Dodanie wersji algorytmu z wyuczonym modelem.

Reviewed-on: #2

Biedap/algorytm_uczenia.py

@@ -0,0 +1,21 @@
+#%%
+
+from detecto import core, utils, visualize
+import os
+
+os.chdir(r"C:\Users\pittb\Documents\biedronkapp\BiedronkApp\biedronki_dataset")
+dataset = core.Dataset('images/')
+
+model = core.Model(['azjatycka','siedmiokropka'])
+
+model.fit(dataset)
+# %%
+image = utils.read_image('images/4733127105_0a658bd317_c.jpg')
+predictions = model.predict(image)
+# %%
+labels, boxes, scores = predictions
+print(labels) 
+print(boxes)
+print(scores)
+visualize.show_labeled_image(image, boxes, labels)
+# %%

Biedap/igiveup.ipynb

@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9083ebd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m \n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/pyplot.py:49\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcycler\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cycler\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolorbar\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimage\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/colorbar.py:21\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpl\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api, collections, cm, colors, contour, ticker\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01martist\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmartist\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpatches\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpatches\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/contour.py:13\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpl\u001b[39;00m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api, docstring\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MouseButton\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpath\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpath\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mticker\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mticker\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py:46\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpl\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     47\u001b[0m     _api, backend_tools \u001b[38;5;28;01mas\u001b[39;00m tools, cbook, colors, docstring, textpath,\n\u001b[1;32m     48\u001b[0m     tight_bbox, transforms, widgets, get_backend, is_interactive, rcParams)\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pylab_helpers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Gcf\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_managers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ToolManager\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/widgets.py:22\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api, backend_tools, cbook, colors, ticker\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlines\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Line2D\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpatches\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Circle, Rectangle, Ellipse\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TransformedPatchPath\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mLockDraw\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/patches.py:4143\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m   4138\u001b[0m         \u001b[38;5;124;03m\"\"\"Return the `.Bbox`.\"\"\"\u001b[39;00m\n\u001b[1;32m   4139\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m transforms\u001b[38;5;241m.\u001b[39mBbox\u001b[38;5;241m.\u001b[39mfrom_bounds(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y,\n\u001b[1;32m   4140\u001b[0m                                            \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n\u001b[0;32m-> 4143\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mFancyArrowPatch\u001b[39;00m(Patch):\n\u001b[1;32m   4144\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4145\u001b[0m \u001b[38;5;124;03m    A fancy arrow patch. It draws an arrow using the `ArrowStyle`.\u001b[39;00m\n\u001b[1;32m   4146\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4149\u001b[0m \u001b[38;5;124;03m    does not change when the axis is moved or zoomed.\u001b[39;00m\n\u001b[1;32m   4150\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m   4151\u001b[0m     _edge_default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:119\u001b[0m, in \u001b[0;36mArtist.__init_subclass__\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.set\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_set_signature_and_docstring\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:147\u001b[0m, in \u001b[0;36mArtist._update_set_signature_and_docstring\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m__signature__ \u001b[38;5;241m=\u001b[39m Signature(\n\u001b[1;32m    138\u001b[0m     [Parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, Parameter\u001b[38;5;241m.\u001b[39mPOSITIONAL_OR_KEYWORD),\n\u001b[1;32m    139\u001b[0m      \u001b[38;5;241m*\u001b[39m[Parameter(prop, Parameter\u001b[38;5;241m.\u001b[39mKEYWORD_ONLY, default\u001b[38;5;241m=\u001b[39m_UNSET)\n\u001b[1;32m    140\u001b[0m        \u001b[38;5;28;01mfor\u001b[39;00m prop \u001b[38;5;129;01min\u001b[39;00m ArtistInspector(\u001b[38;5;28mcls\u001b[39m)\u001b[38;5;241m.\u001b[39mget_setters()\n\u001b[1;32m    141\u001b[0m        \u001b[38;5;28;01mif\u001b[39;00m prop \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m Artist\u001b[38;5;241m.\u001b[39m_PROPERTIES_EXCLUDED_FROM_SET]])\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m_autogenerated_signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__doc__\u001b[39m \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSet multiple properties at once.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    146\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSupported properties are\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 147\u001b[0m     \u001b[38;5;241m+\u001b[39m \u001b[43mkwdoc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:1749\u001b[0m, in \u001b[0;36mkwdoc\u001b[0;34m(artist)\u001b[0m\n\u001b[1;32m   1731\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1732\u001b[0m \u001b[38;5;124;03mInspect an `~matplotlib.artist.Artist` class (using `.ArtistInspector`) and\u001b[39;00m\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;124;03mreturn information about its settable properties and their current values.