFLPs9ldJ+l+8xaIhHk/BS6GH9zSVgW9a1wScFxnAh/KTiOE7ES6kN/0J9Uc2KVgY2OQXS3/Al0PDswhLe9cmU64CkKXDKCqiyfhRJfBqBMfWC6GKpKDH9hPDmVoIsREbDF8uAibWcFqo5t+XJE9y9hVLWug43R7M7MLtu9yBc6djG2A7iW6/5c4s6cUulS6liJmleKSwqO40T4S8FxnIiSJCsPoEq7dlZV/27d93YAPw/gqXqzt6nqJ+rP3grgTQBGAP6Vqv5J7hz9Xn/ih8D5EljUTLkjR27DmE7LbakP7AodVg5YzCpRGazcCrNUhpwIOIucj4EUiPwpcpZsK7LTOkZKVbDStq+PGjE/db5czgnrHrKawOpDtD2t17TPYj2nglsxN/NGZubUh4MqRf9eAK9K9P+uqt5Z/4QXwvcBeD2A76/3+U/CGU0cx1l5SjIvfUpEbi883t0APqiquwD+RkQeA/AyAP9n1k7S72OzlhRG9Ga7fHE6PyJLCttbjVSRCojavUJSBfXL/u5UP0sEJbO4ZdAcjdh4JK1j9aPPmn2Mt7xMF4ABgHG9/Zhnrj6Pk9uZWoO87WRM3JdPSx8FGgnV4aylt9GQvEn3aV6u/UWEvO567KdAQldUsKW+j5xDIion3+cx8LWQQTQacyyxxAFH7IU6n9coY83olrdrkHpy30lTaprDHrqITeEtUpWNe0BEgj/yLQC+Tds8UfdNISL3isjDIvLwpYvnU5s4jnMIzPtSeBeAFwO4E1Wth3d2PYCq3q+qd6nqXUePpcOhHce59szlp6CqT4a2iLwbwB/Xf54GcBttemvdNxNO3MrqwWWq6hQMhVaATjD8sfrAQU7cn0uRNisRa2qbWUZHa42emdfQ2Noge4wu50ttWxL4kwqastbjU8l6+RxsoOT9UsZhThO/qEF3nnRo15IuruvzMJekICI305+vBRAqUn8cwOtFZENE7kBV9+EvFhqh4zjXlHnrPvyYiNyJajXncQC/AACq+iUR+TCAL6MqJ/dmLcnD7TjOyrDUug/19r8B4De6DEJVJyIhV2G6RO0gErGYmPI94KKjOxRFydvmCn+WrJVbkZFt8dlKAVaEIbkmx1Qg5pbmZyhZ57aiUXN1H6JtE27X1v1KqXS8LWf27ury21YXrO/HIu7oKTHfIqW+dFnViFWJ7OmmcI9Gx3EiViIgajQeT2o28kyfykXAfbs7jSExGBXZoGglYM29dUtmQTNwh+j3+2ZtTIt4ZrPKqE+//keGWJFP+FqeT8GiS3Wj1PGOHj2a3MfyXg1er9vb25M+qz5F16xIbUNjyT5dmGc8Xc8dP7/uY3ZJwXGcCH8pOI4TsRLqw3A4xLlz08WpUyKhVd4tqAqW0Y/JGXKsICDLN8FKaCoipstsUZozSa+xpwxeOi4PXMqd2xJxuxrawnEs4+H2VuW0FoLhgPj5nT17NnnckLKPjYv8vVikKO5h+SXkVLYufgieT8FxnKXiLwXHcSJWQn3Q8XhiaWaRkMXAVD6FVEVoS6zvUonZ8lNoZ2pObdPrxanJ4tJ1+Sjykrj4VN6DcQcpsYt4WaLm5PIesLsyq4TPe16lBnDaPc7QbeVhCKsPfE/4ebAvyzwrCV1XCOZ1G7f6u6h0qRR6rj44jrNUVkJSgKSNQzwzB6kgJR0AzUwRGRqN2ZxJ9VtBV9GQjcCdwaCJzWdPzfZxS2bg3Fuex26t01vbz3O+EkkhlQGJE6lyO8zo7Jty/nw6jJ4lyIB1b7tKB+3+eX0JZu3b5d7mErOm9ls02xLjkoLjOBErISmI9CYziJVXMbQtm0HQ3SMfecM2kNPZeNa1qkKxjsuSwtpao8+ORqOZy5W5tkUqVXvfmClzYbbzUrSkWo8pzpnYtMP9fPrppyd9zz777KTNUgXbHYI0yPfWCrPuKjW07/+iYcildPk+dPFCVfVako7jLIi/FBzHiVgN9QGNOM6GRG4H9cFSL0J/tHRnGN9y4qWlMrBYxkE8nJae1YcrV660DJVpA6aZmcjwaEwdA9HS53xLUqnU6VFVJaOqlaWahWdqGW5Pn66ScvFzZDXAWnIMWB6mrHZYWMa4WeqDFUZtXR+PL7RLKoOlMlHlVDTrOVvZvmaRlRSkSsx6VkQepb4Picgj9c/jIvJI3X+7iOzQZ3/QeUSO4xwqJZLCewH8PoD3hQ5V/WehLSLvBMDrSN9Q1Tu