Commit 8b6c1728 authored by Kelly Chang's avatar Kelly Chang

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

parents 3943682b ef51cf73
...@@ -490,7 +490,57 @@ ...@@ -490,7 +490,57 @@
"id": "a282f9e6", "id": "a282f9e6",
"metadata": {}, "metadata": {},
"outputs": [], "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": { "metadata": {
...@@ -509,7 +559,7 @@ ...@@ -509,7 +559,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.1" "version": "3.8.11"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
...@@ -156,7 +156,7 @@ ...@@ -156,7 +156,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 16,
"id": "d70daa12", "id": "f62c1af6",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -308,7 +308,7 @@ ...@@ -308,7 +308,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 35,
"id": "1cef1036", "id": "9e91c81d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -480,7 +480,7 @@ ...@@ -480,7 +480,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 38,
"id": "424775a4", "id": "d342f424",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -521,10 +521,30 @@ ...@@ -521,10 +521,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 42,
"id": "a9502e22", "id": "a9502e22",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.76234755148326\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"x = np.abs(error.copy())\n", "x = np.abs(error.copy())\n",
"y = diff.copy()\n", "y = diff.copy()\n",
...@@ -576,7 +596,7 @@ ...@@ -576,7 +596,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "1f7d7b6a", "id": "c0bb307b",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -642,7 +662,7 @@ ...@@ -642,7 +662,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "3cf6279a", "id": "671f7847",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -672,12 +692,35 @@ ...@@ -672,12 +692,35 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 39,
"id": "61318477", "id": "eec0746a",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'plt.hexbin(np.abs(e), diff, mincnt=1, bins=\"log\")\\nplt.colorbar()\\nplt.show()\\n\\nplt.hexbin(diff, l, mincnt=1, bins=\"log\")\\nplt.show()\\n\\nplt.hexbin(np.abs(e), .9*diff + .1*l, mincnt=1, bins=\"log\")\\nplt.colorbar()\\nplt.show()'"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"def plot_hist_lstsq(tiff_list, lam=.75):\n", "def plot_hist_lstsq(tiff_list):\n",
"\n", "\n",
" image = tiff_list\n", " image = tiff_list\n",
" image = Image.open(image) #Open the image and read it as an Image object\n", " image = Image.open(image) #Open the image and read it as an Image object\n",
...@@ -693,14 +736,12 @@ ...@@ -693,14 +736,12 @@
" y1 = np.ravel(z0+z1+z2)\n", " y1 = np.ravel(z0+z1+z2)\n",
" y2 = np.ravel(-z0-z1-z2-z3)\n", " y2 = np.ravel(-z0-z1-z2-z3)\n",
" y = np.vstack((y0,y1,y2))\n", " y = np.vstack((y0,y1,y2))\n",
" # use numpy solver to solve the system of equations all at once\n",
" #predict = np.floor(np.linalg.solve(A,y)[-1])\n",
" predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1))\n",
" \n", " \n",
" points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])\n", " # use numpy solver to solve the system of equations all at once\n",
" #fit = la.solve(A,y)\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", " \n",
" #mse_start = (points@fit).T\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",
" \n", " \n",
" # flatten the neighbor pixels and stack them together\n", " # flatten the neighbor pixels and stack them together\n",
...@@ -712,8 +753,6 @@ ...@@ -712,8 +753,6 @@
" \n", " \n",
" f, res, rank, s = la.lstsq(points, neighbor.T, rcond=None) \n", " f, res, rank, s = la.lstsq(points, neighbor.T, rcond=None) \n",
" \n", " \n",
" #mse_finish = (neighbor-mse_start)**2\n",
" #lstsqur = np.sum(mse_finish, axis=1) / 4\n",
" \n", " \n",
" # calculate the difference\n", " # calculate the difference\n",
" diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n", " diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
...@@ -722,32 +761,21 @@ ...@@ -722,32 +761,21 @@
" image = np.ravel(image[1:-1,1:-1])\n", " image = np.ravel(image[1:-1,1:-1])\n",
