{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running on local URL: http://127.0.0.1:7860/\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(,\n", " 'http://127.0.0.1:7860/',\n", " None)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "2022-02-09 14:10:22.417549: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "Total workers: 5\n", "Number of Helmets: 4\n", "Number of Vests: 0\n", "dict vals:\n", "{'W': 5, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 5\n", "dict vals:\n", "{'W': 5, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 5\n", "Workers wearing helmet and vest: 0\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 5\n", "dict vals:\n", "{'W': 5, 'WH': 5, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 5\n", "Number of Helmets: 4\n", "Number of Vests: 0\n", "dict vals:\n", "{'W': 5, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "WARNING:tensorflow:5 out of the last 5 calls to .predict_function at 0x7fbc729998b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "\n", "\n", "\n", "Total workers: 5\n", "Workers wearing helmet and vest: 0\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 5\n", "dict vals:\n", "{'W': 5, 'WH': 5, 'WHV': 0, 'WV': 0}\n", "WARNING:tensorflow:6 out of the last 6 calls to .predict_function at 0x7fbc979e9ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "\n", "\n", "\n", "Total workers: 3\n", "dict vals:\n", "{'W': 3, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 3\n", "Workers wearing helmet and vest: 3\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 0\n", "dict vals:\n", "{'W': 3, 'WH': 0, 'WHV': 3, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 3\n", "Number of Helmets: 3\n", "Number of Vests: 1\n", "dict vals:\n", "{'W': 3, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 5\n", "Number of Helmets: 4\n", "Number of Vests: 0\n", "dict vals:\n", "{'W': 5, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 6\n", "Workers wearing helmet and vest: 0\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 4\n", "Workers not wearing helmet and vest: 2\n", "\n", "\n", "dict vals:\n", "{'W': 6, 'WH': 4, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 6\n", "dict vals:\n", "{'W': 6, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 5\n", "Number of Helmets: 4\n", "Number of Vests: 0\n", "dict vals:\n", "{'W': 5, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 6\n", "dict vals:\n", "{'W': 6, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 6\n", "Workers wearing helmet and vest: 0\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 4\n", "Workers not wearing helmet and vest: 2\n", "\n", "\n", "dict vals:\n", "{'W': 6, 'WH': 4, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 1\n", "Number of Helmets: 1\n", "Number of Vests: 0\n", "dict vals:\n", "{'W': 1, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 1\n", "Workers wearing helmet and vest: 0\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 1\n", "dict vals:\n", "{'W': 1, 'WH': 1, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 1\n", "dict vals:\n", "{'W': 1, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 1\n", "Workers wearing helmet and vest: 0\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 1\n", "dict vals:\n", "{'W': 1, 'WH': 1, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 5\n", "Number of Helmets: 4\n", "Number of Vests: 0\n", "dict vals:\n", "{'W': 5, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 6\n", "Workers wearing helmet and vest: 0\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 4\n", "Workers not wearing helmet and vest: 2\n", "\n", "\n", "dict vals:\n", "{'W': 6, 'WH': 4, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 3\n", "Workers wearing helmet and vest: 3\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 0\n", "dict vals:\n", "{'W': 3, 'WH': 0, 'WHV': 3, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 3\n", "dict vals:\n", "{'W': 3, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 3\n", "Number of Helmets: 3\n", "Number of Vests: 1\n", "dict vals:\n", "{'W': 3, 'WH': 0, 'WHV': 0, 'WV': 0}\n", "\n", "\n", "\n", "Total workers: 3\n", "Workers wearing helmet and vest: 3\n", "Workers wearing only vest: 0\n", "Workers wearing only helmet: 0\n", "dict vals:\n", "{'W': 3, 'WH': 0, 'WHV': 3, 'WV': 0}\n" ] } ], "source": [ "import numpy as np\n", "import run_code\n", "import cv2\n", "import gradio as gr\n", "\n", "\n", "def sepia(Input_Image, Approach):\n", " pil_image = Input_Image\n", " open_cv_image = np.asarray(pil_image)\n", " # Convert RGB to BGR\n", " #open_cv_image = open_cv_image[:, :, ::-1].copy()\n", " #Approach = 3\n", " sepia_img = run_code.run(open_cv_image, Approach)\n", " images = sepia_img['img']\n", " texts= sepia_img['text']\n", " #print (labels)\n", " return images, texts\n", "\n", "image = [gr.inputs.Image(type=\"pil\"), gr.inputs.Radio([1, 2, 3])]\n", "#output = [\"image\", gr.outputs.Label(num_top_classes=4)]\n", "output = [\"image\", gr.outputs.Textbox(type=\"auto\")]\n", "#output = gr.outputs.Label(num_top_classes=4)\n", "\n", "title=\"Real-time Detection of Personal-Protective-Equipment (PPE)\"\n", "description=\"This demo is the implementation of Real-time Detection of Personal-Protective-Equipment (PPE) paper https://github.com/ciber-lab/pictor-ppe\" \\\n", " \" - by Sanjay Kamath \"\n", "examples = [[\"examples/ex1.jpg\", 1], [\"examples/ex2.jpg\", 2], [\"examples/ex3.jpg\", 3]]\n", "\n", "#iface = gr.Interface(sepia , [ gr.inputs.Image(shape=(200, 200)), gr.inputs.Radio([1, 2, 3])], \"image\", title=title,\n", "# examples = [[\"examples/ex1.jpg\"], [\"examples/ex2.jpg\"], [\"examples/ex3.jpg\"]],\n", "# description=description)\n", "\n", "iface = gr.Interface(fn=sepia, inputs=image, outputs=output, title=title, description=description, examples=examples)\n", "\n", "iface.launch()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }