AngleEye.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# target = '26_0.jpg'\n",
    "target = 'dumpster1_1.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(584, 500)\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "with Image.open(f\"cropped_images/{target}\") as img:\n",
    "    div = img.size[1] / 500\n",
    "    img = img.resize((int(img.size[0]/div), int(img.size[1] / div))) # resize image to (x, 500)\n",
    "    \n",
    "    \n",
    "    img.show()\n",
    "    \n",
    "    print(img.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "    img_np = np.array(img)\n",
    "    pallet = np.zeros(img_np.shape[:2])\n",
    "    \n",
    "    for y in range(img.width):\n",
    "        for x in range(img.height):\n",
    "            rgb = img_np[x, y]\n",
    "            if rgb[1] > rgb[0] * 1.1 and rgb[1] > rgb[2] * 1.1: # keep green areas\n",
    "                pallet[x,y] = rgb[1]\n",
    "        \n",
    "Image.fromarray(pallet).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "transposed_img = pallet.transpose() # transpose image to easily detect the bottom of an object\n",
    "Image.fromarray(transposed_img).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12493bdd0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "siluet=[]\n",
    "\n",
    "for idx, row in enumerate(transposed_img):\n",
    "    indices = np.where(row != 0)\n",
    "    \n",
    "    try:\n",
    "        siluet.append(indices[0][-1]) # keep colored indices\n",
    "    except:\n",
    "        if len(indices[0]) == 0:\n",
    "            siluet.append(0)\n",
    "            \n",
    "plt.scatter(range(len(siluet)), siluet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x124983fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final = np.zeros(transposed_img.transpose().shape)\n",
    "\n",
    "for x in range(final.shape[1]):\n",
    "    if siluet[x] > img.size[1] / 3:  # leave only the two-thirds of an image\n",
    "        final[siluet[x], x] = 255\n",
    "        \n",
    "plt.imshow(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "answer: 0 degrees\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "max_values = []\n",
    "\n",
    "for i in ['0','-15','-30','15','30']:\n",
    "    with Image.open(f\"kernels/degree{i}.png\") as degree:\n",
    "        kernel = np.array(degree)\n",
    "        a = cv2.filter2D(final, -1, kernel)\n",
    "        max_values.append(np.max(a)) # get max values of each kernel\n",
    "\n",
    "answer = ['0','-15','-30','15','30'][np.argmax(max_values)] # kernel with the largest value is the answer\n",
    "\n",
    "with Image.open(f\"kernels/degree{answer}.png\") as degree:\n",
    "    kernel = np.array(degree)\n",
    "    a = cv2.filter2D(final, -1, kernel)\n",
    "    plt.imshow(a)\n",
    "    \n",
    "print(f\"answer: {answer} degrees\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv1",
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}