Merge branch 'master' of https://git.elphel.com/Elphel/master

.ipynb_checkpoints/Encoding_decoding-checkpoint.ipynb

@@ -490,7 +490,57 @@
    "id": "a282f9e6",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "def predict_pix_lstsq(tiff_list):\n",
+    "    \"\"\"\n",
+    "    Predict the next pixel using a fit hyperplane of the four closest pixels.\n",
+    "    The gradient measure in this function is the summed distance to the fitted hyperplane\n",
+    "    of each of the four points, aka the residual from the least squares function. The previous\n",
+    "    predict_pix function uses the difference between the minimal and maximal pixels of the surrounding\n",
+    "    four.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    image = tiff_list\n",
+    "    image = Image.open(image)    #Open the image and read it as an Image object\n",
+    "    image = np.array(image)[1:,:]    #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
+    "    image = image.astype(int)\n",
+    "    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
+    "    z0 = image[0:-2,0:-2]   # get all the first pixel for the entire image\n",
+    "    z1 = image[0:-2,1:-1]   # get all the second pixel for the entire image\n",
+    "    z2 = image[0:-2,2::]    # get all the third pixel for the entire image\n",
+    "    z3 = image[1:-1,0:-2]   # get all the forth pixel for the entire image\n",
+    "    # calculate the out put of the system of equation\n",
+    "    y0 = np.ravel(-z0+z2-z3)\n",
+    "    y1 = np.ravel(z0+z1+z2)\n",
+    "    y2 = np.ravel(-z0-z1-z2-z3)\n",
+    "    y = np.vstack((y0,y1,y2))\n",
+    "    \n",
+    "    # use numpy solver to solve the system of equations all at once\n",
+    "    predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1)) #round the solution to the nearest integer so that encoding/decoding is easier\n",
+    "    \n",
+    "    points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]]) #Matrix system of points that will be used to solve the least squares fitting hyperplane\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    # flatten the neighbor pixels and stack them together\n",
+    "    z0 = np.ravel(z0)\n",
+    "    z1 = np.ravel(z1)\n",
+    "    z2 = np.ravel(z2)\n",
+    "    z3 = np.ravel(z3)\n",
+    "    neighbor = np.vstack((z0,z1,z2,z3)).T\n",
+    "    \n",
+    "    f, res, rank, s = la.lstsq(points, neighbor.T, rcond=None) \n",
+    "    \n",
+    "    \n",
+    "    # calculate the difference\n",
+    "    diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
+    "    \n",
+    "    # flatten the image to a vector\n",
+    "    image = np.ravel(image[1:-1,1:-1])\n",
+    "    error = image-predict\n",
+    "    \n",
+    "    return image, predict, res, error, A, diff"
+   ]
   }
  ],
  "metadata": {
@@ -509,7 +559,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,

Encoding_decoding.ipynb

@@ -493,7 +493,57 @@
    "id": "a282f9e6",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "def predict_pix_lstsq(tiff_list):\n",
+    "    \"\"\"\n",
+    "    Predict the next pixel using a fit hyperplane of the four closest pixels.\n",
+    "    The gradient measure in this function is the summed distance to the fitted hyperplane\n",
+    "    of each of the four points, aka the residual from the least squares function. The previous\n",
+    "    predict_pix function uses the difference between the minimal and maximal pixels of the surrounding\n",
+    "    four.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    image = tiff_list\n",
+    "    image = Image.open(image)    #Open the image and read it as an Image object\n",
+    "    image = np.array(image)[1:,:]    #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
+    "    image = image.astype(int)\n",
+    "    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
+    "    z0 = image[0:-2,0:-2]   # get all the first pixel for the entire image\n",
+    "    z1 = image[0:-2,1:-1]   # get all the second pixel for the entire image\n",
+    "    z2 = image[0:-2,2::]    # get all the third pixel for the entire image\n",
+    "    z3 = image[1:-1,0:-2]   # get all the forth pixel for the entire image\n",
+    "    # calculate the out put of the system of equation\n",
+    "    y0 = np.ravel(-z0+z2-z3)\n",
+    "    y1 = np.ravel(z0+z1+z2)\n",
+    "    y2 = np.ravel(-z0-z1-z2-z3)\n",
+    "    y = np.vstack((y0,y1,y2))\n",
+    "    \n",
+    "    # use numpy solver to solve the system of equations all at once\n",
+    "    predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1)) #round the solution to the nearest integer so that encoding/decoding is easier\n",
+    "    \n",
+    "    points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]]) #Matrix system of points that will be used to solve the least squares fitting hyperplane\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    # flatten the neighbor pixels and stack them together\n",
+    "    z0 = np.ravel(z0)\n",
+    "    z1 = np.ravel(z1)\n",
+    "    z2 = np.ravel(z2)\n",
+    "    z3 = np.ravel(z3)\n",
+    "    neighbor = np.vstack((z0,z1,z2,z3)).T\n",
+    "    \n",
+    "    f, res, rank, s = la.lstsq(points, neighbor.T, rcond=None) \n",
+    "    \n",
+    "    \n",
+    "    # calculate the difference\n",
+    "    diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
+    "    \n",
+    "    # flatten the image to a vector\n",
+    "    image = np.ravel(image[1:-1,1:-1])\n",
+    "    error = image-predict\n",
+    "    \n",
+    "    return image, predict, res, error, A, diff"
+   ]
   }
  ],
  "metadata": {
@@ -512,7 +562,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,