horse-ride-time-series-analysis.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":9841346,"sourceType":"datasetVersion","datasetId":6014938}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"# Common\nimport numpy as np\nimport pandas as pd\nfrom typing import List, Tuple\n\n# Data Visualization\nimport plotly.express as px\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n# Image Processing\nimport cv2\nfrom PIL import Image","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:03.814396Z","iopub.execute_input":"2024-11-12T10:02:03.814937Z","iopub.status.idle":"2024-11-12T10:02:03.822248Z","shell.execute_reply.started":"2024-11-12T10:02:03.814890Z","shell.execute_reply":"2024-11-12T10:02:03.820785Z"}},"outputs":[],"execution_count":15},{"cell_type":"markdown","source":"# Constants","metadata":{}},{"cell_type":"code","source":"RESULTS_1 = '/kaggle/input/horse-riding-jumping-walking/sample_1_results.csv'\nRESULTS_2 = '/kaggle/input/horse-riding-jumping-walking/sample_2_results.csv'\nRESULTS_3 = '/kaggle/input/horse-riding-jumping-walking/sample_3_results.csv'\nRESULTS_4 = '/kaggle/input/horse-riding-jumping-walking/sample_4_results.csv'\nHORSE_HH_METERS = 1.524 # 5Feet","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:04.515110Z","iopub.execute_input":"2024-11-12T10:02:04.515631Z","iopub.status.idle":"2024-11-12T10:02:04.522264Z","shell.execute_reply.started":"2024-11-12T10:02:04.515568Z","shell.execute_reply":"2024-11-12T10:02:04.520681Z"}},"outputs":[],"execution_count":16},{"cell_type":"markdown","source":"# Utils","metadata":{}},{"cell_type":"code","source":"def plt_img(image, cmap=\"gray\", flip_x_axis=False, flip_y_axis=False):\n    \"\"\"Display an image using matplotlib\"\"\"\n    plt.imshow(image, cmap)\n    if flip_y_axis:\n        plt.gca().invert_yaxis() \n    if flip_x_axis:\n        plt.gca().invert_xaxis() \n    plt.axis('on')\n    plt.show()\n\n\ndef plt_frame_points(df_row, img_width=640, img_height=360, radius=5, thickness=2):\n    test_row = df_row\n    x_min, y_min, x_max, y_max = test_row.bbox\n    \n    img = np.zeros((img_height, img_width, 3), dtype=np.uint8)\n    cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color=(255, 0, 0), thickness=thickness)\n    \n    for col_name in test_row.index[2:-2]:\n        r,g,b = parse_color(col_name)\n        x, y = test_row[col_name] if test_row[col_name] else (0,0)\n        cv2.circle(img, (x, y), radius=radius, color=(b,g,r), thickness=-1)\n    \n    plt_img(img)\n\n\n# Function to safely convert a string to a list of integers\ndef parse_bbox(value):\n    try:\n        # Remove square brackets and split by spaces, then convert to integers\n        return list(map(int, value.strip('[]').split()))\n    except Exception as e:\n        return []\n\ndef parse_color(column_name):\n    # Split the string by underscores and convert each part to an integer\n    try:\n        return tuple(map(int, column_name.split('_')))\n    except ValueError:\n        return (0,0,0)\n\ndef plt_cartesian_coord_graph(points):\n    x_points = [x for x, y in points]\n    y_points = [y for x, y in points]\n\n    plt.scatter(x_points, y_points)\n    plt.axis(\"equal\")\n    plt.title(\"Transformed Cartesian Coordinates (Origin Bottom-Left)\")\n    plt.xlabel(\"X\")\n    plt.ylabel(\"Y\")\n    plt.legend()\n    \n    plt.tight_layout()\n    plt.show()\n\n\ndef plt_pixel_and_cartesian_graph(point_dict):\n    # Plot Pixel Coordinates\n    plt.figure(figsize=(10, 5))\n    \n    plt.subplot(1, 2, 1)\n    for color, (x, y) in points.items():\n        plt.scatter(x, y, label=color)\n        