" error = image-predict\n", " error = image-predict\n",
" \n", " \n",
" return image, predict, res, error, A, diff, (lam*l + (1-lam)*d)\n", " return image, predict, res, error, A, diff\n",
"\n", "\n",
"i, p, l, e, A, d, lam = plot_hist_lstsq(images[0], .75)\n", "i, p, l, e, A, d = plot_hist_lstsq(images[0])\n",
"\n", "\n",
"plt.hexbin(np.abs(e), lam, mincnt=1, bins=\"log\")\n", "plt.hexbin(np.abs(e), l, mincnt=1, bins=\"log\")\n",
"plt.xlabel(\"Error\")\n", "plt.xlabel(\"Error\")\n",
"plt.ylabel(\"Lst Sqr Residual\")\n", "plt.ylabel(\"Lst Sqr Residual\")\n",
"plt.colorbar()\n", "plt.colorbar()\n",
"plt.show()\n", "plt.show()\n"
"\n",
"\"\"\"plt.hexbin(np.abs(e), diff, mincnt=1, bins=\"log\")\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n",
"plt.hexbin(diff, l, mincnt=1, bins=\"log\")\n",
"plt.show()\n",
"\n",
"plt.hexbin(np.abs(e), .9*diff + .1*l, mincnt=1, bins=\"log\")\n",
"plt.colorbar()\n",
"plt.show()\"\"\"\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "c396601f", "id": "700f6e7f",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -769,12 +797,22 @@ ...@@ -769,12 +797,22 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 43,
"id": "081f2d67", "id": "0c297da9",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.0392286797151855\n",
"5.304096657944529\n",
"15\n"
]
}
],
"source": [ "source": [
"imm, p, res, e, A, d, lam = plot_hist_lstsq(images[0], 1)\n", "imm, p, res, e, A, d = plot_hist_lstsq(images[0])\n",
"res = res.astype(int)\n", "res = res.astype(int)\n",
"uni = np.unique(res)\n", "uni = np.unique(res)\n",
"uni_n = len(np.unique(res))\n", "uni_n = len(np.unique(res))\n",
...@@ -790,10 +828,19 @@ ...@@ -790,10 +828,19 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 44,
"id": "82337dfb", "id": "d7fc288d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.9301269429408086\n",
"0.262008\n"
]
}
],
"source": [ "source": [
"fre = rel_freq(list(res))\n", "fre = rel_freq(list(res))\n",
"print(np.array(fre)@np.array(entropy))\n", "print(np.array(fre)@np.array(entropy))\n",
......
...@@ -493,7 +493,57 @@ ...@@ -493,7 +493,57 @@
"id": "a282f9e6", "id": "a282f9e6",
"metadata": {}, "metadata": {},
"outputs": [], "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": { "metadata": {
...@@ -512,7 +562,7 @@ ...@@ -512,7 +562,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.1" "version": "3.8.11"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
...@@ -156,7 +156,7 @@ ...@@ -156,7 +156,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 16,
"id": "d70daa12", "id": "f62c1af6",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -308,7 +308,7 @@ ...@@ -308,7 +308,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 35,
"id": "1cef1036", "id": "9e91c81d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -480,7 +480,7 @@ ...@@ -480,7 +480,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 38,
"id": "424775a4", "id": "d342f424",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -521,10 +521,30 @@ ...@@ -521,10 +521,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 42,
"id": "a9502e22", "id": "a9502e22",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.76234755148326\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"x = np.abs(error.copy())\n", "x = np.abs(error.copy())\n",
"y = diff.copy()\n", "y = diff.copy()\n",
...@@ -576,7 +596,7 @@ ...@@ -576,7 +596,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "1f7d7b6a", "id": "c0bb307b",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -642,7 +662,7 @@ ...@@ -642,7 +662,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "3cf6279a", "id": "671f7847",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -672,12 +692,35 @@ ...@@ -672,12 +692,35 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 39,
"id": "61318477", "id": "eec0746a",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'plt.hexbin(np.abs(e), diff, mincnt=1, bins=\"log\")\\nplt.colorbar()\\nplt.show()\\n\\nplt.hexbin(diff, l, mincnt=1, bins=\"log\")\\nplt.show()\\n\\nplt.hexbin(np.abs(e), .9*diff + .1*l, mincnt=1, bins=\"log\")\\nplt.colorbar()\\nplt.show()'"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"def plot_hist_lstsq(tiff_list, lam=.75):\n", "def plot_hist_lstsq(tiff_list):\n",