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;124;03m    use in Sphinx) if it is True.\u001b[39;00m\n\u001b[1;32m   1745\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m ai \u001b[38;5;241m=\u001b[39m ArtistInspector(artist)\n\u001b[1;32m   1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(ai\u001b[38;5;241m.\u001b[39mpprint_setters_rest(leadingspace\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m   1748\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdocstring.hardcopy\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[0;32m-> 1749\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProperties:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[43mai\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpprint_setters\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleadingspace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m))\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:1509\u001b[0m, in \u001b[0;36mArtistInspector.pprint_setters\u001b[0;34m(self, prop, leadingspace)\u001b[0m\n\u001b[1;32m   1506\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (pad, prop, accepts)\n\u001b[1;32m   1508\u001b[0m lines \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m-> 1509\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m prop \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_setters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[1;32m   1510\u001b[0m     accepts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_valid_values(prop)\n\u001b[1;32m   1511\u001b[0m     name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maliased_name(prop)\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/artist.py:1435\u001b[0m, in \u001b[0;36mArtistInspector.get_setters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1432\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m   1433\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mo, name)\n\u001b[1;32m   1434\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m callable(func)\n\u001b[0;32m-> 1435\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[43minspect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mparameters) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m   1436\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_alias(func)):\n\u001b[1;32m   1437\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m   1438\u001b[0m setters\u001b[38;5;241m.\u001b[39mappend(name[\u001b[38;5;241m4\u001b[39m:])\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:3113\u001b[0m, in \u001b[0;36msignature\u001b[0;34m(obj, follow_wrapped)\u001b[0m\n\u001b[1;32m   3111\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msignature\u001b[39m(obj, \u001b[38;5;241m*\u001b[39m, follow_wrapped\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m   3112\u001b[0m     \u001b[38;5;124;03m\"\"\"Get a signature object for the passed callable.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 3113\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_callable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_wrapped\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_wrapped\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:2862\u001b[0m, in \u001b[0;36mSignature.from_callable\u001b[0;34m(cls, obj, follow_wrapped)\u001b[0m\n\u001b[1;32m   2859\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m   2860\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_callable\u001b[39m(\u001b[38;5;28mcls\u001b[39m, obj, \u001b[38;5;241m*\u001b[39m, follow_wrapped\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m   2861\u001b[0m     \u001b[38;5;124;03m\"\"\"Constructs Signature for the given callable object.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 2862\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_signature_from_callable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msigcls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2863\u001b[0m \u001b[43m                                    \u001b[49m\u001b[43mfollow_wrapper_chains\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_wrapped\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:2325\u001b[0m, in \u001b[0;36m_signature_from_callable\u001b[0;34m(obj, follow_wrapper_chains, skip_bound_arg, sigcls)\u001b[0m\n\u001b[1;32m   2320\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m sig\u001b[38;5;241m.\u001b[39mreplace(parameters\u001b[38;5;241m=\u001b[39mnew_params)\n\u001b[1;32m   2322\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isfunction(obj) \u001b[38;5;129;01mor\u001b[39;00m _signature_is_functionlike(obj):\n\u001b[1;32m   2323\u001b[0m     \u001b[38;5;66;03m# If it's a pure Python function, or an object that is duck type\u001b[39;00m\n\u001b[1;32m   2324\u001b[0m     \u001b[38;5;66;03m# of a Python function (Cython functions, for instance), then:\u001b[39;00m\n\u001b[0;32m-> 2325\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_signature_from_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43msigcls\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2326\u001b[0m \u001b[43m                                    \u001b[49m\u001b[43mskip_bound_arg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_bound_arg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2328\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _signature_is_builtin(obj):\n\u001b[1;32m   2329\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _signature_from_builtin(sigcls, obj,\n\u001b[1;32m   2330\u001b[0m                                    skip_bound_arg\u001b[38;5;241m=\u001b[39mskip_bound_arg)\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:2196\u001b[0m, in \u001b[0;36m_signature_from_function\u001b[0;34m(cls, func, skip_bound_arg)\u001b[0m\n\u001b[1;32m   2194\u001b[0m kind \u001b[38;5;241m=\u001b[39m _POSITIONAL_ONLY \u001b[38;5;28;01mif\u001b[39;00m posonly_left \u001b[38;5;28;01melse\u001b[39;00m _POSITIONAL_OR_KEYWORD\n\u001b[1;32m   2195\u001b[0m annotation \u001b[38;5;241m=\u001b[39m annotations\u001b[38;5;241m.\u001b[39mget(name, _empty)\n\u001b[0;32m-> 2196\u001b[0m parameters\u001b[38;5;241m.