plt.text(x, y, f\"{color} ({x}, {y})\", fontsize=9, ha='right')\n    plt.gca().invert_yaxis()  # Pixel coordinate y-axis is inverted\n    plt.axis(\"equal\")\n    plt.title(\"Pixel Coordinates (Origin Top-Left)\")\n    plt.xlabel(\"X (Pixels)\")\n    plt.ylabel(\"Y (Pixels)\")\n    plt.legend()\n    \n    # Plot Cartesian Coordinates (transformed)\n    plt.subplot(1, 2, 2)\n    for color, (x, y) in cartesian_points.items():\n        plt.scatter(x, y, label=color)\n        plt.text(x, y, f\"{color} ({x}, {y})\", fontsize=9, ha='right')\n    plt.axis(\"equal\")\n    plt.title(\"Transformed Cartesian Coordinates (Origin Bottom-Left)\")\n    plt.xlabel(\"X\")\n    plt.ylabel(\"Y\")\n    plt.legend()\n    \n    plt.tight_layout()\n    plt.show()\n\n\ndef pixel_to_cartesian(pixel_points: List[Tuple[int, int]]) -> List[Tuple[int, int]]:\n    # Find the maximum y-value to determine the height of the coordinate space\n    max_y = max(y for _, y in pixel_points)\n    \n    # Transform each point, except for (0, 0)\n    cartesian_points = [\n        (x, max_y - y) if (x, y) != (0, 0) else (x, y)  # Keep (0, 0) unchanged\n        for x, y in pixel_points\n    ]\n    \n    return cartesian_points\n\n\ndef calculate_horse_hh_px(frame_data, horse_hh_meters):\n    if not frame_data[\"255_51_255\"]:\n        return 0\n    \n    # 1. Extract detected horse bounding box\n    _, _, _, y_max = test_row.bbox\n\n    # 2. Extract horse withers px pts\n    _,y = frame_data[\"255_51_255\"]\n\n    # 3. Calculate height\n    horse_height_px = y_max - y\n    px_to_m_scale = (horse_hh_meters / horse_height_px)\n    \n    return px_to_m_scale\n\n\ndef get_highest_points(points_dict):\n    highest_points = {}\n    \n    for color, points in points_dict.items():\n        # Find the point with the maximum y-coordinate\n        highest_point = max(points, key=lambda p: p[1])\n        highest_points[color] = highest_point\n    \n    return highest_points\n\n\ndef find_highest_color(highest_points):\n    # Find the color with the maximum y-coordinate value\n    highest_color = max(highest_points, key=lambda color: highest_points[color][1])\n    return highest_color\n\n\ndef find_highest_jump_height(peak_analysis_data, px_to_m_scale):\n    # 1. Find heightest points for each pose\n    highest_points_dict = get_highest_points(peak_analysis_data)\n\n    # 2. Find heightest peak\n    highest_color = find_highest_color(highest_points_dict)\n\n    # 3. Get heighest y-axis value\n    _, y = highest_points_dict[highest_color]\n\n    # 4. Calculate jump height\n    return y * px_to_m_scale\n\n\ndef meters_to_feet(meters):\n    return meters * 3.28084","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:06.311597Z","iopub.execute_input":"2024-11-12T10:02:06.312083Z","iopub.status.idle":"2024-11-12T10:02:06.343512Z","shell.execute_reply.started":"2024-11-12T10:02:06.312043Z","shell.execute_reply":"2024-11-12T10:02:06.342194Z"}},"outputs":[],"execution_count":17},{"cell_type":"markdown","source":"## Step1: Read data & Format","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv(RESULTS_1)\n\n# Sort Values w.r.t frames numbers\ndf = df.sort_values(by='frame_name', ascending=True)\n\n# Format bbox & calculate area values\nfor idx, row in df.iterrows():\n    # Parse the bounding box (assuming it is in the form 'x y w h')\n    bbox = parse_bbox(row['bbox'])\n    \n    # Calculate the bounding box area (w * h)\n    width = 0\n    height = 0\n    if len(bbox) > 0:\n        x, y, w, h = bbox\n        width = w\n        height = h\n\n    # Update the DataFrame\n    df.at[idx, 'bbox'] = bbox\n    df.at[idx, 'width'] = width \n    df.at[idx, 'height'] = height \n\n# Drop rows where the 'bbox' column contains an empty list\ndf = df[df['bbox'].apply(lambda x: x != [])]\n\n# Change color column's type\nfor col in df.columns[2:-2]:\n    df[col] = df[col].apply(\n        lambda x: tuple(map(int, x.strip('()').split(','))) if pd.notnull(x) else None\n    )\n\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:15.604468Z","iopub.execute_input":"2024-11-12T10:02:15.604966Z","iopub.status.idle":"2024-11-12T10:02:15.723376Z","shell.execute_reply.started":"2024-11-12T10:02:15.604924Z","shell.execute_reply":"2024-11-12T10:02:15.721831Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"    frame_name                  bbox   255_128_0  255_153_51 255_178_102  \\\n9           66   [96, 154, 229, 233]  (226, 156)  (225, 156)  (221, 154)   \n8           99  [131, 149, 234, 230]  (227, 155)  (224, 160)  (216, 151)   \n88         132  [130, 149, 236, 232]  (227, 153)  (222, 154)  (215, 150)   \n23         165  [132, 148, 238, 232]        None  (223, 158)        None   \n28         198  [133, 148, 241, 235]        None        None        None   \n\n     230_230_0 255_153_255 153_204_255 255_102_255  255_51_255  ...  \\\n9   (218, 154)  (233, 163)  (214, 172)  (144, 176)  (206, 159)  ...   \n8   (218, 153)  (233, 163)  (213, 173)  (141, 175)  (202, 164)  ...   \n88  (216, 150)  (232, 162)  (214, 170)  (145, 174)  (200, 164)  ...   \n23  (219, 153)  (228, 162)  (214, 170)  (144, 176)  (201, 162)  ...   \n28        None  (184, 180)  (212, 171)  (148, 175)  (196, 168)  ...   \n\n     255_51_51 153_255_153 102_255_102   51_255_51     0_255_0     0_0_255  \\\n9   (187, 214)  (206, 212)  (134, 211)  (151, 210)  (183, 227)  (197, 222)   \n8   (188, 213)  (211, 211)  (138, 206)  (155, 209)  (180, 226)  (201, 221)   \n88  (192, 213)  (192, 215)  (159, 211)  (143, 208)  (208, 223)  (185, 226)   \n23        None  (197, 212)  (161, 212)  (148, 210)  (215, 225)  (190, 221)   \n28  (219, 207)  (218, 209)  (153, 212)  (155, 213)  (224, 226)  (191, 219)   \n\n       255_0_0 255_255_255  width height  \n9   (134, 223)  (162, 224)  229.0  233.0  \n8   (140, 225)  (166, 224)  234.0  230.0  \n88  (171, 226)  (151, 227)  236.0  232.0  \n23  (171, 228)  (157, 228)  238.0  232.0  \n28  (165, 223)  (166, 229)  241.0  235.0  \n\n[5 rows x 24 columns]","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>frame_name</th>\n      <th>bbox</th>\n      <th>255_128_0</th>\n      <th>255_153_51</th>\n      <th>255_178_102</th>\n      <th>230_230_0</th>\n      <th>255_153_255</th>\n      <th>153_204_255</th>\n      <th>255_102_255</th>\n      <th>255_51_255</th>\n      <th>...</th>\n      <th>255_51_51</th>\n      <th>153_255_153</th>\n      <th>102_255_102</th>\n      <th>51_255_51</th>\n      <th>0_255_0</th>\n      <th>0_0_255</th>\n      <th>255_0_0</th>\n      <th>255_255_255</th>\n      <th>width</th>\n      <th>height</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9</th>\n      <td>66</td>\n      <td>[96, 154, 229, 233]</td>\n      <td>(226, 156)</td>\n      <td>(225, 156)</td>\n      <td>(221, 154)</td>\n      <td>(218, 154)</td>\n      <td>(233, 163)</td>\n      <td>(214, 172)</td>\n      <td>(144, 176)</td>\n      <td>(206, 159)</td>\n      <td>...</td>\n      <td>(187, 214)</td>\n      <td>(206, 212)</td>\n      <td>(134, 211)</td>\n      <td>(151, 210)</td>\n      <td>(183, 227)</td>\n      <td>(197, 222)</td>\n      <td>(134, 223)</td>\n      <td>(162, 224)</td>\n      <td>229.0</td>\n      <td>233.