7DEJVJ8Yjy0EovGmtkOVkiGkH6YCxlnesJUBu80y3sbHR8s9vZkTGNIZhtsGwxIEod62pmYtnF+s6mZEhpYQZ25rxw7POGYHbhO1LntMy6VoLlMktHc675JiS7rqOrc1CdR+kGunrAPzDzmd2HGclWdTQ+KMAnlTVr1PfHSLylyLyv0TkRxc8vuM415hFDY1vAPAB+vsMgO9W1WdE5AcB/JGIfL+qXmjvKCL3ArgXAI4eOznJmGRlTgpipxXinFIfepEmMW+Ou7T/QomIvb6+Hq2v7+2lPSxN1UbSqc9TfVaa9NT2ligaxm7FKlgxBfycUnkT+Tlx7Ep41pZh0zKS5bxQc9vO6hcR8/5ZRtCUcRVIq7pd40cWVR8OxNBoISIDAP8UwIdCn6ruquozdftzAL4B4HtT+3MxmK0j26lNHMc5BBZRH/4RgL9W1SdCh4g8X+pQQBF5Eaq6D99cbIiO41xL5qr7oKrvQVVd+gOtzV8B4NdFZB9VedBfVNXplEotdDxOBzyRWBpEUCt5atrfYL5kpl0DXGatiJT4CVjXZKkPOZHQGn/Sl4PaQTw2S8cXrP+nKmKxesHBT2H1gY9rib6p67BE6pKQeYtZz8vy2ShRCXIJWFN+GHy8LqHVUXuOaX/eug9Q1Z9N9H0EwEe6D8NxnFVhJTwax6oToxPPKiOjRmMgm058zvFYnmuWkZONo1yKfjgcRtdjnaNkHClpIprV+ouvlaeMVjlPOiCWLJgw+7F0cInqg6aOW1I8pzR1fMl+7fOIiDkG6zq7SA0ltUlzdPFSnSegy2MfHMeJ8JeC4zgRK6E+qOpEzLbKwKcMNSwYLTNXQAnWOIfDOJiqS2WqWWSNVnQzugREpVQiS2ViNanE5Tlsz3kX2aC2tbE2dSxLFE/dO9NAa1CiCg0Gg6SxtN22jmsZRwOpIClr25Kxlxoiu+CSguM4Ef5ScBwnYmXUhyBWdlljTom5kehk+CmUjCeFJc7GYmAs0ubUoVnnEGP9JCkqG+oDE+5XLl+EFQ3KqpDl2s3bhJUGVjtYvN6o1YecyG31l0S+lhyv7Rth+WmU5GCw/CxSKp+1ytXlHDk3Z1cfHMdZmJWQFIDyteect2HOm8/avssMNYtZ6cK7zmyWpJCcdZCedSzPy1nntmYw9rlgiYADpbg/BD9ZWZHW1gZTY5xXUuBrS83Qs47Xnt2tYLcuAUxWm8/FUuRBSQrz5FNwScFxnAh/KTiOE7ES6oOoolev72fFMqrHOBpOu4aykNnFzGipHZb7aamItrbGhqqm33Jxjeta7CW3T+YToPe7KIm5I2qH3ywGR4OqxqqcE2DYuCiz6N+nmplXrzQ5EtileVSrEmtcI6PPOTCq/nlVNOu7YuWWsIyAbUOn6c4+TudTWBuQ+/OA7h2dOzzX3X2uZUL3YpxW2fr98L0nNZCM2f26zYFP/QEFh2mj2pXikoLjOBH+UnAcJ2Il1If/HykRay1S4nOJxT23wpOLkrQi/KxcALkCsykXXWu8XbJxXwui+4b0PeJxhlWZVArBdjt1nviZ0AaZ0nz9wRa6UlL34TYR+TMR+bKIfElEfqnuPyUiD4nI1+vfN9T9IiK/JyKPicgXROSlnUflOM6hUSIpDAH8iqp+XkSOAficiDwE4GcBfFJV3yEi9wG4D8CvAng1qjRsLwHwQwDeVf9eiJw/wTzHutbkPA1nbZ8yTJZkoUoZUNvJZQMh0Sp79FljsAr9ppKbxgV415L75cbeJfHuMoODGJYI+kb1Li7omvLrGA4bPw6LnH9DlEYjVOFiSYGe6fETJ7Pna5OVFFT1jKp+vm5fBPAVALcAuBvAg/VmDwL4qbp9N4D3acWnAZwUkZs7j8xxnEOhk6FRRG4H8AMAPgPgJlU9U3/0HQA31e1bAHybdnui7nMc5zqg2NAoIkdR5V/8ZVW90HL5VOFIoLLjTeo+HDlylPv5uFP7dRH95932WqgXJbH9uXRdXUXtILpzQdzjx49P2qFoLqsUlmuzVZ+jJK9BIBWgVaIS5XIIlDBLxTCfDdmA+4N0fzufRmB/f3fqOiyDbsoHxv6+VCe3SheevOEGdKVIUhCRNVQvhPer6h/W3U8GtaD+fbbuPw3gNtr91rovgus+bGx0t5A6jnMwlKw+CID3APiKqv4OffRxAPfU7XsAfIz631ivQrwcwHlSMxzHWXFK1IcfBvAzAL4odcl5AG8D8A4AHxaRNwH4FqpCswDwCQCvAfAYgCsAfm6pI14Ci6ol87JMSzhQ5qfA5wnRjKwynDhxYtLe3q4qdbH6cOFCU/EvVy8CSLtssxjN7S71K1JYInfJNrPaZm6CgojJ2JdjukygOR5znCGSNP1Mw7br640789ZWox5uHz+GrpTUffhzwMyW/srE9grgzZ1H4jjOSrDSHo2pt3Fu9ijx5pv3GIuyLGNYmGFL6lOkjpfytAOa/AdsROS2dVxrxs8ZHcPnOb+KNl2SlS5VUuC5kQKYRmNKxkoBTzpqrj/4OPTIv4F9HbgasrDUMwkEpHHQ7V7bqKS6re1GOjhGkuDG1ia64rEPjuNErJykUJLdZplcS+/GRXz8u9gjcrMt6/1cuzMsLfIMb2UeKtOppyWFVOahzhmpMjETXZb6Um3rM0V6iZilLW6nQrijMVOt0F4vnacyZSsZ9BsbzuZGJSEcP9Z4Lp664VSz/1r3f3GXFBzHifCXguM4ESunPiyTeY2Ly1guTFGSxLWL+sB9LOZb+4WlRl5y5P1Shr+SZb+c+mAdrzSRrNUuMSiWqA/t45gp0vnZcKUnVhk4UIyuZeIBGX218suoybFTdqf1zWopkg2N28caD+GhJ251HGdRVkJSUG0MN11m6ZTDTonvfWrmspb05s0fGNpdEp60yc1+/DkbvqxQ5VQpdb5f4RxWivOSvJLcDttYBXFCu+tyYuqZManw7XbbKvLSPiY/p+GwWW4cjbiIDhfPMYrB9KaXg0tS1E+Wn2lJ8ujRZsnxhS/8LgDAqVM30qi7Je1p45KC4zgR/lJwHCdiJdQHyHzGvVyp+mV4MZaoEqW+DiXXmPPiA9LhtJZ3Y0pMztUaLDGIWv4IqbYVAp0TbXOqRIlBsWsWprbKF6lo+2REZJWIw9p76XvXr+ffsfV55A9Cz6xOHx+yYgFx7MpmbWAUMj7yt5GNyqW4pOA4ToS/FBzHiVgN9QHdXHcDqZWGknLvuVTni/gpzFJZFjluzvqeCpiy2iUqSiC3clCyjfVMUqshJapYapWkxIW5RJVon5/Hvj9MB4eNKVlrT3kVa3rOtdScwWA69T3QiP83UAalU89vVhq2j1Sh0YN+s38XFS2FSwqO40SsjKQwD8tMEW6xaIBW1zF0CR1mctJBuz1rTNxnSQEpfwRr+5yX5ry+HCV+ByUGyPY5zdyQFC6NkueE6eOIkRqeYd+QkPjmxKlGUjh2tEmMM7luMmAO+mnv1lJcUnAcJ8JfCo7jRMi1zCdgDkLkKQCXATx92GNZgBtxfY8fuP6v4XofP3Cw1/A9qvr83EYr8VIAABF5WFXvOuxxzMv1Pn7g+r+G6338wGpcg6sPjuNE+EvBcZyIVXop3H/YA1iQ6338wPV/Ddf7+IEVuIaVsSk4jrMarJKk4DjOCnDoLwUReZWIfFVEHhOR+w57PKWIyOMi8kUReUREHq77TonIQyLy9fp395K/B4iIPCAiZ0XkUepLjlkqfq9+Ll8QkZce3sgnY02N/+0icrp+Do+IyGvos7fW4/+qiPzk4Yy6QURuE5E/E5Evi8iXROSX6v7VegYhfvwwfgD0AXwDwIsArAP4KwDfd5hj6jD2xwHc2Or7LQD31e37APzmYY+zNb5XAHgpgEdzY0ZVD/S/oUo1+nIAn1nR8b8dwL9ObPt99fdpA8Ad9fesf8jjvxnAS+v2MQBfq8e5Us/gsCWFlwF4TFW/qap7AD4I4O5DHtMi3A3gwbr9IICfOsSxTKGqnwJwrtVtjfluAO/Tik8DOCkiN1+bkaYxxm9xN4APququqv4NqoLHLzuwwRWgqmdU9fN1+yKArwC4BSv2DA77pXALgG/T30/UfdcDCuBPReRzInJv3XeTqp6p298BcNPhDK0T1pivp2fzllq8foBUtpUev4jcDuAHAHwGK/YMDvulcD3zI6r6UgCvBvBmEXkFf6iV/HddLe1cj2MG8C4ALwZwJ4AzAN55uMPJIyJHAXwEwC+r6gX+bBWewWG/FE4DuI3+vrXuW3lU9XT9+yyAj6ISTZ8M4l39++zhjbAYa8zXxbNR1SdVdaSqYwDvRqMirOT4RWQN1Qvh/ar6h3X3Sj2Dw34pfBbAS0TkDhFZB/B6AB8/5DFlEZFtETkW2gB+AsCjqMZ+T73ZPQA+djgj7IQ15o8DeGNtAX85gPMk4q4MLR37taieA1CN//UisiEidwB4CYC/uNbjY6RKoPAeAF9R1d+hj1brGRymNZYsrF9DZR3+tcMeT+GYX4TKsv1XAL4Uxg3geQA+CeDrAP4HgFOHPdbWuD+ASsTeR6WfvskaMyqL93+sn8sXAdy1ouP/z/X4voDqn+hm2v7X6vF/FcCrV2D8P4JKNfgCgEfqn9es2jNwj0bHcSIOW31wHGfF8JeC4zgR/lJwHCfCXwqO40T4S8FxnAh/KTiOE+EvBcdxIvyl4DhOxP8DpPwSINw/1IIAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"numpy