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Image Compression
Commit 5c7cf95e authored by Kelly Chang's avatar Kelly Chang
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kelly changes

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Showing with 76 additions and 90 deletions
.ipynb_checkpoints/compression_jupyter-checkpoint.ipynb → .ipynb_checkpoints/multiple_compression_kelly-checkpoint.ipynb
View file @ 5c7cf95e
......@@ -213,7 +213,7 @@
},
{
"cell_type": "markdown",
"id": "d866df6f",
"id": "5437bd8d",
"metadata": {},
"source": [
"## use MSE"
......@@ -221,7 +221,7 @@
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 50,
"id": "73412bc9",
"metadata": {},
"outputs": [],
......@@ -233,47 +233,38 @@
" fix this later. 1/25/22\n",
" \"\"\"\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",
" row, col = image.shape\n",
" predict = np.empty([row,col]) # create a empty matrix to update prediction\n",
" predict[0,:] = np.copy(image[0,:]) # keep the first row from the image\n",
" predict[:,0] = np.copy(image[:,0]) # keep the first columen from the image\n",
" predict[-1,:] = np.copy(image[-1,:]) # keep the first row from the image\n",
" predict[:,-1] = np.copy(image[:,-1]) # keep the first columen from the image\n",
" diff = np.empty([row,col])\n",
" diff[0,:] = np.zeros(col) # keep the first row from the image\n",
" diff[:,0] = np.zeros(row)\n",
" diff[-1,:] = np.zeros(col) # keep the first row from the image\n",
" diff[:,-1] = np.zeros(row)\n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
" z0 = image[0:-2,0:-2]\n",
" z1 = image[0:-2,1:-1]\n",
" z2 = image[0:-2,2::]\n",
" z3 = image[1:-1,0:-2]\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",
" # use numpy solver to solve the system of equations all at once\n",
" predict = np.linalg.solve(A,y)[-1]\n",
" \n",
" # flatten the neighbor pixlels 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",
" # 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",
" return image, predict, diff"
]
},
{
"cell_type": "code",
"execution_count": 44,
"execution_count": 51,
"id": "1bc0bed5",
"metadata": {},
"outputs": [],
......@@ -287,8 +278,8 @@
},
{
"cell_type": "code",
"execution_count": 46,
"id": "a8c46fb2",
"execution_count": 52,
"id": "0f908e6c",
"metadata": {},
"outputs": [
{
......@@ -296,7 +287,7 @@
"output_type": "stream",
"text": [
"mean of error = 18.740279058331875\n",
"time to run = 0.06311893463134766\n"
"time to run = 0.07091403007507324\n"
]
},
{
......@@ -330,7 +321,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "0c73bbe2",
"id": "fb798c32",
"metadata": {},
"outputs": [],
"source": []
......@@ -338,7 +329,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1f19bb5d",
"id": "23f8c8a7",
"metadata": {},
"outputs": [],
"source": []
......
.ipynb_checkpoints/prediction_MSE-checkpoint.ipynb → .ipynb_checkpoints/prediction_MSE_kelly-checkpoint.ipynb
View file @ 5c7cf95e
......@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 44,
"id": "b7a550e0",
"metadata": {},
"outputs": [],
......@@ -74,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 41,
"execution_count": 51,
"id": "9ed20f84",
"metadata": {},
"outputs": [],
......@@ -91,33 +91,35 @@
" 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",
" row, col = image.shape\n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
" z0 = image[0:-2,0:-2]\n",
" z1 = image[0:-2,1:-1]\n",
" z2 = image[0:-2,2::]\n",
" z3 = image[1:-1,0:-2]\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",
" predict = np.linalg.solve(A,y[:10])[-1]\n",
" \n",
" # use numpy solver to solve the system of equations all at once\n",
" predict = np.linalg.solve(A,y)[-1]\n",
" # flatten the neighbor pixlels 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",
" diff = np.max(neighbor,axis = 1) - np.max(neighbor, axis=1)\n",
" \n",
" # calculate the difference\n",
" diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
" # flatten the image to a vector\n",
" image = np.ravel(image[1:-1,1:-1])\n",
" return image, predict, diff"
" return image, predict, diff\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 49,
"id": "8e3ef654",
"metadata": {},
"outputs": [],
......@@ -150,7 +152,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 52,
"id": "fa65dcd6",
"metadata": {},
"outputs": [
......@@ -160,8 +162,8 @@
"text": [
"Average Error First and Second Added: 20.017164930235474\n",
"Standard Deviaiton of Mean Errors: 0.16101183692475135\n",
"Average Difference: -53.678648426455226\n",
"Average Time per Image for First: 0.05081495642662048\n"
"Average Difference: 53.678648426455226\n",
"Average Time per Image for First: 0.04535740613937378\n"
]
}
],
......
