notebook

src/CursoAR.ipynb

@@ -99,7 +99,7 @@
     "referenceImage = cv2.imread('../resources/img/patron.png', 0)\n",
     "referenceImage_rgb = cv2.imread('../resources/img/patron.png', 1)\n",
     "\n",
-    "# Show image\n",
+    "# Mostrar Imagen\n",
     "plt.imshow( cv2.cvtColor(referenceImage_rgb, cv2.COLOR_BGR2RGB))\n",
     "plt.show()"
    ]
@@ -134,7 +134,7 @@
     "sourceImage = cv2.imread('../resources/img/escena0.jpg', 0)\n",
     "sourceImage_rgb = cv2.imread('../resources/img/escena0.jpg', 1)\n",
     "\n",
-    "# Show image\n",
+    "# Mostrar Imagen\n",
     "plt.imshow(cv2.cvtColor(sourceImage_rgb, cv2.COLOR_BGR2RGB))\n",
     "plt.show()"
    ]
@@ -243,26 +243,20 @@
    "source": [
     "MIN_MATCHES = 30\n",
     "            \n",
-    "# create brute force  matcher object\n",
+    "# Crear objeto brute force  matcher\n",
     "bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n",
     "\n",
-    "# Compute model keypoints and its descriptors\n",
+    "# Encontrar puntos de interes y generar descriptores\n",
     "referenceImagePts, referenceImageDsc = orb.detectAndCompute(referenceImage, None)\n",
-    "\n",
-    "# Compute scene keypoints and its descriptors\n",
     "sourceImagePts, sourceImageDsc = orb.detectAndCompute(sourceImage, None)\n",
     "\n",
-    "# Match frame descriptors with model descriptors\n",
+    "# Encontrar correspondencias entre puntos de interes\n",
     "matches = bf.match(referenceImageDsc, sourceImageDsc)\n",
-    "\n",
-    "# Sort them in the order of their distance\n",
     "matches = sorted(matches, key=lambda x: x.distance)\n",
     "\n",
     "if len(matches) > MIN_MATCHES:\n",
-    "    # draw first 15 matches.\n",
-    "    idxPairs = cv2.drawMatches(referenceImage_rgb, referenceImagePts, sourceImage_rgb, sourceImagePts,\n",
-    "                          matches[:MIN_MATCHES], 0, flags=2)\n",
-    "    # show result\n",
+    "    idxPairs = cv2.drawMatches(referenceImage_rgb, referenceImagePts, sourceImage_rgb, sourceImagePts, matches[:MIN_MATCHES], 0, flags=2)\n",
+    "    # Mostrar Resultado\n",
     "    plt.figure(figsize=(12, 6))\n",
     "    plt.axis('off')\n",
     "    plt.imshow(cv2.cvtColor(idxPairs, cv2.COLOR_BGR2RGB))\n",
@@ -335,36 +329,34 @@
     }
    ],
    "source": [
-    "# Apply the homography transformation if we have enough good matches\n",
+    "# Aplicar Homografia\n",
     "if len(matches) > MIN_MATCHES:\n",
-    "    # Get the good key points positions\n",
+    "    # Obtener posicion de los puntos de interes\n",
     "    sourcePoints = np.float32([referenceImagePts[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)\n",
     "    destinationPoints = np.float32([sourceImagePts[m.trainIdx].pt for m in matches ]).reshape(-1, 1, 2)\n",
     "\n",
-    "    # Obtain the homography matrix\n",
+    "    #Obtener la matriz de Homografia\n",
     "    homography, mask = cv2.findHomography(sourcePoints, destinationPoints, cv2.RANSAC, 5.0)\n",
     "    matchesMask = mask.ravel().tolist()\n",
     "\n",
-    "    # Apply the perspective transformation to the source image corners\n",
+    "    # Aplicar la transformacion proyectiva a las esquinas de la imagen de referncia\n",
     "    h, w = referenceImage.shape\n",
     "    corners = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)\n",
     "    transformedCorners = cv2.perspectiveTransform(corners, homography)\n",
     "\n",
-    "    # Draw a polygon on the second image joining the transformed corners\n",
-    "    sourceImageMarker = sourceImage_rgb.copy()\n",
-    "    \n",
-    "    sourceImageMarker = cv2.polylines(sourceImageMarker, [np.int32(transformedCorners)], True,\n",
-    "                                      (255,0,0), 5, cv2.LINE_AA)\n",
+    "    #Dibujar poligono uniendo las esquinas proyectadas\n",
+    "    sourceImageMarker = sourceImage_rgb.copy()    \n",
+    "    sourceImageMarker = cv2.polylines(sourceImageMarker, [np.int32(transformedCorners)], True, (255,0,0), 5, cv2.LINE_AA)\n",
     "    \n",
     "else:\n",
     "    print(\"Not enough matches are found - %d/%d\" % (len(matches), MIN_MATCHES))\n",
     "    matchesMask = None\n",
     "\n",
-    "# Draw the matches\n",
+    "# Dibujar Correspondencias\n",
     "drawParameters = dict(matchColor=(0, 255, 0), singlePointColor=None,matchesMask=matchesMask, flags=2)\n",
     "result = cv2.drawMatches(referenceImage_rgb, referenceImagePts, sourceImageMarker, sourceImagePts, matches, None, **drawParameters)\n",
     "print(type(result))\n",
-    "# Show image\n",
+    "# Mostrar Imagen\n",
     "plt.figure(figsize=(12, 6))\n",
     "plt.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))\n",
     "plt.show()"
@@ -414,16 +406,10 @@
    "source": [
     "from objloader_simple import *\n",
     "import math\n",
-    "# Camera parameters\n",
+    "# Matriz de calibracion de la camara\n",
     "camera_parameters = np.array([[852,    0, 387],\n",
     "                              [   0,852, 294],\n",
-    "                              [   0,    0,   1]])\n",
-    "# obtain 3D projection matrix from homography matrix and camera parameters\n",
-    "#projection = projection_matrix(camera_parameters, homography)  \n",
-    "# Load 3D model from OBJ file\n",
-    "obj1 = OBJ('../models/chair.obj', swapyz=True)\n",
-    "#obj = OBJ('../models/knight.obj', swapyz=True)\n",
-    "\n"
+    "                              [   0,    0,   1]])\n"
    ]
   },
   {
@@ -433,7 +419,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# project cube or model\n",
     "def render(img, obj, projection, model, color=False):\n",
     "\n",
     "    vertices = obj.vertices\n",
@@ -579,39 +564,6 @@
     "        plt.imshow(cv2.cvtColor(augmented, cv2.COLOR_BGR2RGB))\n",
     "        plt.axis('off')\n"
    ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "258fea4c-8ef8-4d65-8a8d-c04188ab7e11",
-   "metadata": {},
-   "source": [
-    "# Defining the dimensions of checkerboard\n",
-    "CHECKERBOARD = (6,9)\n",
-    "criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)\n",
-    "\n",
-    "# Creating vector to store vectors of 3D points for each checkerboard image\n",
-    "objpoints = []\n",
-    "# Creating vector to store vectors of 2D points for each checkerboard image\n",
-    "imgpoints = [] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a062d89a-e2d1-4e6f-8360-8806ad65d423",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extracting path of individual image stored in a given directory\n",
-    "images = glob.glob('../images/*.JPG')\n",
-    "for fname in images:\n",
-    "    img = cv2.imread(fname)\n",
-    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
-    "    # Find the chess board corners\n",
-    "    # If desired number of corners are found in the image then ret = true\n",
-    "    ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE)\n",
-    "    print(fname, ret)\n"
-   ]
   }
  ],
  "metadata": {