\u001b[39mappend(\u001b[43mParameter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mannotation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mannotation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2197\u001b[0m \u001b[43m                            \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   2198\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m posonly_left:\n\u001b[1;32m   2199\u001b[0m     posonly_left \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.9/inspect.py:2518\u001b[0m, in \u001b[0;36mParameter.__init__\u001b[0;34m(self, name, kind, default, annotation)\u001b[0m\n\u001b[1;32m   2515\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname must be a str, not a \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(name)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m   2516\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m-> 2518\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m \u001b[38;5;129;01mand\u001b[39;00m name[\u001b[38;5;241m1\u001b[39m:]\u001b[38;5;241m.\u001b[39misdigit():\n\u001b[1;32m   2519\u001b[0m     \u001b[38;5;66;03m# These are implicit arguments generated by comprehensions. In\u001b[39;00m\n\u001b[1;32m   2520\u001b[0m     \u001b[38;5;66;03m# order to provide a friendlier interface to users, we recast\u001b[39;00m\n\u001b[1;32m   2521\u001b[0m     \u001b[38;5;66;03m# their name as \"implicitN\" and treat them as positional-only.\u001b[39;00m\n\u001b[1;32m   2522\u001b[0m     \u001b[38;5;66;03m# See issue 19611.\u001b[39;00m\n\u001b[1;32m   2523\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kind \u001b[38;5;241m!=\u001b[39m _POSITIONAL_OR_KEYWORD:\n\u001b[1;32m   2524\u001b[0m         msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   2525\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimplicit arguments must be passed as \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   2526\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpositional or keyword arguments, not \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   2527\u001b[0m         )\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Flatten, Dropout, Activation, Conv2D, MaxPooling2D\n",
+    "\n",
+    "# Input data files are available in the \"../input/\" directory.\n",
+    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
+    "\n",
+    "import os\n",
+    "print(os.listdir(\"Biedap/input\"))\n",
+    "\n",
+    "import zipfile\n",
+    "\n",
+    "with zipfile.ZipFile(\"Biedap/input/train.zip\",\"r\") as z:\n",
+    "    z.extractall(\".\")\n",
+    "    \n",
+    "with zipfile.ZipFile(\"Biedap/input/test1.zip\",\"r\") as z:\n",
+    "    z.extractall(\".\")\n",
+    "\n",
+    "# Any results you write to the current directory are saved as output.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1a09825",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "main_dir = \"Biedap\"\n",
+    "train_dir = \"train\"\n",
+    "path = os.path.join(main_dir,train_dir)\n",
+    "\n",
+    "for p in os.listdir(path):\n",
+    "    category = p.split(\".\")[0]\n",
+    "    img_array = cv2.imread(os.path.join(path,p),cv2.IMREAD_GRAYSCALE)\n",
+    "    new_img_array = cv2.resize(img_array, dsize=(80, 80))\n",
+    "    plt.imshow(new_img_array,cmap=\"gray\")\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9248ca56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = []\n",
+    "y = []\n",
+    "convert = lambda category : int(category == 'dog')\n",
+    "def create_test_data(path):\n",
+    "    for p in os.listdir(path):\n",
+    "        category = p.split(\".\")[0]\n",
+    "        category = convert(category)\n",
+    "        img_array = cv2.imread(os.path.join(path,p),cv2.IMREAD_GRAYSCALE)\n",
+    "        new_img_array = cv2.resize(img_array, dsize=(80, 80))\n",
+    "        X.append(new_img_array)\n",
+    "        y.append(category)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e9925cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "create_test_data(path)\n",
+    "X = np.array(X).reshape(-1, 80,80,1)\n",
+    "y = np.array(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20083f26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = X/255.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bdf7b8df",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "# Adds a densely-connected layer with 64 units to the model:\n",
+    "model.add(Conv2D(64,(3,3), activation = 'relu', input_shape = X.shape[1:]))\n",
+    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
+    "# Add another:\n",
+    "model.add(Conv2D(64,(3,3), activation = 'relu'))\n",
+    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(64, activation='relu'))\n",
+    "# Add a softmax layer with 10 output units:\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
+    "\n",
+    "model.compile(optimizer=\"adam\",\n",
+    "              loss='binary_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00ffe16a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X, y, epochs=10, batch_size=32, validation_split=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6790491b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dir = \"test1\"\n",
+    "path = os.path.join(main_dir,train_dir)\n",
+    "#os.listdir(path)\n",
+    "\n",
+    "X_test = []\n",
+    "id_line = []\n",
+    "def create_test1_data(path):\n",
+    "    for p in os.listdir(path):\n",
+    "        id_line.append(p.split(\".\")[0])\n",
+    "        img_array = cv2.imread(os.path.join(path,p),cv2.IMREAD_GRAYSCALE)\n",
+    "        new_img_array = cv2.resize(img_array, dsize=(80, 80))\n",
+    "        X_test.append(new_img_array)\n",
+    "create_test1_data(path)\n",
+    "X_test = np.array(X_test).reshape(-1,80,80,1)\n",
+    "X_test = X_test/255"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c73fc88b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d28edd26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_val = [int(round(p[0])) for p in predictions]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "861ebb64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df = pd.DataFrame({'id':id_line, 'label':predicted_val})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58e57ec7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission_df.to_csv(\"submission.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec9cdc21",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Biedap/readme.md