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>99</td>\n      <td>[131, 149, 234, 230]</td>\n      <td>(227, 155)</td>\n      <td>(224, 160)</td>\n      <td>(216, 151)</td>\n      <td>(218, 153)</td>\n      <td>(233, 163)</td>\n      <td>(213, 173)</td>\n      <td>(141, 175)</td>\n      <td>(202, 164)</td>\n      <td>...</td>\n      <td>(188, 213)</td>\n      <td>(211, 211)</td>\n      <td>(138, 206)</td>\n      <td>(155, 209)</td>\n      <td>(180, 226)</td>\n      <td>(201, 221)</td>\n      <td>(140, 225)</td>\n      <td>(166, 224)</td>\n      <td>234.0</td>\n      <td>230.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>132</td>\n      <td>[130, 149, 236, 232]</td>\n      <td>(227, 153)</td>\n      <td>(222, 154)</td>\n      <td>(215, 150)</td>\n      <td>(216, 150)</td>\n      <td>(232, 162)</td>\n      <td>(214, 170)</td>\n      <td>(145, 174)</td>\n      <td>(200, 164)</td>\n      <td>...</td>\n      <td>(192, 213)</td>\n      <td>(192, 215)</td>\n      <td>(159, 211)</td>\n      <td>(143, 208)</td>\n      <td>(208, 223)</td>\n      <td>(185, 226)</td>\n      <td>(171, 226)</td>\n      <td>(151, 227)</td>\n      <td>236.0</td>\n      <td>232.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>165</td>\n      <td>[132, 148, 238, 232]</td>\n      <td>None</td>\n      <td>(223, 158)</td>\n      <td>None</td>\n      <td>(219, 153)</td>\n      <td>(228, 162)</td>\n      <td>(214, 170)</td>\n      <td>(144, 176)</td>\n      <td>(201, 162)</td>\n      <td>...</td>\n      <td>None</td>\n      <td>(197, 212)</td>\n      <td>(161, 212)</td>\n      <td>(148, 210)</td>\n      <td>(215, 225)</td>\n      <td>(190, 221)</td>\n      <td>(171, 228)</td>\n      <td>(157, 228)</td>\n      <td>238.0</td>\n      <td>232.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>198</td>\n      <td>[133, 148, 241, 235]</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>(184, 180)</td>\n      <td>(212, 171)</td>\n      <td>(148, 175)</td>\n      <td>(196, 168)</td>\n      <td>...</td>\n      <td>(219, 207)</td>\n      <td>(218, 209)</td>\n      <td>(153, 212)</td>\n      <td>(155, 213)</td>\n      <td>(224, 226)</td>\n      <td>(191, 219)</td>\n      <td>(165, 223)</td>\n      <td>(166, 229)</td>\n      <td>241.0</td>\n      <td>235.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"},"metadata":{}}],"execution_count":18},{"cell_type":"markdown","source":"## Step2: Shift Bounding Box & Its points to the origin","metadata":{}},{"cell_type":"code","source":"# Step 1: Make a copy of 'df' as 'shifted_df'\nshifted_df = df.copy()\n\n# Step 2: Loop through each row and perform the shifting operations\nfor idx, test_row in shifted_df.iterrows():\n    # Extract the bounding box values\n    x_min, y_min, x_max, y_max = test_row.bbox\n\n    # Shift the rectangle to the image origin (0, 0)\n    new_x_min = 0\n    new_y_min = 0\n    new_x_max = x_max - x_min\n    new_y_max = y_max - y_min\n    \n    # Update the bounding box in shifted_df\n    shifted_df.at[idx, 'bbox'] = (new_x_min, new_y_min, new_x_max, new_y_max)\n\n    # Iterate through columns from index 2 to the second-to-last column (color columns)\n    for col_name in test_row.index[2:-2]:\n        # Parse the color column name to get RGB values\n        r, g, b = parse_color(col_name)\n        \n        # If the column has pixel points (non-null)\n        if pd.notnull(test_row[col_name]):\n            x, y = test_row[col_name]  # Original coordinates\n            \n            # Shift the coordinates to the new bounding box origin\n            updated_x, updated_y = (x - x_min, y - y_min)\n            \n            # Update the color column with the new coordinates (x, y)\n            