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Author: jakkcoder

Custom Object detection live video.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "#auther - Jay Shankar Bhatt \n",
+    "# using this code without author's permission other then leaning task is strictly prohibited"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## provide the path for testing cofing file and tained model form colab\n",
+    "net = cv2.dnn.readNetFromDarknet(\"yolov3_custom.cfg\",r\"Downloads\\yolov3_custom_6000.weights\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Change here for custom classes for trained model \n",
+    "\n",
+    "classes = ['Elon_Musk','Barak_obama','Mark_Zuckerberg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "while 1:\n",
+    "    _, img = cap.read()\n",
+    "    img = cv2.resize(img,(1280,720))\n",
+    "    hight,width,_ = img.shape\n",
+    "    blob = cv2.dnn.blobFromImage(img, 1/255,(416,416),(0,0,0),swapRB = True,crop= False)\n",
+    "\n",
+    "    net.setInput(blob)\n",
+    "\n",
+    "    output_layers_name = net.getUnconnectedOutLayersNames()\n",
+    "\n",
+    "    layerOutputs = net.forward(output_layers_name)\n",
+    "\n",
+    "    boxes =[]\n",
+    "    confidences = []\n",
+    "    class_ids = []\n",
+    "\n",
+    "    for output in layerOutputs:\n",
+    "        for detection in output:\n",
+    "            score = detection[5:]\n",
+    "            class_id = np.argmax(score)\n",
+    "            confidence = score[class_id]\n",
+    "            if confidence > 0.7:\n",
+    "                center_x = int(detection[0] * width)\n",
+    "                center_y = int(detection[1] * hight)\n",
+    "                w = int(detection[2] * width)\n",
+    "                h = int(detection[3]* hight)\n",
+    "                x = int(center_x - w/2)\n",
+    "                y = int(center_y - h/2)\n",
+    "                boxes.append([x,y,w,h])\n",
+    "                confidences.append((float(confidence)))\n",
+    "                class_ids.append(class_id)\n",
+    "\n",
+    "\n",
+    "    indexes = cv2.dnn.NMSBoxes(boxes,confidences,.5,.4)\n",
+    "\n",
+    "    boxes =[]\n",
+    "    confidences = []\n",
+    "    class_ids = []\n",
+    "\n",
+    "    for output in layerOutputs:\n",
+    "        for detection in output:\n",
+    "            score = detection[5:]\n",
+    "            class_id = np.argmax(score)\n",
+    "            confidence = score[class_id]\n",
+    "            if confidence > 0.5:\n",
+    "                center_x = int(detection[0] * width)\n",
+    "                center_y = int(detection[1] * hight)\n",
+    "                w = int(detection[2] * width)\n",
+    "                h = int(detection[3]* hight)\n",
+    "\n",
+    "                x = int(center_x - w/2)\n",
+    "                y = int(center_y - h/2)\n",
+    "\n",
+    "\n",
+    "\n",
+    "                boxes.append([x,y,w,h])\n",
+    "                confidences.append((float(confidence)))\n",
+    "                class_ids.append(class_id)\n",
+    "\n",
+    "    indexes = cv2.dnn.NMSBoxes(boxes,confidences,.8,.4)\n",
+    "    font = cv2.FONT_HERSHEY_PLAIN\n",
+    "    colors = np.random.uniform(0,255,size =(len(boxes),3))\n",
+    "    if  len(indexes)>0:\n",
+    "        for i in indexes.flatten():\n",
+    "            x,y,w,h = boxes[i]\n",
+    "            label = str(classes[class_ids[i]])\n",
+    "            confidence = str(round(confidences[i],2))\n",
+    "            color = colors[i]\n",
+    "            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)\n",
+    "            cv2.putText(img,label + \" \" + confidence, (x,y+400),font,2,color,2)\n",
+    "\n",
+    "    cv2.imshow('img',img)\n",
+    "    if cv2.waitKey(1) == ord('q'):\n",
+    "        break\n",
+    "    \n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  }
+ ],
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+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
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