"\n", "\n",
" image = tiff_list\n", " image = tiff_list\n",
" image = Image.open(image) #Open the image and read it as an Image object\n", " image = Image.open(image) #Open the image and read it as an Image object\n",
...@@ -693,14 +736,12 @@ ...@@ -693,14 +736,12 @@
" y1 = np.ravel(z0+z1+z2)\n", " y1 = np.ravel(z0+z1+z2)\n",
" y2 = np.ravel(-z0-z1-z2-z3)\n", " y2 = np.ravel(-z0-z1-z2-z3)\n",
" y = np.vstack((y0,y1,y2))\n", " y = np.vstack((y0,y1,y2))\n",
" # use numpy solver to solve the system of equations all at once\n",
" #predict = np.floor(np.linalg.solve(A,y)[-1])\n",
" predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1))\n",
" \n", " \n",
" points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])\n", " # use numpy solver to solve the system of equations all at once\n",
" #fit = la.solve(A,y)\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", " \n",
" #mse_start = (points@fit).T\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",
" \n", " \n",
" # flatten the neighbor pixels and stack them together\n", " # flatten the neighbor pixels and stack them together\n",
...@@ -712,8 +753,6 @@ ...@@ -712,8 +753,6 @@
" \n", " \n",
" f, res, rank, s = la.lstsq(points, neighbor.T, rcond=None) \n", " f, res, rank, s = la.lstsq(points, neighbor.T, rcond=None) \n",
" \n", " \n",
" #mse_finish = (neighbor-mse_start)**2\n",
" #lstsqur = np.sum(mse_finish, axis=1) / 4\n",
" \n", " \n",
" # calculate the difference\n", " # calculate the difference\n",
" diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n", " diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
...@@ -722,32 +761,21 @@ ...@@ -722,32 +761,21 @@
" image = np.ravel(image[1:-1,1:-1])\n", " image = np.ravel(image[1:-1,1:-1])\n",
" error = image-predict\n", " error = image-predict\n",
" \n", " \n",
" return image, predict, res, error, A, diff, (lam*l + (1-lam)*d)\n", " return image, predict, res, error, A, diff\n",
"\n", "\n",
"i, p, l, e, A, d, lam = plot_hist_lstsq(images[0], .75)\n", "i, p, l, e, A, d = plot_hist_lstsq(images[0])\n",
"\n", "\n",
"plt.hexbin(np.abs(e), lam, mincnt=1, bins=\"log\")\n", "plt.hexbin(np.abs(e), l, mincnt=1, bins=\"log\")\n",
"plt.xlabel(\"Error\")\n", "plt.xlabel(\"Error\")\n",
"plt.ylabel(\"Lst Sqr Residual\")\n", "plt.ylabel(\"Lst Sqr Residual\")\n",
"plt.colorbar()\n", "plt.colorbar()\n",
"plt.show()\n", "plt.show()\n"
"\n",
"\"\"\"plt.hexbin(np.abs(e), diff, mincnt=1, bins=\"log\")\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n",
"plt.hexbin(diff, l, mincnt=1, bins=\"log\")\n",
"plt.show()\n",
"\n",
"plt.hexbin(np.abs(e), .9*diff + .1*l, mincnt=1, bins=\"log\")\n",
"plt.colorbar()\n",
"plt.show()\"\"\"\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "c396601f", "id": "700f6e7f",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -769,12 +797,22 @@ ...@@ -769,12 +797,22 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 43,
"id": "081f2d67", "id": "0c297da9",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.0392286797151855\n",
"5.304096657944529\n",
"15\n"
]
}
],
"source": [ "source": [
"imm, p, res, e, A, d, lam = plot_hist_lstsq(images[0], 1)\n", "imm, p, res, e, A, d = plot_hist_lstsq(images[0])\n",
"res = res.astype(int)\n", "res = res.astype(int)\n",
"uni = np.unique(res)\n", "uni = np.unique(res)\n",
"uni_n = len(np.unique(res))\n", "uni_n = len(np.unique(res))\n",
...@@ -790,10 +828,19 @@ ...@@ -790,10 +828,19 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 44,
"id": "82337dfb", "id": "d7fc288d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.9301269429408086\n",
"0.262008\n"
]
}
],
"source": [ "source": [
"fre = rel_freq(list(res))\n", "fre = rel_freq(list(res))\n",
"print(np.array(fre)@np.array(entropy))\n", "print(np.array(fre)@np.array(entropy))\n",
......
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