shifted_df.at[idx, col_name] = (updated_x, updated_y)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:24.013818Z","iopub.execute_input":"2024-11-12T10:02:24.015048Z","iopub.status.idle":"2024-11-12T10:02:24.141989Z","shell.execute_reply.started":"2024-11-12T10:02:24.014998Z","shell.execute_reply":"2024-11-12T10:02:24.140813Z"}},"outputs":[],"execution_count":19},{"cell_type":"code","source":"plt_frame_points(df.iloc[23])\nplt_frame_points(shifted_df.iloc[23])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:26.578318Z","iopub.execute_input":"2024-11-12T10:02:26.579592Z","iopub.status.idle":"2024-11-12T10:02:27.176383Z","shell.execute_reply.started":"2024-11-12T10:02:26.579541Z","shell.execute_reply":"2024-11-12T10:02:27.175048Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}],"execution_count":20},{"cell_type":"markdown","source":"## Step3: Scaling Points","metadata":{}},{"cell_type":"code","source":"def get_scale_ratio(current_frame, largest_frame):\n    current_width, current_height = current_frame\n    largest_width, largest_height = largest_frame\n\n    # Calculate the differences in width and height\n    width_diff = largest_width - current_width\n    height_diff = largest_height - current_height\n\n    if height_diff <= width_diff:\n        # Scale based on height\n        scale_ratio_height = largest_height / current_height\n        scale_ratio_width = scale_ratio_height * (current_width / current_height)  # Maintain aspect ratio\n    else:\n        # Scale based on width\n        scale_ratio_width = largest_width / current_width\n        scale_ratio_height = scale_ratio_width * (current_height / current_width)  # Maintain aspect ratio\n\n    return scale_ratio_width, scale_ratio_height\n\n\n\ndef get_fixed_scale_ratio(current_width, current_height, target_width: int = 120, target_height: int = 80):\n    # Calculate scale ratios for width and height\n    width_ratio = target_width / current_width if current_width != 0 else 1\n    height_ratio = target_height / current_height if current_height != 0 else 1\n    \n    return width_ratio, height_ratio","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:35.152020Z","iopub.execute_input":"2024-11-12T10:02:35.152506Z","iopub.status.idle":"2024-11-12T10:02:35.162748Z","shell.execute_reply.started":"2024-11-12T10:02:35.152462Z","shell.execute_reply":"2024-11-12T10:02:35.161261Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"# largest_width = df.loc[df['width'].idxmax()][\"width\"]\n# largest_height = df.loc[df['height'].idxmax()][\"height\"]\n\n# Step 1: Make a copy for 'scaled_df'\nscaled_df = shifted_df.copy()\n\n# Step 2: Loop through each row and perform the scaling operations\nfor idx, test_row in scaled_df.iterrows():\n    x_min, y_min, x_max, y_max = test_row.bbox\n    \n    # Step3: Calculate the scale ratio\n    current_width = x_max\n    current_height = y_max\n    \n    ratio_width, ratio_height = get_fixed_scale_ratio(current_width, current_height)\n    # ratio_width, ratio_height = get_scale_ratio((current_width, current_height), (largest_width, largest_height))\n    \n    scaled_df.at[idx, 'width'] = int(current_width * ratio_width)\n    scaled_df.at[idx, 'height'] = int(current_height * ratio_height)\n    \n    # Step4: Update bbox values\n    new_x_min = int(x_min * ratio_width)\n    new_y_min = int(y_min * ratio_height)\n    new_x_max = int(x_max * ratio_width)\n    new_y_max = int(y_max * ratio_height)\n    scaled_df.at[idx, 'bbox'] = (new_x_min, new_y_min, new_x_max, new_y_max)\n    \n    # Step5: Iterate pose points\n    for col_name in test_row.index[2:-2]:\n        # If the column has pixel points (non-null)\n        if pd.notnull(test_row[col_name]):\n            x, y = test_row[col_name]\n            updated_x, updated_y = (int(x * ratio_width), int(y * ratio_height))\n            scaled_df.at[idx, col_name] = (updated_x, updated_y)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:35.479840Z","iopub.execute_input":"2024-11-12T10:02:35.480305Z","iopub.status.idle":"2024-11-12T10:02:35.613854Z","shell.execute_reply.started":"2024-11-12T10:02:35.480263Z","shell.execute_reply":"2024-11-12T10:02:35.612379Z"}},"outputs":[],"execution_count":22},{"cell_type":"code","source":"idx = -70\nplt_frame_points(df.iloc[idx])\nplt_frame_points(shifted_df.iloc[idx])\nplt_frame_points(scaled_df.iloc[idx], img_width=150, img_height=100, radius=2, thickness=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:35.931128Z","iopub.execute_input":"2024-11-12T10:02:35.932665Z","iopub.status.idle":"2024-11-12T10:02:36.768015Z","shell.execute_reply.started":"2024-11-12T10:02:35.932567Z","shell.execute_reply":"2024-11-12T10:02:36.766693Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}],"execution_count":23},{"cell_type":"markdown","source":"## Step4: Transform Pixel Points to Cartesian Coordinates","metadata":{}},{"cell_type":"code","source":"hoof_1 = []\nhoof_2 = []\nhoof_3 = []\nhoof_4 = []\nwithers = []\n\n# Step 2: Loop through each row and perform the shifting operations\nfor idx, test_row in scaled_df.iterrows():\n    frame_name = test_row[\"frame_name\"]\n\n    pt17 = test_row[\"0_255_0\"] if test_row[\"0_255_0\"] else (0,0)\n    pt18 = test_row[\"0_0_255\"] if test_row[\"0_0_255\"] else (0,0)\n    pt19 = test_row[\"255_0_0\"] if test_row[\"255_0_0\"] else (0,0)\n    pt20 = test_row[\"255_255_255\"] if test_row[\"255_255_255\"] else (0,0)\n    pt16 = test_row[\"255_51_255\"] if test_row[\"255_51_255\"] else (0,0)\n\n    pixel_points = [pt17, pt18, pt19, pt20, pt16]\n    cartesian_points = pixel_to_cartesian(pixel_points)\n\n    hoof_1.append((cartesian_points[0][0] + frame_name, cartesian_points[0][1]))\n    hoof_2.append((cartesian_points[1][0] + frame_name, cartesian_points[1][1]))\n    hoof_3.append((cartesian_points[2][0] + frame_name, cartesian_points[2][1]))\n    hoof_4.append((cartesian_points[3][0] + frame_name, cartesian_points[3][1]))\n    withers.append((cartesian_points[4][0] + frame_name, cartesian_points[4][1]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:36.770276Z","iopub.execute_input":"2024-11-12T10:02:36.770643Z","iopub.status.idle":"2024-11-12T10:02:36.798217Z","shell.execute_reply.started":"2024-11-12T10:02:36.770588Z","shell.execute_reply":"2024-11-12T10:02:36.797077Z"}},"outputs":[],"execution_count":24},{"cell_type":"markdown","source":"## Step5: Path Analysis","metadata":{}},{"cell_type":"code","source":"# Combine all data for plotting\nall_data = [hoof_1, hoof_2, hoof_3, hoof_4]\ncolors = ['blue', 'green', 'orange', 'red']\nlabels = ['Hoof 1', 'Hoof 2', 'Hoof 3', 'Hoof 4']\n\n# Plot each list with different colors\nplt.figure(figsize=(16, 6))\nfor data, color, label in zip(all_data, colors, labels):\n    x_points, y_points = zip(*data)  # Unpack x and y coordinates\n    plt.plot(x_points, y_points, color=color, label=label)\n\n# Set equal scaling for x and y axes\n# plt.gca().set_aspect('equal', adjustable='box')\n\n# Set equal scaling for x and y axes with modified limits\nplt.gca().set_aspect('auto')  # Allows better fit with non-uniform scales\nplt.xlim(0, 5000)   # Adjusted x-axis limit for a closer view, tweak as needed\nplt.ylim(0, 100)   # Adjusted y-axis limit to fit y range exactly\n\n# Adding titles and