@@ -0,0 +1,2 @@
+Plik igiveup.ipynb zawiera wczesną wersję algorytmu uczenia maszynowego opracowanego na podstawie jednego z rozwiązań popularnego zagadnienia ,,Dogs vs Cats" umieszczonego na stronie Kaggle.com. Datasety pochodzą ze strony: https://www.kaggle.com/competitions/dogs-vs-cats/data .
+Plik algorytm_uczenia.py zawiera drugi testowany algorytm uczenia maszynowego. Został on opracowany na podstawie instrukcji ze strony https://towardsdatascience.com/build-a-custom-trained-object-detection-model-with-5-lines-of-code-713ba7f6c0fb.

Proszezadzałaj

@@ -0,0 +1 @@
+print('hej')

README.md

@@ -1,6 +1,12 @@
 # BiedronkApp
 
-Projekt z przedmiotu Systemy informatyczne.
+## Motywacja rynkowa
+Rozpoczynając od powodów najbardziej ryzykownych dla utrzymania firmy, chcielibyśmy przedstawić motywację rynkową dla stworzenia naszej innowacji. Biedronki azjatyckie to gatunek chrząszcza z rodziny biedronkowatych, który w Polsce został określony gatunkiem inwazyjnym zagrażającym światowej bioróżnorodności. W Poznaniu, jako po raz pierwszy w Polsce, stwierdzono jego obecność w 2006 roku. W odróżnieniu od innych biedronek (w tym biedronek siedmiokropek - naszych polskich), zauważalny jest wzrost statystyk w przypadku ukąszeń ludzi, które w części przypadków wywołują reakcje alergiczne dotyczące skóry i dróg oddechowych. Jesienią rozpoczyna się przygotowywanie tego gatunku do odbycia hibernacji, dlatego też szukają schronienia w naszych domach, wchodząc nieproszone i pojawiając się w dużych ilościach na ścianach budynków. Z tego powodu co raz częściej Polacy pytają się: w jaki sposób pozbyć się biedronek, które mogą być dla nas szkodliwe?  
 
