{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80377b3b",
   "metadata": {},
   "source": [
    "![Logo 1](img/aitech-logotyp-1.jpg)\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<h1> Widzenie komputerowe </h1>\n",
    "<h2> 09. <i>Metody głębokiego uczenia (1)</i> [laboratoria]</h2> \n",
    "<h3>Andrzej Wójtowicz (2021)</h3>\n",
    "</div>\n",
    "\n",
    "![Logo 2](img/aitech-logotyp-2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07159136",
   "metadata": {},
   "source": [
    "W poniższym materiale zobaczymy w jaki sposób korzystać z metod głębokiego uczenia sieci neuronowych w pakiecie OpenCV.\n",
    "\n",
    "Na początku załadujmy niezbędne biblioteki:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2e906f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4348bc5",
   "metadata": {},
   "source": [
    "OpenCV wspiera [wiele](https://github.com/opencv/opencv/wiki/Deep-Learning-in-OpenCV) bibliotek i modeli sieci neuronowych. Modele trenuje się poza OpenCV - bibliotekę wykorzystuje się tylko do predykcji, aczkolwiek sama w sobie ma całkiem sporo możliwych optymalizacji w porównaniu do źródłowych bibliotek neuronowych, więc predykcja może być tutaj faktycznie szybsza.\n",
    "\n",
    "Pliki z modelami i danymi pomocniczymi będziemy pobierali z sieci i będziemy je zapisywali w katalogu `dnn`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b85f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p dnn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac09b098",
   "metadata": {},
   "source": [
    "# Klasyfikacja obrazów\n",
    "\n",
    "Spróbujemy wykorzystać sieć do klasyfikacji obrazów wyuczonej na zbiorze [ImageNet](https://www.image-net.org/). Pobierzmy plik zawierający opis 1000 możliwych klas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85b1b68c",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -q --show-progress -O dnn/classification_classes_ILSVRC2012.txt https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0c577b",
   "metadata": {},
   "source": [
    "Spójrzmy na pierwsze pięć klas w pliku:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb0d0546",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dnn/classification_classes_ILSVRC2012.txt', 'r') as f_fd:\n",
    "    classes = f_fd.read().splitlines()\n",
    "    \n",
    "print(len(classes), classes[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0ee6ff",
   "metadata": {},
   "source": [
    "Do klasyfikacji użyjemy sieci [DenseNet](https://arxiv.org/abs/1608.06993). Pobierzemy jedną z mniejszych [reimplementacji](https://github.com/shicai/DenseNet-Caffe), która jest hostowana m.in. na Google Drive (musimy doinstalować jeden pakiet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb2bf2a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install --user --disable-pip-version-check gdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27996509",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gdown\n",
    "\n",
    "url = 'https://drive.google.com/uc?id=0B7ubpZO7HnlCcHlfNmJkU2VPelE'\n",
    "output = 'dnn/DenseNet_121.caffemodel'\n",
    "gdown.download(url, output, quiet=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "648ec9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -q --show-progress -O dnn/DenseNet_121.prototxt https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_121.prototxt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7294c54",
   "metadata": {},
   "source": [
    "Konkretne biblioteki neuronowe posiadają dedykowane funkcje do ładowania modeli, np. [`readNetFromCaffe()`](https://docs.opencv.org/4.5.3/d6/d0f/group__dnn.html#ga29d0ea5e52b1d1a6c2681e3f7d68473a) lub [`readNetFromTorch()`](https://docs.opencv.org/4.5.3/d6/d0f/group__dnn.html#ga65a1da76cb7d6852bdf7abbd96f19084), jednak można też użyć ogólnej [`readNet()`](https://docs.opencv.org/4.5.3/d6/d0f/group__dnn.html#ga3b34fe7a29494a6a4295c169a7d32422):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fd2d6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cv.dnn.readNet(model='dnn/DenseNet_121.prototxt', config='dnn/DenseNet_121.caffemodel', framework='Caffe')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe22fd6f",
   "metadata": {},
   "source": [
    "Spróbujemy sklasyfikować poniższy obraz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ace4606",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv.imread('img/flamingo.jpg')\n",
