widzenie-komputerowe-projekt/rybki.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "import numpy as np\n",
    "import threading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model('./model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(\"rybki.mp4\")\n",
    "cap.set(cv2.CAP_PROP_FPS, 60)\n",
    "\n",
    "class_names=sorted(['Fish', \"Jellyfish\", 'Penguin', 'Puffin', 'Shark', 'Starfish', 'Stingray'])\n",
    "object_detector = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=50)\n",
    "\n",
    "\n",
    "width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) \n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "out = cv2.VideoWriter('track_fish.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (width, height))\n",
    "\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "    if(frame is not None):\n",
    "        roi = frame[100: 900,330:1900]\n",
    "        mask = object_detector.apply(roi)\n",
    "        _, mask = cv2.threshold(mask,254,255, cv2.THRESH_BINARY)\n",
    "        conturs, _ =cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "        images = []\n",
    "\n",
    "        for cnt in conturs:\n",
    "            area = cv2.contourArea(cnt)\n",
    "            if area > 200:\n",
    "                #cv2.drawContours(roi,[cnt],-1,(0,255,0),2)\n",
    "                x,y,w,h = cv2.boundingRect(cnt)\n",
    "                rectangle = cv2.rectangle(roi,(x,y),(x+w,y+h),(0,255,0),3)\n",
    "                image_to_predict = roi[y:y+h,x:x+w]\n",
    "                image_to_predict = cv2.resize(image_to_predict,(227,227))\n",
    "                images.append((x,y,rectangle,np.expand_dims(image_to_predict,axis=0)))\n",
    "                \n",
    "                # pred = model.predict(np.expand_dims(image_to_predict, axis=0))\n",
    "                # label = class_names[np.argmax(pred)]\n",
    "        if images:\n",
    "            pred = model.predict(np.vstack([image[3] for image in images]))\n",
    "            labels = [class_names[np.argmax(pre)] for pre in pred]\n",
    "            for i,image in enumerate(images):\n",
    "                cv2.putText(image[2], labels[i], (image[0], image[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 1)\n",
    "        roi = cv2.resize(roi, (960, 540)) \n",
    "        cv2.imshow(\"roi\", roi)\n",
    "\n",
    "        key = cv2.waitKey(30)\n",
    "        if key == 27:\n",
    "            break\n",
    "\n",
    "        #out.write(frame)\n",
    "    else:\n",
    "        break\n",
    "\n",
    "\n",
    "#out.release()\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": "3.9.2"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "393784674bcf6e74f2fc9b1b5fb3713f9bd5fc6f8172c594e5cfa8e8c12849bc"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}