labels\nplt.title(\"Time Series Scatter Plot\")\nplt.xlabel(\"X Coordinate\")\nplt.ylabel(\"Y Coordinate\")\nplt.legend()\n\n# Save the plot with high resolution (e.g., 300 dpi for HD)\n# plt.savefig('high_resolution_plot.png', dpi=300)\n\n# Show the plot\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:36.799746Z","iopub.execute_input":"2024-11-12T10:02:36.800108Z","iopub.status.idle":"2024-11-12T10:02:37.182033Z","shell.execute_reply.started":"2024-11-12T10:02:36.800054Z","shell.execute_reply":"2024-11-12T10:02:37.180882Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x600 with 1 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"},"metadata":{}}],"execution_count":25},{"cell_type":"markdown","source":"### Highest Peak Analysis","metadata":{}},{"cell_type":"code","source":"# Plot each list with different colors\nplt.figure(figsize=(16, 6))\n\npeak_analysis_data = {}\n\n# Iterate through each hoe data, color, and label\nfor data, color, label in zip(all_data, colors, labels):\n    # Find the highest y value (peak) for each hoe\n    peak_y = max(data, key=lambda point: point[1])[1]\n    \n    # Modify the y values based on the 75% threshold\n    modified_data = [(x, 0 if y <= 0.75 * peak_y else peak_y) for x, y in data]\n\n    # Append the data\n    peak_analysis_data[color] = modified_data\n    \n    # Unpack the modified x and y points\n    x_points, y_points = zip(*modified_data)\n    \n    # Plot the data with the modified y points\n    plt.plot(x_points, y_points, color=color, label=label)\n\n# Set equal scaling for x and y axes with modified limits\nplt.gca().set_aspect('auto')  # Allows better fit with non-uniform scales\nplt.xlim(0, 5000)   # Adjusted x-axis limit for a closer view, tweak as needed\nplt.ylim(0, 150)    # Adjusted y-axis limit to fit y range exactly\n\n# Adding titles and labels\nplt.title(\"Time Series Scatter Plot\")\nplt.xlabel(\"X Coordinate\")\nplt.ylabel(\"Y Coordinate\")\nplt.legend()\n\n# Show the plot\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:37.246842Z","iopub.execute_input":"2024-11-12T10:02:37.247258Z","iopub.status.idle":"2024-11-12T10:02:37.580621Z","shell.execute_reply.started":"2024-11-12T10:02:37.247220Z","shell.execute_reply":"2024-11-12T10:02:37.579430Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x600 with 1 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Step6: Horse Height Calculation","metadata":{}},{"cell_type":"code","source":"test_row = scaled_df.iloc[1]\npx_to_m_scale = calculate_horse_hh_px(test_row, HORSE_HH_METERS)\npx_to_m_scale","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:37.940178Z","iopub.execute_input":"2024-11-12T10:02:37.941421Z","iopub.status.idle":"2024-11-12T10:02:37.950290Z","shell.execute_reply.started":"2024-11-12T10:02:37.941370Z","shell.execute_reply":"2024-11-12T10:02:37.948695Z"}},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"0.023090909090909092"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"jump_height_meters = find_highest_jump_height(peak_analysis_data, px_to_m_scale)\njump_height_feet = meters_to_feet(jump_height_meters)\n\nprint(f\"Jump Height: {jump_height_meters} m, feet: {jump_height_feet} ft\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T10:02:40.708070Z","iopub.execute_input":"2024-11-12T10:02:40.708544Z","iopub.status.idle":"2024-11-12T10:02:40.716368Z","shell.execute_reply.started":"2024-11-12T10:02:40.708503Z","shell.execute_reply":"2024-11-12T10:02:40.714890Z"}},"outputs":[{"name":"stdout","text":"Jump Height: 1.1545454545454545 m, feet: 3.787878909090909 ft\n","output_type":"stream"}],"execution_count":28}]}