{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n\ndef map_new_to_old_style(sequence):\n    types = []\n    landmark_indexes = []\n    for column in list(sequence.columns)[1:544]:\n        parts = column.split(\"_\")\n        if len(parts) == 4:\n            types.append(parts[1] + \"_\" + parts[2])\n        else:\n            types.append(parts[1])\n\n        landmark_indexes.append(int(parts[-1]))\n\n    data = {\n        \"frame\": [],\n        \"type\": [],\n        \"landmark_index\": [],\n        \"x\": [],\n        \"y\": [],\n        \"z\": []\n    }\n\n    for index, row in sequence.iterrows():\n        data[\"frame\"] += [int(row.frame)]*543\n        data[\"type\"] += types\n        data[\"landmark_index\"] += landmark_indexes\n\n        for _type, landmark_index in zip(types, landmark_indexes):\n            data[\"x\"].append(row[f\"x_{_type}_{landmark_index}\"])\n            data[\"y\"].append(row[f\"y_{_type}_{landmark_index}\"])\n            data[\"z\"].append(row[f\"z_{_type}_{landmark_index}\"])\n\n    return pd.DataFrame.from_dict(data)\n\n# assign desired colors to landmarks\ndef assign_color(row):\n    if row == 'face':\n        return 'red'\n    elif 'hand' in row:\n        return 'dodgerblue'\n    else:\n        return 'green'\n\n# specifies the plotting order\ndef assign_order(row):\n    if row.type == 'face':\n        return row.landmark_index + 101\n    elif row.type == 'pose':\n        return row.landmark_index + 30\n    elif row.type == 'left_hand':\n        return row.landmark_index + 80\n    else:\n        return row.landmark_index\n    \ndef visualise2d_landmarks(parquet_df, title=\"\"):\n    connections = [  \n        [0, 1, 2, 3, 4,],\n        [0, 5, 6, 7, 8],\n        [0, 9, 10, 11, 12],\n        [0, 13, 14, 15, 16],\n        [0, 17, 18, 19, 20],\n\n        \n        [38, 36, 35, 34, 30, 31, 32, 33, 37],\n        [40, 39],\n        [52, 46, 50, 48, 46, 44, 42, 41, 43, 45, 47, 49, 45, 51],\n        [42, 54, 56, 58, 60, 62, 58],\n        [41, 53, 55, 57, 59, 61, 57],\n        [54, 53],\n\n        \n        [80, 81, 82, 83, 84, ],\n        [80, 85, 86, 87, 88],\n        [80, 89, 90, 91, 92],\n        [80, 93, 94, 95, 96],\n        [80, 97, 98, 99, 100], ]\n\n    parquet_df = map_new_to_old_style(parquet_df)\n    frames = sorted(set(parquet_df.frame))\n    first_frame = min(frames)\n    parquet_df['color'] = parquet_df.type.apply(lambda row: assign_color(row))\n    parquet_df['plot_order'] = parquet_df.apply(lambda row: assign_order(row), axis=1)\n    first_frame_df = parquet_df[parquet_df.frame == first_frame].copy()\n    first_frame_df = first_frame_df.sort_values([\"plot_order\"]).set_index('plot_order')\n\n\n    frames_l = []\n    for frame in frames:\n        filtered_df = parquet_df[parquet_df.frame == frame].copy()\n        filtered_df = filtered_df.sort_values([\"plot_order\"]).set_index(\"plot_order\")\n        traces = [go.Scatter(\n            x=filtered_df['x'],\n            y=filtered_df['y'],\n            mode='markers',\n            marker=dict(\n                color=filtered_df.color,\n                size=9))]\n\n        for i, seg in enumerate(connections):\n            trace = go.Scatter(\n                    x=filtered_df.loc[seg]['x'],\n                    y=filtered_df.loc[seg]['y'],\n                    mode='lines',\n            )\n            traces.append(trace)\n        frame_data = go.Frame(data=traces, traces = [i for i in range(17)])\n        frames_l.append(frame_data)\n\n    traces = [go.Scatter(\n        x=first_frame_df['x'],\n        y=first_frame_df['y'],\n        mode='markers',\n        marker=dict(\n            color=first_frame_df.color,\n            size=9\n        )\n    )]\n    for i, seg in enumerate(connections):\n        trace = go.Scatter(\n            x=first_frame_df.loc[seg]['x'],\n            y=first_frame_df.loc[seg]['y'],\n            mode='lines',\n            line=dict(\n                color='black',\n                width=2\n            )\n        )\n        traces.append(trace)\n    fig = go.Figure(\n        data=traces,\n        frames=frames_l\n    )\n\n\n    fig.update_layout(\n        width=500,\n        height=800,\n        scene={\n            'aspectmode': 'data',\n        },\n        updatemenus=[\n            {\n                \"buttons\": [\n                    {\n                        \"args\": [None, {\"frame\": {\"duration\": 100,\n                                                  \"redraw\": True},\n                                        \"fromcurrent\": True,\n                                        \"transition\": {\"duration\": 0}}],\n                        \"label\": \"▶\",\n                        \"method\": \"animate\",\n                    },\n                    {\n                        \"args\": [[None], {\"frame\": {\"duration\": 0, \"redraw\": False},\n                                          \"mode\": \"immediate\",\n                                          \"transition\": {\"duration\": 0}}],\n                        \"label\": \"▌▌\",\n                        \"method\": \"animate\",\n                    },\n                ],\n                \"direction\": \"left\",\n                \"pad\": {\"r\": 100, \"t\": 100},\n                \"font\": {\"size\":20},\n                \"type\": \"buttons\",\n                \"x\": 0.1,\n                \"y\": 0,\n            }\n        ],\n    )\n    camera = dict(\n        up=dict(x=0, y=-1, z=0),\n        eye=dict(x=0, y=0, z=2.5)\n    )\n    fig.update_layout(title_text=title, title_x=0.5)\n    fig.update_layout(scene_camera=camera, showlegend=False)\n    fig.update_layout(xaxis = dict(visible=False),\n            yaxis = dict(visible=False),\n    )\n    fig.update_yaxes(autorange=\"reversed\")\n\n    fig.show()\n    \ndef get_phrase(df, file_id, sequence_id):\n    return df[\n        np.logical_and(\n            df.file_id == file_id, \n            df.sequence_id == sequence_id\n        )\n    ].phrase.iloc[0]\n\nimport pandas as pd,numpy as np,os\nimport json\nimport plotly.graph_objects as go\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly.express as px\nimport plotly.io as pio\nfrom pathlib import Path\npio.templates.default = \"simple_white\"\nprint(\"importing..\")\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-05-25T05:29:09.369773Z","iopub.execute_input":"2023-05-25T05:29:09.370155Z","iopub.status.idle":"2023-05-25T05:29:12.063516Z","shell.execute_reply.started":"2023-05-25T05:29:09.370124Z","shell.execute_reply":"2023-05-25T05:29:12.062443Z"}}},{"cell_type":"code","source":"train_df = pd.read_csv(\"/kaggle/input/asl-fingerspelling/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-05-25T04:46:23.012324Z","iopub.execute_input":"2023-05-25T04:46:23.013368Z","iopub.status.idle":"2023-05-25T04:46:23.178333Z","shell.execute_reply.started":"2023-05-25T04:46:23.013325Z","shell.execute_reply":"2023-05-25T04:46:23.177171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"supplemental_landmarks = pd.read_csv(\"/kaggle/input/asl-fingerspelling/supplemental_metadata.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-05-25T05:06:04.386051Z","iopub.execute_input":"2023-05-25T05:06:04.386469Z","iopub.status.idle":"2023-05-25T05:06:04.50582Z","shell.execute_reply.started":"2023-05-25T05:06:04.386428Z","shell.execute_reply":"2023-05-25T05:06:04.50473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **What is American Sign Language?**","metadata":{}},{"cell_type":"markdown","source":"American Sign Language (ASL) is a complete, natural language that has the same linguistic properties as spoken languages, with grammar that differs from English. ASL is expressed by movements of the hands and face. It is the primary language of many North Americans who are deaf and hard of hearing and is used by some hearing people as 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"}}},{"cell_type":"markdown","source":"## **2.Dataset Attributes**","metadata":{}},{"cell_type":"markdown","source":"**train_df**\n\npath: The path to the landmark file.\n\nfile_id: A unique identifier for the data file.\n\nsequence_id: A unique identifier for the landmark sequence. Each data file may contain many sequences.