array size (224, 224, 3)\n",
"image batch size (1, 224, 224, 3)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x133d14d30>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from keras.preprocessing.image import load_img\n",
"from keras.preprocessing.image import img_to_array\n",
"from keras.applications.imagenet_utils import decode_predictions\n",
" \n",
"filename = 'masina_0.png'\n",
"# incarc imaginea in format PIL\n",
"original = load_img(filename, target_size=(224, 224))\n",
"print('PIL image size',original.size)\n",
"plt.imshow(original)\n",
"plt.show()\n",
" \n",
"# convertesc PIL-ul in numpy array \n",
"# in PIL imaginea e (latime, inaltime, channel)\n",
"# in numpy imaginea este (inaltime, latime, channel)\n",
"numpy_image = img_to_array(original)\n",
"plt.imshow(np.uint8(numpy_image))\n",
"plt.show()\n",
"print('numpy array size',numpy_image.shape)\n",
" \n",
"# Convertesc imaginea in format batch\n",
"# adaug o dimensiune pe axa 0 a matriciiimage_batch = np.expand_dims(numpy_image, axis=0)\n",
"image_batch = np.expand_dims(numpy_image, axis=0)\n",
"print('image batch size', image_batch.shape)\n",
"plt.imshow(np.uint8(image_batch[0]))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[('n03344393', 'fireboat', 0.1952117), ('n09288635', 'geyser', 0.031433046), ('n02860847', 'bobsled', 0.02839521), ('n01484850', 'great_white_shark', 0.025608802), ('n01737021', 'water_snake', 0.022196839)]]\n"
]
}
],
"source": [
"processed_image = vgg16.preprocess_input(image_batch.copy())\n",
"predictions = vgg_model.predict(processed_image)\n",
"# top 5 predictii - este valoarea default\n",
"label = decode_predictions(predictions)\n",
"print (label)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hehe! Zice ca in imagine este un firebot! Cool!<br>\n",