-# Opis testowy
-testtesttesttes
+Na to zapotrzebowanie odpowiada nasza aplikacja. Dzięki najbardziej przyjaznej dla użytkownika drodze - zrobieniu zdjęcia - dostajemy odpowiedź na pytanie czy to jest biedronka azjatycka, czy może inny insekt. Wraz z klasyfikacją otrzymujemy dodatkowe informacje, takie jak zestaw cech różnicujących ten insekt, rozpoznawanie i postępowanie w przypadku reakcji alergicznych. 
+
+## Wartość produktowa
+Przechodząc płynnie do wartości naszego produktu  - aplikacji mobilnej działającej na zaimplementowanym modelu różnicującym gatunki biedronek, warto wspomnieć o jego unikalnej propozycji wartości (UVP). Naszą grupą docelową klientów są mieszkańcy miast, którzy najczęściej zmagają się z plagami biedronek azjatyckich, jednocześnie są grupą, która najwięcej korzysta z urządzeń mobilnych, co ułatwia im korzystanie z aplikacji. Kluczowym benefitem funkcjonalności naszej aplikacji jest jej przystępność i łatwość obsługi, brak kosztów dla użytkownika oraz odpowiedź na współczesne problemy dużej części społeczeństwa.  
+
+## Zespół
+Nasz zespół tworzący BiedronkAPP jest gronem specjalistów w dziedzinie Machine Learning, którzy wraz z współpracą zestawu zdjęć udostępnionych przez biologów stworzył model definiujący rodzaj biedronki, której zdjęcie wstawimy do aplikacji. Płynna znajomość języka angielskiego pozwala nam rozważać wejście na rynek europejski.

algorytm_biedronki.py

@@ -0,0 +1,152 @@
+#%%
+
+from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Dropout
+from tensorflow.keras.models import Model
+from tensorflow.keras.applications.vgg19 import VGG19
+from tensorflow.keras.applications.vgg19 import preprocess_input
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential
+import numpy as np
+import pandas as pd
+import os
+import cv2
+import matplotlib.pyplot as plt
+# %%
+os.chdir(r"C:\Users\pittb\Documents\biedronkapp_2.0\BiedronkApp\biedronki_dataset")
+train_path="train"
+test_path="test"
+# %%
+x_train=[]
+
+for folder in os.listdir(train_path):
+
+    sub_path=train_path+"/"+folder
+
+    for img in os.listdir(sub_path):
+        image_path=sub_path+"/"+img
+
+        img_arr=cv2.imread(image_path)
+
+        img_arr=cv2.resize(img_arr,(224,224))
+
+        x_train.append(img_arr)
+
+x_test=[]
+
+for folder in os.listdir(test_path):
+
+    sub_path=test_path+"/"+folder
+
+    for img in os.listdir(sub_path):
+
+        image_path=sub_path+"/"+img
+
+        img_arr=cv2.imread(image_path)
+
+        img_arr=cv2.resize(img_arr,(224,224))
+
+        x_test.append(img_arr)
+# %%
+train_x=np.array(x_train)
+test_x=np.array(x_test)
+
+train_x=train_x/255.0
+test_x=test_x/255.0
+# %%
+train_datagen = ImageDataGenerator(rescale = 1./255)
+test_datagen = ImageDataGenerator(rescale = 1./255)
+# %%
+training_set = train_datagen.flow_from_directory(train_path,
+                                                 target_size = (224, 224),
+                                                 batch_size = 32,
+                                                 class_mode = 'sparse')
+test_set = test_datagen.flow_from_directory(test_path,
+                                            target_size = (224, 224),
+                                            batch_size = 32,
+                                            class_mode = 'sparse')
+# %%
+train_y=training_set.classes
+test_y=test_set.classes
+
+training_set.class_indices
+
+train_y.shape,test_y.shape
+# %%
+IMAGE_SIZE = [224, 224]
+
+vgg = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
+
+for layer in vgg.layers:
+    layer.trainable = False
+
+# %%
+# our layers - you can add more if you want
+x = Flatten()(vgg.output)
+
+prediction = Dense(2, activation='softmax')(x)
+# %%
+# create a model object
+model = Model(inputs=vgg.input, outputs=prediction)
+
+# view the structure of the model
+model.summary()
+# %%
+model.compile(
+  loss='sparse_categorical_crossentropy',
+  optimizer="adam",
+  metrics=['accuracy']
+)
+# %%
+from tensorflow.keras.callbacks import EarlyStopping
+early_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=5)
+# %%
+history = model.fit(
+  train_x,
+  train_y,
+  epochs=10,
+  callbacks=[early_stop],
+  batch_size=32,shuffle=True)
+# %%
+plt.plot(history.history['loss'], label='train loss')
+plt.legend()
+
+plt.savefig('vgg-loss-rps-1.png')
+plt.show()
+# %%
+# accuracies
+plt.plot(history.history['accuracy'], label='train acc')
+plt.legend()
+
+plt.savefig('vgg-acc-rps-1.png')
+plt.show()
+# %%
+model.evaluate(test_x,test_y,batch_size=6)
+# %%
+from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
+import numpy as np
+
+y_pred=model.predict(test_x)
+y_pred=np.argmax(y_pred,axis=1)
+
+accuracy_score(y_pred,test_y)
+
+print(classification_report(y_pred,test_y))
+# %%
+img_path = r"C:\Users\pittb\Documents\biedronkapp_2.0\BiedronkApp\biedronki_dataset\test\azjatyckie\78687-Biedronki-azjatyckie-Czy-sa-grozne-fot.-cocoparisienne---Pixabay.jpg"
+
+img_nump = np.array(cv2.imread(img_path))
+img_nump = cv2.resize(img_nump,(224,224))
+img_nump = img_nump/255.0
+img_nump = img_nump[np.newaxis,...]
+
+pr = model.predict(img_nump)
+print(pr)
+print(np.argmax(pr,axis=1))
+# %%
+# %%
+model.save('saved_model/my_model')
+
+# %%
+
+# %%