    "plt.figure(figsize=[5,5])\n",
    "plt.imshow(image[:,:,::-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e51db3ac",
   "metadata": {},
   "source": [
    "Aby móc przepuścić obraz przez sieć musimy zmienić jego formę reprezentacji poprzez funkcję [`blobFromImage()`](https://docs.opencv.org/4.5.3/d6/d0f/group__dnn.html#ga29f34df9376379a603acd8df581ac8d7). Aby uzyskać sensowne dane musimy ustawić parametry dotyczące preprocessingu (informacje o tym są zawarte na [stronie modelu](https://github.com/shicai/DenseNet-Caffe)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4e945ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_blob = cv.dnn.blobFromImage(image=image, scalefactor=0.017, size=(224, 224), mean=(104, 117, 123), \n",
    "                                  swapRB=False, crop=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625aebdd",
   "metadata": {},
   "source": [
    "Ustawiamy dane wejściowe w naszej sieci i pobieramy obliczone wartości:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "753333a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.setInput(image_blob)\n",
    "outputs = model.forward()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34316ddb",
   "metadata": {},
   "source": [
    "Wyliczamy która klasa jest najbardziej prawdopodobna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13423a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = outputs.reshape(1000, 1)\n",
    "\n",
    "label_id = np.argmax(outputs)\n",
    "\n",
    "probs = np.exp(outputs) / np.sum(np.exp(outputs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874c1b1d",
   "metadata": {},
   "source": [
    "Wynik:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec75a3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(image[:,:,::-1])\n",
    "plt.title(classes[label_id])\n",
    "print(\"{:.2f} %\".format(np.max(probs) * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3808c42c",
   "metadata": {},
   "source": [
    "# Wykrywanie twarzy\n",
    "\n",
    "Do wykrywania twarzy użyjemy sieci bazującej na [SSD](https://github.com/weiliu89/caffe/tree/ssd):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c0df387",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -q --show-progress -O dnn/res10_300x300_ssd_iter_140000_fp16.caffemodel https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel\n",
    "!wget -q --show-progress -O dnn/res10_300x300_ssd_iter_140000_fp16.prototxt https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6142f6e",
   "metadata": {},
   "source": [
    "Ładujemy model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60d41efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cv.dnn.readNet(model='dnn/res10_300x300_ssd_iter_140000_fp16.prototxt', config='dnn/res10_300x300_ssd_iter_140000_fp16.caffemodel', framework='Caffe')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad612cc6",
   "metadata": {},
   "source": [
    "Będziemy chcieli wykryć twarze na poniższym obrazie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b404d8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv.imread('img/people.jpg')\n",
    "plt.figure(figsize=[7,7])\n",
    "plt.imshow(image[:,:,::-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77f8e64",
   "metadata": {},
   "source": [
    "Znajdujemy twarze i oznaczamy je na zdjęciu (za próg przyjęliśmy 0.5; zob. informacje o [preprocessingu](https://github.com/opencv/opencv/tree/master/samples/dnn#face-detection)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d16f230",
   "metadata": {},
   "outputs": [],
   "source": [
    "height, width, _ = image.shape\n",
    "\n",
    "image_blob = cv.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300), mean=[104, 177, 123], \n",
    "                                  swapRB=False, crop=False)\n",
    "\n",
    "model.setInput(image_blob)\n",
    "\n",
    "detections = model.forward()\n",
    "\n",
    "image_out = image.copy()\n",
    "\n",
    "for i in range(detections.shape[2]):\n",
    "    confidence = detections[0, 0, i, 2]\n",
    "    if confidence > 0.5:\n",
    "\n",
    "        box = detections[0, 0, i, 3:7] * np.array([width, height, width, height])\n",
    "        (x1, y1, x2, y2) = box.astype('int')\n",
    "\n",
    "        cv.rectangle(image_out, (x1, y1), (x2, y2), (0, 255, 0), 6)\n",
    "        label = '{:.3f}'.format(confidence)\n",