"Hai sa le vedem si pe alelalte:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[('n03485407', 'hand-held_computer', 0.27265123), ('n03793489', 'mouse', 0.10449034), ('n02783161', 'ballpoint', 0.09948672), ('n02992529', 'cellular_telephone', 0.051670115), ('n04074963', 'remote_control', 0.050274193)]]\n"
]
}
],
"source": [
"processed_image_1 = inception_v3.preprocess_input(image_batch.copy())\n",
"predictions_1 = inception_model.predict(processed_image_1)\n",
"label_1 = decode_predictions(predictions_1)\n",
"print (label_1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hai ca-i funny!"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[('n04141327', 'scabbard', 0.21076545), ('n03481172', 'hammer', 0.06233157), ('n03786901', 'mortar', 0.04946894), ('n03498962', 'hatchet', 0.049148425), ('n04208210', 'shovel', 0.046690773)]]\n"
]
}
],
"source": [
"processed_image_2 = resnet50.preprocess_input(image_batch.copy())\n",
"predictions_2 = resnet_model.predict(processed_image_2)\n",
"label_2 = decode_predictions(predictions_2)\n",
"print (label_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Marturisesc ca nu stiam ca 'scabbard' inseamna 'teaca'!<br>\n",
"Hai sa vedem si ultimul model:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[('n04525038', 'velvet', 0.58461165), ('n01930112', 'nematode', 0.21978034), ('n02999410', 'chain', 0.028027471), ('n03658185', 'letter_opener', 0.012321507), ('n02783161', 'ballpoint', 0.0117406985)]]\n"
]
}
],
"source": [
"processed_image_3 = mobilenet.preprocess_input(image_batch.copy())\n",
"predictions_3 = mobilenet_model.predict(processed_image_3)\n",
"label_3 = decode_predictions(predictions_3)\n",
"print (label_3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mdap, total aiurea!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Csf, n-am csf decat s-o apuc pe hard way!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... desi, poate, ar fi o idee buna sa re-trainuiesc modelele astea celebre (ca nu degeaba sunt celebre, ci pentru ca sunt bune) pe imaginile astea decupate. <br>\n",
"As putea avea surprize placute!<br>\n",
"Pana data viitoare, sa fiti cuminti si sa veniti cu idei mai bune ca ale mele!"
]
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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