\n\nphrase: The labels for the landmark sequence. The train and test datasets contain randomly generated addresses, phone numbers, and urls derived from components of real addresses/phone numbers/urls. Any overlap with real addresses, phone numbers, or urls is purely accidental. The supplemental dataset consists of fingerspelled sentences. Note that some of the urls include adult content. The intent of this competition is to support the Deaf and Hard of Hearing community in engaging with technology on an equal footing with other adults.","metadata":{}},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-05-25T04:51:43.898149Z","iopub.execute_input":"2023-05-25T04:51:43.898555Z","iopub.status.idle":"2023-05-25T04:51:43.927772Z","shell.execute_reply.started":"2023-05-25T04:51:43.898514Z","shell.execute_reply":"2023-05-25T04:51:43.926957Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.shape","metadata":{"execution":{"iopub.status.busy":"2023-05-25T04:52:14.93744Z","iopub.execute_input":"2023-05-25T04:52:14.937849Z","iopub.status.idle":"2023-05-25T04:52:14.94513Z","shell.execute_reply.started":"2023-05-25T04:52:14.937819Z","shell.execute_reply":"2023-05-25T04:52:14.944124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"What is a .parquet file?\n\nIt is used in the path column\nParquet is an open source file format built to handle flat columnar storage data formats. Parquet operates well with complex data in large volumes.It is known for its both performant data compression and its ability to handle a wide variety of encoding types. ","metadata":{}},{"cell_type":"markdown","source":"**Let's try to visualize what is inside the .parquet file for the following sequence_id and file_id**","metadata":{}},{"cell_type":"code","source":"sequence_id = 1816796431\nfile_id = 5414471","metadata":{"execution":{"iopub.status.busy":"2023-05-25T05:11:27.266511Z","iopub.execute_input":"2023-05-25T05:11:27.267433Z","iopub.status.idle":"2023-05-25T05:11:27.272605Z","shell.execute_reply.started":"2023-05-25T05:11:27.267367Z","shell.execute_reply":"2023-05-25T05:11:27.271607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"path_to_sign = f\"/kaggle/input/asl-fingerspelling/train_landmarks/{file_id}.parquet\"\nsign = pd.read_parquet(path_to_sign)","metadata":{"execution":{"iopub.status.busy":"2023-05-25T05:11:27.658956Z","iopub.execute_input":"2023-05-25T05:11:27.659307Z","iopub.status.idle":"2023-05-25T05:11:44.176524Z","shell.execute_reply.started":"2023-05-25T05:11:27.659281Z","shell.execute_reply":"2023-05-25T05:11:44.175611Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sign","metadata":{"execution":{"iopub.status.busy":"2023-05-25T05:12:12.218138Z","iopub.execute_input":"2023-05-25T05:12:12.21857Z","iopub.status.idle":"2023-05-25T05:12:12.276126Z","shell.execute_reply.started":"2023-05-25T05:12:12.218536Z","shell.execute_reply":"2023-05-25T05:12:12.274653Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"appears to be frames of the video!","metadata":{}},{"cell_type":"markdown","source":"**supplemental_landmarks**","metadata":{}},{"cell_type":"code","source":"supplemental_landmarks","metadata":{"execution":{"iopub.status.busy":"2023-05-25T05:07:03.653463Z","iopub.execute_input":"2023-05-25T05:07:03.653873Z","iopub.status.idle":"2023-05-25T05:07:03.673539Z","shell.execute_reply.started":"2023-05-25T05:07:03.653841Z","shell.execute_reply":"2023-05-25T05:07:03.672438Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Get sequence of frames for 1816796431","metadata":{}},{"cell_type":"code","source":"sequence = sign[sign.index == sequence_id]\nsequence","metadata":{"execution":{"iopub.status.busy":"2023-05-25T05:15:35.245935Z","iopub.execute_input":"2023-05-25T05:15:35.246335Z","iopub.status.idle":"2023-05-25T05:15:35.283583Z","shell.execute_reply.started":"2023-05-25T05:15:35.246304Z","shell.execute_reply":"2023-05-25T05:15:35.282466Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sequence_phrase = get_phrase(train_df, file_id, sequence_id)\nvisualise2d_landmarks(sequence, f\"Phrase: {sequence_phrase}\")","metadata":{"execution":{"iopub.status.busy":"2023-05-25T05:29:16.061068Z","iopub.execute_input":"2023-05-25T05:29:16.061448Z","iopub.status.idle":"2023-05-25T05:29:35.803657Z","shell.execute_reply.started":"2023-05-25T05:29:16.061417Z","shell.execute_reply":"2023-05-25T05:29:35.802079Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sequence = sign[sign.index == sequence_id]\nsequence","metadata":{},"execution_count":null,"outputs":[]}]}