    "        label_size, base_line = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 3.0, 1)\n",
    "        cv.rectangle(image_out, (x1, y1 - label_size[1]), (x1 + label_size[0], y1 + base_line), \n",
    "                      (255, 255, 255), cv.FILLED)\n",
    "        cv.putText(image_out, label, (x1, y1), cv.FONT_HERSHEY_SIMPLEX, 3.0, (0, 0, 0))\n",
    "        \n",
    "plt.figure(figsize=[12,12])\n",
    "plt.imshow(image_out[:,:,::-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590841cd",
   "metadata": {},
   "source": [
    "## Punkty charakterystyczne twarzy\n",
    "\n",
    "W OpenCV jest możliwość wykrywania punktów charakterystycznych twarzy (ang. *facial landmarks*). Użyjemy zaimplementowanego [modelu](http://www.jiansun.org/papers/CVPR14_FaceAlignment.pdf) podczas Google Summer of Code przy użyciu [`createFacemarkLBF()`](https://docs.opencv.org/4.5.3/d4/d48/namespacecv_1_1face.html#a0bec73a729ed878430c2feb9ce65bc2a):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8534a399",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -q --show-progress -O dnn/lbfmodel.yaml https://raw.githubusercontent.com/kurnianggoro/GSOC2017/master/data/lbfmodel.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2971f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "landmark_detector = cv.face.createFacemarkLBF()\n",
    "landmark_detector.loadModel('dnn/lbfmodel.yaml')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761dbc15",
   "metadata": {},
   "source": [
    "Ograniczamy nasze poszukiwania do twarzy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39215601",
   "metadata": {},
   "outputs": [],
   "source": [
    "faces = []\n",
    "\n",
    "for detection in detections[0][0]:\n",
    "    if detection[2] >= 0.5:\n",
    "        left   = detection[3] * width\n",
    "        top    = detection[4] * height\n",
    "        right  = detection[5] * width\n",
    "        bottom = detection[6] * height\n",
    "\n",
    "        face_w = right - left\n",
    "        face_h = bottom - top\n",
    "\n",
    "        face_roi = (left, top, face_w, face_h)\n",
    "        faces.append(face_roi)\n",
    "\n",
    "faces = np.array(faces).astype(int)\n",
    "\n",
    "_, landmarks_list = landmark_detector.fit(image, faces)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56aa90c9",
   "metadata": {},
   "source": [
    "Model generuje 68 punktów charakterycznych, które możemy zwizualizować:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d3ab726",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_display = image.copy()\n",
    "landmarks = landmarks_list[0][0].astype(int)\n",
    "\n",
    "for idx, landmark in enumerate(landmarks):\n",
    "    cv.circle(image_display, landmark, 2, (0,255,255), -1)\n",
    "    cv.putText(image_display, str(idx), landmark, cv.FONT_HERSHEY_SIMPLEX, 0.35, (0, 255, 0), 1, \n",
    "                cv.LINE_AA)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(image_display[700:1050,500:910,::-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cee8969",
   "metadata": {},
   "source": [
    "Jeśli nie potrzebujemy numeracji, to możemy użyć prostszego podejścia, tj. funkcji [`drawFacemarks()`](https://docs.opencv.org/4.5.3/db/d7c/group__face.html#ga318d9669d5ed4dfc6ab9fae2715310f5):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1039e253",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_display = image.copy()\n",
    "for landmarks_set in landmarks_list:\n",
    "    cv.face.drawFacemarks(image_display, landmarks_set, (0, 255, 0))\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(image_display[500:1050,500:1610,::-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db16a1bf",
   "metadata": {},
   "source": [
    "# Zadanie 1\n",
    "\n",
    "W katalogu `vid` znajdują się filmy `blinking-*.mp4`. Napisz program do wykrywania mrugnięć. Opcjonalnie możesz użyć *eye aspect ratio* z [tego artykułu](http://vision.fe.uni-lj.si/cvww2016/proceedings/papers/05.pdf) lub zaproponować własne rozwiązanie."
   ]
  }
 ],
 "metadata": {
  "author": "Andrzej Wójtowicz",
  "email": "andre@amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "lang": "pl",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "subtitle": "09. Wykrywanie i rozpoznawanie tekstu [laboratoria]",
  "title": "Widzenie komputerowe",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}