compression_jupyter.ipynb → multiple_compression_kelly.ipynb
View file @ 5c7cf95e
......@@ -213,7 +213,7 @@
},
{
"cell_type": "markdown",
"id": "d866df6f",
"id": "5437bd8d",
"metadata": {},
"source": [
"## use MSE"
......@@ -221,7 +221,7 @@
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 50,
"id": "73412bc9",
"metadata": {},
"outputs": [],
......@@ -233,47 +233,38 @@
" fix this later. 1/25/22\n",
" \"\"\"\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",
" row, col = image.shape\n",
" predict = np.empty([row,col]) # create a empty matrix to update prediction\n",
" predict[0,:] = np.copy(image[0,:]) # keep the first row from the image\n",
" predict[:,0] = np.copy(image[:,0]) # keep the first columen from the image\n",
" predict[-1,:] = np.copy(image[-1,:]) # keep the first row from the image\n",
" predict[:,-1] = np.copy(image[:,-1]) # keep the first columen from the image\n",
" diff = np.empty([row,col])\n",
" diff[0,:] = np.zeros(col) # keep the first row from the image\n",
" diff[:,0] = np.zeros(row)\n",
" diff[-1,:] = np.zeros(col) # keep the first row from the image\n",
" diff[:,-1] = np.zeros(row)\n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
" z0 = image[0:-2,0:-2]\n",
" z1 = image[0:-2,1:-1]\n",
" z2 = image[0:-2,2::]\n",
" z3 = image[1:-1,0:-2]\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",
" # use numpy solver to solve the system of equations all at once\n",
" predict = np.linalg.solve(A,y)[-1]\n",
" \n",
" # flatten the neighbor pixlels 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",
" # 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",
" return image, predict, diff"
]
},
{
"cell_type": "code",
"execution_count": 44,
"execution_count": 51,
"id": "1bc0bed5",
"metadata": {},
"outputs": [],
......@@ -287,8 +278,8 @@
},
{
"cell_type": "code",
"execution_count": 46,
"id": "a8c46fb2",
"execution_count": 52,
"id": "0f908e6c",
"metadata": {},
"outputs": [
{
......@@ -296,7 +287,7 @@
"output_type": "stream",
"text": [
"mean of error = 18.740279058331875\n",
"time to run = 0.06311893463134766\n"
"time to run = 0.07091403007507324\n"
]
},
{
......@@ -330,7 +321,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "0c73bbe2",
"id": "fb798c32",
"metadata": {},
"outputs": [],
"source": []
......@@ -338,7 +329,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1f19bb5d",
"id": "23f8c8a7",
"metadata": {},
"outputs": [],
"source": []
......
prediction_MSE.ipynb → prediction_MSE_kelly.ipynb
View file @ 5c7cf95e
......@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 44,
"id": "b7a550e0",
"metadata": {},
"outputs": [],
......@@ -74,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 41,
"execution_count": 51,
"id": "9ed20f84",
"metadata": {},
"outputs": [],
......@@ -91,33 +91,35 @@
" 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",
" row, col = image.shape\n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
" z0 = image[0:-2,0:-2]\n",
" z1 = image[0:-2,1:-1]\n",
" z2 = image[0:-2,2::]\n",
" z3 = image[1:-1,0:-2]\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",
" predict = np.linalg.solve(A,y[:10])[-1]\n",
" \n",
" # use numpy solver to solve the system of equations all at once\n",
" predict = np.linalg.solve(A,y)[-1]\n",
" # flatten the neighbor pixlels 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",
" diff = np.max(neighbor,axis = 1) - np.max(neighbor, axis=1)\n",
" \n",
" # calculate the difference\n",
" diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
" # flatten the image to a vector\n",
" image = np.ravel(image[1:-1,1:-1])\n",
" return image, predict, diff"
" return image, predict, diff\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 49,
"id": "8e3ef654",
"metadata": {},
"outputs": [],
......@@ -150,7 +152,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 52,
"id": "fa65dcd6",
"metadata": {},
"outputs": [
......@@ -160,8 +162,8 @@
"text": [
"Average Error First and Second Added: 20.017164930235474\n",
"Standard Deviaiton of Mean Errors: 0.16101183692475135\n",
"Average Difference: -53.678648426455226\n",
"Average Time per Image for First: 0.05081495642662048\n"
"Average Difference: 53.678648426455226\n",
"Average Time per Image for First: 0.04535740613937378\n"
]
}
],
......
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