TinyYolov3-Prototype-custom-rocket.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_weights = \"yolov3-tiny-obj_5000.weights\"\n",
    "model_config = \"yolov3-tiny-obj.cfg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load tiny Yolo custom trained weights\n",
    "net = cv2.dnn.readNetFromDarknet(model_config, model_weights)\n",
    "\n",
    "classes = []\n",
    "with open(\"obj.names\", \"r\") as f:\n",
    "    classes = [line.strip() for line in f.readlines()]\n",
    "layer_names = net.getLayerNames()\n",
    "output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n",
    "#colors = np.random.uniform(0, 255, size=(len(classes), 3))\n",
    "colors = (255,255,255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# Loading image\\nimg = cv2.imread(\"rocket+man.jpg\")\\nimg = cv2.resize(img, None, fx=0.4, fy=0.4)\\nheight, width, channels = img.shape\\n'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "# Loading image\n",
    "img = cv2.imread(\"rocket+man.jpg\")\n",
    "img = cv2.resize(img, None, fx=0.4, fy=0.4)\n",
    "height, width, channels = img.shape\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter 1 for webcam.\n",
      " /anything else for video file mode\n",
      "2\n"
     ]
    },
    {
     "ename": "error",
     "evalue": "OpenCV(4.2.0) C:\\projects\\opencv-python\\opencv\\modules\\imgproc\\src\\resize.cpp:4045: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-f659fbe12e95>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m     \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m     \u001b[1;31m#img = cv2.resize(img, (width, height), interpolation = cv2.INTER_AREA)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m     \u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchannels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.2.0) C:\\projects\\opencv-python\\opencv\\modules\\imgproc\\src\\resize.cpp:4045: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'\n"
     ]
    }
   ],
   "source": [
    "print(\"Enter 1 for webcam.\\n /anything else for video file mode\")\n",
    "input_choice = int(input())\n",
    "if input_choice == 1:\n",
    "    print(\"Starting webcam\")\n",
    "    cap = cv2.VideoCapture(0)\n",
    "else:\n",
    "    #print(\"Enter video file name/path\")\n",
    "    video_file = \"launch-video-1.mp4\"\n",
    "    cap = cv2.VideoCapture(video_file)\n",
    "\n",
    "font = cv2.FONT_HERSHEY_PLAIN\n",
    "\n",
    "while(True):\n",
    "    # Capture frame-by-frame\n",
    "    ret, img = cap.read()\n",
    "    \n",
    "    img = cv2.resize(img, None, fx=0.4, fy=0.4)\n",
    "    #img = cv2.resize(img, (width, height), interpolation = cv2.INTER_AREA)\n",
    "    height, width, channels = img.shape\n",
    "    # Our operations on the frame come here\n",
    "    #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "    net.setInput(blob)\n",
    "    outs = net.forward(output_layers)\n",
    "    class_ids = []\n",
    "    confidences = []\n",
    "    boxes = []\n",
    "    for out in outs:\n",
    "        for detection in out:\n",
    "            scores = detection[5:]\n",
    "            class_id = np.argmax(scores)\n",
    "            confidence = scores[class_id]\n",
    "            if confidence > 0.3: #we will lower this since we only have 1 class.\n",
    "                # Object detected\n",
    "                center_x = int(detection[0] * width)\n",
    "                center_y = int(detection[1] * height)\n",
    "                w = int(detection[2] * width)\n",
    "                h = int(detection[3] * height)\n",
    "                # Rectangle coordinates\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",
    "    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) #we’ll apply non-maxima suppression\n",
    "    for i in range(len(boxes)):\n",
    "        if i in indexes:\n",
    "            x, y, w, h = boxes[i]\n",
    "            label = str(classes[class_ids[i]])\n",
    "            color = colors[i-1]\n",
    "            cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)\n",
    "            cv2.putText(img, label, (x, y), font, 1, color, 1)\n",
    "    \n",
    "    \n",
    "    # Display the resulting frame\n",
    "    cv2.namedWindow('frame', cv2.WINDOW_FREERATIO)\n",
    "    #cv2.cvSetWindowProperty('frame', cv2.CV_WND_PROP_FULLSCREEN, cv2.CV_WINDOW_FULLSCREEN);\n",
    "    cv2.imshow('frame',img);\n",
    "\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# When everything done, release the capture\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n# Detecting objects\\nblob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\\nnet.setInput(blob)\\nouts = net.forward(output_layers)\\n'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "\n",
    "# Detecting objects\n",
    "blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "net.setInput(blob)\n",
    "outs = net.forward(output_layers)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# Showing informations on the screen\\nclass_ids = []\\nconfidences = []\\nboxes = []\\nfor out in outs:\\n    for detection in out:\\n        scores = detection[5:]\\n        class_id = np.argmax(scores)\\n        confidence = scores[class_id]\\n        if confidence > 0.5:\\n            # Object detected\\n            center_x = int(detection[0] * width)\\n            center_y = int(detection[1] * height)\\n            w = int(detection[2] * width)\\n            h = int(detection[3] * height)\\n            # Rectangle coordinates\\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'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "# Showing informations on the screen\n",
    "class_ids = []\n",
    "confidences = []\n",
    "boxes = []\n",
    "for out in outs:\n",
    "    for detection in out:\n",
    "        scores = detection[5:]\n",
    "        class_id = np.argmax(scores)\n",
    "        confidence = scores[class_id]\n",
    "        if confidence > 0.5:\n",
    "            # Object detected\n",
    "            center_x = int(detection[0] * width)\n",
    "            center_y = int(detection[1] * height)\n",
    "            w = int(detection[2] * width)\n",
    "            h = int(detection[3] * height)\n",
    "            # Rectangle coordinates\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",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nindexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) #we’ll apply non-maxima suppression\\n'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) #we’ll apply non-maxima suppression\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfont = cv2.FONT_HERSHEY_PLAIN\\nfor i in range(len(boxes)):\\n    if i in indexes:\\n        x, y, w, h = boxes[i]\\n        label = str(classes[class_ids[i]])\\n        color = colors[i]\\n        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)\\n        cv2.putText(img, label, (x, y + 30), font, 3, color, 3)\\ncv2.imshow(\"Image\", img)\\ncv2.waitKey(0)\\ncv2.destroyAllWindows()\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "font = cv2.FONT_HERSHEY_PLAIN\n",
    "for i in range(len(boxes)):\n",
    "    if i in indexes:\n",
    "        x, y, w, h = boxes[i]\n",
    "        label = str(classes[class_ids[i]])\n",
    "        color = colors[i]\n",
    "        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)\n",
    "        cv2.putText(img, label, (x, y + 30), font, 3, color, 3)\n",
    "cv2.imshow(\"Image\", img)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "\"\"\""
   ]
  },
  {
   "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",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}