From 047b48e4ae61d9d557f43e5e7b954e2cadbba6cd Mon Sep 17 00:00:00 2001 From: vjshankar4204 <80892041+vjshankar4204@users.noreply.github.com> Date: Sat, 25 Nov 2023 17:15:23 +0100 Subject: [PATCH] Camera feed controls --- DigitalTwin/Vizforpresentation.ipynb | 109 ++++++++++ SquatMaster/functions/camera_control.ipynb | 237 +++++++++++++++++++++ 2 files changed, 346 insertions(+) create mode 100644 SquatMaster/functions/camera_control.ipynb diff --git a/DigitalTwin/Vizforpresentation.ipynb b/DigitalTwin/Vizforpresentation.ipynb index e69de29..7a6578c 100644 --- a/DigitalTwin/Vizforpresentation.ipynb +++ b/DigitalTwin/Vizforpresentation.ipynb @@ -0,0 +1,109 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Example data: Country names and corresponding values\n", + "segments = ['Gym Memberships', 'Equipment Sales', 'Personal Training', 'Nutrition Supplements', 'Fitness Apparel', 'Online Fitness Platforms', 'Wellness Programs', 'Fitness Apps']\n", + "economic_potential = [25, 15, 10, 20, 5, 10, 8, 7] # Hypothetical values for economic potential\n", + "\n", + "\n", + "# Normalize the values to determine the bubble sizes\n", + "sizes = np.array(economic_potential) * 300\n", + "\n", + "# Choose a color for each bubble\n", + "colors = plt.cm.viridis(np.linspace(0, 1, len(segments)))\n", + "\n", + "# Create the bubble chart\n", + "plt.figure(figsize=(10, 8))\n", + "plt.scatter(range(len(segments)), np.zeros(len(segments)), s=sizes, c=colors, alpha=0.6)\n", + "\n", + "# Add labels to the bubbles\n", + "for i, (segments, economic_potential) in enumerate(zip(segments, economic_potential)):\n", + " plt.text(i, 0, f'{segments}', ha='center', va='center')\n", + "\n", + "# Remove axes and ticks\n", + "plt.axis('off')\n", + "\n", + "# Show the chart\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "range(len(segments)+20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.zeros(len(segments)+20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Define pie chart slice labels\n", + "pie_chart_slice_labels = ['Germany', 'Poland', 'Romania', 'Czech Republic', 'Bulgaria', 'Ukraine', 'Greece']\n", + "\n", + "# Define pie chart slice values\n", + "pie_chart_slice_values = [399.8, 100.7, 31.7, 15.0, 12.3, 24.1, 14.0]\n", + "\n", + "# Create a pie chart of the slice values\n", + "plt.pie(pie_chart_slice_values, labels=pie_chart_slice_labels, autopct=\"%1.1f%%\")\n", + "plt.title(\"Pie Chart of Percentage of People in Each Country\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/SquatMaster/functions/camera_control.ipynb b/SquatMaster/functions/camera_control.ipynb new file mode 100644 index 0000000..b0ac325 --- /dev/null +++ b/SquatMaster/functions/camera_control.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from pynput import keyboard\n", + "\n", + "def main():\n", + " # Initialize camera\n", + " cap = cv2.VideoCapture(1)\n", + " capturing = False\n", + " \n", + "\n", + " while True:\n", + " if capturing:\n", + " # Read and display live feed\n", + " ret, frame = cap.read()\n", + " print('i am capturing')\n", + " if ret:\n", + " cv2.imshow('Live Feed', frame)\n", + "\n", + " #key = cv2.waitKey(1) & 0xFF\n", + "\n", + " # Check for hotkeys\n", + " if keyboard.Key == 's': \n", + " capturing = True\n", + " print('Camera is on')\n", + " elif keyboard.Key == 'r' and capturing:\n", + " capture_frame(cap)\n", + " print('feed is being captured')\n", + " elif keyboard.Key == 't':\n", + " standby_mode()\n", + " elif keyboard.Key == 'q':\n", + " close_feed(cap)\n", + " break\n", + "\n", + "def capture_frame(cap):\n", + " # Capture and process a single frame\n", + " ret, frame = cap.read()\n", + " if ret:\n", + " # Process and save frame or perform actions\n", + " cv2.imshow('Captured Frame', frame)\n", + " # Save or process frame as needed\n", + "\n", + "def standby_mode():\n", + " # Implement standby functionality\n", + " print(\"Entering Standby Mode\")\n", + " cv2.destroyAllWindows()\n", + "\n", + "def close_feed(cap):\n", + " # Release the camera and close all windows\n", + " cap.release()\n", + " cv2.destroyAllWindows()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + } + ], + "source": [ + "import cv2\n", + "\n", + "# Initialize camera\n", + "while True:\n", + " cap = cv2.VideoCapture(1)\n", + " ret, frame = cap.read()\n", + " cv2.imshow('Live Feed', frame)\n", + "\n", + "\n", + " if cv2.waitKey(1) & 0xFF == ord('q'):\n", + " cap.release()\n", + " cv2.destroyAllWindows()\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + } + ], + "source": [ + "import cv2\n", + "import keyboard\n", + "\n", + "def main():\n", + " # Initialize camera\n", + " cap = cv2.VideoCapture(0)\n", + " capturing = False\n", + "\n", + " while True:\n", + " # Check for hotkeys\n", + " if keyboard.is_pressed('s'): \n", + " capturing = True\n", + " elif keyboard.is_pressed('r') and capturing:\n", + " capture_frame(cap)\n", + " elif keyboard.is_pressed('t'):\n", + " standby_mode()\n", + " elif keyboard.is_pressed('q'):\n", + " close_feed(cap)\n", + " break\n", + "\n", + " if capturing:\n", + " # Show live feed\n", + " ret, frame = cap.read()\n", + " if ret:\n", + " cv2.imshow('Live Feed', frame)\n", + "\n", + " if cv2.waitKey(1) & 0xFF == ord('q'):\n", + " break\n", + "\n", + "def capture_frame(cap):\n", + " # Capture and process a single frame\n", + " ret, frame = cap.read()\n", + " if ret:\n", + " # Process and save frame or perform actions\n", + " cv2.imshow('Captured Frame', frame)\n", + " # Save or process frame as needed\n", + "\n", + "def standby_mode():\n", + " # Implement standby functionality\n", + " print(\"Entering Standby Mode\")\n", + " cv2.destroyAllWindows()\n", + "\n", + "def close_feed(cap):\n", + " # Release the camera and close all windows\n", + " cap.release()\n", + " cv2.destroyAllWindows()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'pynput.keyboard' has no attribute 'is_pressed'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/vijaishankarbhavanishankar/Documents/EschAR/MediaPipePoseEstimation-eschar/SquatMaster/functions/camera_control.ipynb Cell 5\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 32\u001b[0m cv2\u001b[39m.\u001b[39mdestroyAllWindows()\n\u001b[1;32m 34\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m__name__\u001b[39m \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m__main__\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m---> 35\u001b[0m main()\n", + "\u001b[1;32m/Users/vijaishankarbhavanishankar/Documents/EschAR/MediaPipePoseEstimation-eschar/SquatMaster/functions/camera_control.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 6\u001b[0m cap \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39mVideoCapture(\u001b[39m0\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m 9\u001b[0m \u001b[39m# Check for hotkeys\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[39mif\u001b[39;00m keyboard\u001b[39m.\u001b[39mis_pressed(\u001b[39m'\u001b[39m\u001b[39ms\u001b[39m\u001b[39m'\u001b[39m): \u001b[39m# Replace 'start_hotkey' with your chosen key\u001b[39;00m\n\u001b[1;32m 11\u001b[0m start_capture(cap)\n\u001b[1;32m 12\u001b[0m \u001b[39melif\u001b[39;00m keyboard\u001b[39m.\u001b[39mis_pressed(\u001b[39m'\u001b[39m\u001b[39mt\u001b[39m\u001b[39m'\u001b[39m):\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'pynput.keyboard' has no attribute 'is_pressed'" + ] + } + ], + "source": [ + "import cv2\n", + "from pynput import keyboard # or pynput\n", + "\n", + "def main():\n", + " # Initialize camera\n", + " cap = cv2.VideoCapture(0)\n", + "\n", + " while True:\n", + " # Check for hotkeys\n", + " if keyboard.is_pressed('s'): # Replace 'start_hotkey' with your chosen key\n", + " start_capture(cap)\n", + " elif keyboard.is_pressed('t'):\n", + " standby_mode(cap)\n", + " elif keyboard.is_pressed('q'):\n", + " close_feed(cap)\n", + " break\n", + "\n", + "def start_capture(cap):\n", + " # Capture and process video frames\n", + " ret, frame = cap.read()\n", + " if ret:\n", + " # Process frame\n", + " cv2.imshow('Frame', frame)\n", + "\n", + "def standby_mode(cap):\n", + " # Implement standby functionality\n", + " pass\n", + "\n", + "def close_feed(cap):\n", + " # Release the camera and close all windows\n", + " cap.release()\n", + " cv2.destroyAllWindows()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}