algorytm_biedronki_wersja_z_wyuczonym_modelem.py

@@ -0,0 +1,119 @@
+#%%
+from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Dropout
+from tensorflow.keras.models import Model
+from tensorflow.keras.applications.vgg19 import VGG19
+from tensorflow.keras.applications.vgg19 import preprocess_input
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential
+import numpy as np
+import pandas as pd
+import os
+import cv2
+import matplotlib.pyplot as plt
+import keras
+
+# %%
+os.chdir(r"C:\Users\pittb\Documents\biedronkapp_2.0\BiedronkApp\biedronki_dataset")
+train_path="train"
+test_path="test"
+# %%
+x_train=[]
+
+for folder in os.listdir(train_path):
+
+    sub_path=train_path+"/"+folder
+
+    for img in os.listdir(sub_path):
+        image_path=sub_path+"/"+img
+
+        img_arr=cv2.imread(image_path)
+
+        img_arr=cv2.resize(img_arr,(224,224))
+
+        x_train.append(img_arr)
+
+x_test=[]
+
+for folder in os.listdir(test_path):
+
+    sub_path=test_path+"/"+folder
+
+    for img in os.listdir(sub_path):
+
+        image_path=sub_path+"/"+img
+
+        img_arr=cv2.imread(image_path)
+
+        img_arr=cv2.resize(img_arr,(224,224))
+
+        x_test.append(img_arr)
+# %%
+train_x=np.array(x_train)
+test_x=np.array(x_test)
+
+train_x=train_x/255.0
+test_x=test_x/255.0
+# %%
+train_datagen = ImageDataGenerator(rescale = 1./255)
+test_datagen = ImageDataGenerator(rescale = 1./255)
+# %%
+training_set = train_datagen.flow_from_directory(train_path,
+                                                 target_size = (224, 224),
+                                                 batch_size = 32,
+                                                 class_mode = 'sparse')
+test_set = test_datagen.flow_from_directory(test_path,
+                                            target_size = (224, 224),
+                                            batch_size = 32,
+                                            class_mode = 'sparse')
+# %%
+train_y=training_set.classes
+test_y=test_set.classes
+
+training_set.class_indices
+
+train_y.shape,test_y.shape
+# %%
+IMAGE_SIZE = [224, 224]
+
+vgg = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
+
+for layer in vgg.layers:
+    layer.trainable = False
+
+# %%
+# our layers - you can add more if you want
+x = Flatten()(vgg.output)
+
+prediction = Dense(2, activation='softmax')(x)
+# %%
+model = keras.models.load_model('saved_model/my_model')
+
+# %%
+from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
+import numpy as np
+
+y_pred=model.predict(test_x)
+y_pred=np.argmax(y_pred,axis=1)
+
+accuracy_score(y_pred,test_y)
+
+print(classification_report(y_pred,test_y))
+
+
+# %%
+img_path = r"test\azjatyckie\Asian_lady_beetle-(Harmonia-axyridis).jpg"
+
+img_nump = np.array(cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB))
+
+plt.imshow(img_nump)
+plt.show()
+
+img_nump = cv2.resize(img_nump,(224,224))
+img_nump = img_nump/255.0
+img_nump = img_nump[np.newaxis,...]
+
+pr = model.predict(img_nump)
+print('azjatycka: '+str(pr[0][0])+' , siedmiokropka: '+str(pr[0][1]))
+print(['azjatycka' if np.argmax(pr,axis=1)[0]==0 else 'siedmiokropka'][0])
+# %%