{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objects Expression recognition"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebooks shows some preliminary work on detecting objects expressions with cvlib. It is mainly meant to explore its capabilities and to decide on future research directions. We package our code into a `ammico` package that is imported here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-17T07:56:38.075081Z",
     "iopub.status.busy": "2023-05-17T07:56:38.074628Z",
     "iopub.status.idle": "2023-05-17T07:56:51.738080Z",
     "shell.execute_reply": "2023-05-17T07:56:51.737175Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import ammico\n",
    "from ammico import utils as mutils\n",
    "from ammico import display as mdisplay\n",
    "import ammico.objects as ob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set an image path as input file path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-17T07:56:51.742895Z",
     "iopub.status.busy": "2023-05-17T07:56:51.741831Z",
     "iopub.status.idle": "2023-05-17T07:56:51.746630Z",
     "shell.execute_reply": "2023-05-17T07:56:51.745796Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "images = mutils.find_files(\n",
    "    path=\"data/\",\n",
    "    limit=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-17T07:56:51.750268Z",
     "iopub.status.busy": "2023-05-17T07:56:51.749997Z",
     "iopub.status.idle": "2023-05-17T07:56:51.754088Z",
     "shell.execute_reply": "2023-05-17T07:56:51.753041Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mydict = mutils.initialize_dict(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually inspect what was detected\n",
    "\n",
    "To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-17T07:56:51.759766Z",
     "iopub.status.busy": "2023-05-17T07:56:51.759262Z",
     "iopub.status.idle": "2023-05-17T07:56:53.079877Z",
     "shell.execute_reply": "2023-05-17T07:56:53.078969Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'ammico.display' has no attribute 'explore_analysis'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmdisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexplore_analysis\u001b[49m(mydict, identify\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjects\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'ammico.display' has no attribute 'explore_analysis'"
     ]
    }
   ],
   "source": [
    "mdisplay.explore_analysis(mydict, identify=\"objects\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detect objects and directly write to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-17T07:56:53.085011Z",
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     "shell.execute_reply": "2023-05-17T07:57:02.013565Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading yolov4.cfg from https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg\n"
     ]
    },
    {
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     "text": [
      "  0% |                                                                        |\r"
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     "text": [
      "Downloading yolov4.weights from https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\n"
     ]
    },
    {
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      "  0% |                                                                        |\r"
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      "  1% |                                                                        |\r"
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      "  2% |#                                                                       |\r"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading yolov3_classes.txt from https://github.com/arunponnusamy/object-detection-opencv/raw/master/yolov3.txt\n"
     ]
    },
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    }
   ],
   "source": [
    "for key in mydict:\n",
    "    mydict[key] = ob.ObjectDetector(mydict[key]).analyse_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the dictionary of dictionarys into a dictionary with lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-17T07:57:02.019115Z",
     "iopub.status.busy": "2023-05-17T07:57:02.018450Z",
     "iopub.status.idle": "2023-05-17T07:57:02.023922Z",
     "shell.execute_reply": "2023-05-17T07:57:02.023227Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "outdict = mutils.append_data_to_dict(mydict)\n",
    "df = mutils.dump_df(outdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-17T07:57:02.027079Z",
     "iopub.status.busy": "2023-05-17T07:57:02.026812Z",
     "iopub.status.idle": "2023-05-17T07:57:02.044114Z",
     "shell.execute_reply": "2023-05-17T07:57:02.043348Z"
    },
    "tags": []
   },
   "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>filename</th>\n",
       "      <th>person</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>car</th>\n",
       "      <th>motorcycle</th>\n",
       "      <th>airplane</th>\n",
       "      <th>bus</th>\n",
       "      <th>train</th>\n",
       "      <th>truck</th>\n",
       "      <th>boat</th>\n",
       "      <th>traffic light</th>\n",
       "      <th>cell phone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/102730_eng.png</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/102141_2_eng.png</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/106349S_por.png</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                filename person bicycle car motorcycle airplane bus train   \n",
       "0    data/102730_eng.png    yes      no  no         no       no  no    no  \\\n",
       "1  data/102141_2_eng.png    yes      no  no         no       no  no    no   \n",
       "2   data/106349S_por.png    yes      no  no         no       no  no    no   \n",
       "\n",
       "  truck boat traffic light cell phone  \n",
       "0   yes   no            no         no  \n",
       "1    no   no            no         no  \n",
       "2    no   no            no        yes  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-17T07:57:02.047953Z",
     "iopub.status.busy": "2023-05-17T07:57:02.047237Z",
     "iopub.status.idle": "2023-05-17T07:57:02.052757Z",
     "shell.execute_reply": "2023-05-17T07:57:02.052088Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"./data_out.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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