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ipcv/assignment/assignment_IMG_9056.ipynb

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add: assignment
2019-05-17 02:33:41 +07:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Recompiling stale cache file /home/luibo/.julia/compiled/v1.2/ImageFeatures/JMzL1.ji for ImageFeatures [92ff4b2b-8094-53d3-b29d-97f740f06cef]\n",
"└ @ Base loading.jl:1187\n",
"┌ Warning: Package Distributions does not have Test in its dependencies:\n",
"│ - If you have Distributions checked out for development and have\n",
"│ added Test as a dependency but haven't updated your primary\n",
"│ environment's manifest file, try `Pkg.resolve()`.\n",
"│ - Otherwise you may need to report an issue with Distributions\n",
"└ Loading Test into Distributions from project dependency, future warnings for Distributions are suppressed.\n",
"┌ Warning: The call to compilecache failed to create a usable precompiled cache file for ImageFeatures [92ff4b2b-8094-53d3-b29d-97f740f06cef]\n",
"│ exception = ErrorException(\"Required dependency Distributions [31c24e10-a181-5473-b8eb-7969acd0382f] failed to load from a cache file.\")\n",
"└ @ Base loading.jl:972\n",
"┌ Info: Recompiling stale cache file /home/luibo/.julia/compiled/v1.2/Distributions/xILW0.ji for Distributions [31c24e10-a181-5473-b8eb-7969acd0382f]\n",
"└ @ Base loading.jl:1187\n",
"┌ Warning: Package Distributions does not have Test in its dependencies:\n",
"│ - If you have Distributions checked out for development and have\n",
"│ added Test as a dependency but haven't updated your primary\n",
"│ environment's manifest file, try `Pkg.resolve()`.\n",
"│ - Otherwise you may need to report an issue with Distributions\n",
"└ Loading Test into Distributions from project dependency, future warnings for Distributions are suppressed.\n"
]
}
],
"source": [
"using Images\n",
"using TestImages\n",
"using FileIO\n",
"using Colors\n",
"using ImageFeatures\n",
"using Formatting\n",
"# using Plots\n",
"using PyPlot\n",
"using DSP\n",
"using FixedPointNumbers\n",
"using Statistics"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"projectiveTransform (generic function with 1 method)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function backwardTransform(algo, M, P, outSize=nothing)\n",
" M = inv(M)\n",
" rows, cols = size(P)\n",
" if outSize == nothing\n",
" outSize = size(P)\n",
" end\n",
" out = zeros(eltype(P), outSize)\n",
"\n",
" for index in CartesianIndices(out)\n",
" y, x = Tuple(index)\n",
" sourceIndex = M * [x; y; 1]\n",
" sourceIndex ./= sourceIndex[end]\n",
" x1, y1 = round.(Int32, sourceIndex)\n",
" if (x1 < 1 || y1 < 1 || x1 > cols || y1 > rows)\n",
" continue\n",
" end\n",
" out[index] = P[y1, x1]\n",
" end\n",
" out\n",
"end\n",
"\n",
"function createProjectiveMatrix(points)\n",
" if length(points) != 4\n",
" error(\"Not correct number of point\")\n",
" end\n",
"\n",
" A = zeros(Int64, 2*length(points), 8)\n",
" B = zeros(Int64, 2*length(points), 1)\n",
" for i in 1:length(points)\n",
" x, y, x1, y1 = points[i]\n",
" A[2*i - 1,:] = [x y 1 0 0 0 -x*x1 -x1*y]\n",
" A[2*i,:] = [0 0 0 x y 1 -x*y1 -y*y1]\n",
" B[2*i - 1] = x1\n",
" B[2*i] = y1\n",
" end\n",
"\n",
" H = vcat(A\\B, 1)\n",
" reshape(H, (3,3))'\n",
"end\n",
"\n",
"function projectiveTransform(picture, points, outSize)\n",
" M = createProjectiveMatrix(points)\n",
" backwardTransform(\"zero\", M, picture, outSize)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"hough (generic function with 1 method)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function hough(img)\n",
" rows, cols = size(img)\n",
" D = round(Int32, sqrt(rows ^ 2 + cols ^ 2))\n",
" rhos = -D:1:D\n",
" thetas = deg2rad.(-90:1:90)\n",
"\n",
" A = zeros(Int32, length(rhos), length(thetas))\n",
" sins, coss = sin.(thetas), cos.(thetas)\n",
" \n",
" for idx in CartesianIndices(img)\n",
" if img[idx] == 0\n",
" # 0 or false\n",
" continue\n",
" end\n",
"\n",
" y, x = Tuple(idx)\n",
" for thetaIdx in 1:length(thetas)\n",
" rho = x * coss[thetaIdx] + y * sins[thetaIdx]\n",
" rho = round(Int32, rho)\n",
" A[rho + D, thetaIdx] += 1\n",
" end\n",
" end\n",
" \n",
" rowsA, colsA = size(A)\n",
" for idx in CartesianIndices(A)\n",
" y, x = Tuple(idx)\n",
" if x == 1 || y == 1 || x == colsA || y == rowsA\n",
" continue\n",
" end\n",
" C = A[idx]\n",
" C1 = (C < A[y - 1, x - 1]) || (C < A[y - 1, x])\n",
" C2 = (C < A[y - 1, x + 1]) || (C < A[y, x + 1])\n",
" C3 = (C < A[y + 1, x + 1]) || (C < A[y + 1, x])\n",
" C4 = (C < A[y + 1, x - 1]) || (C < A[y, x - 1])\n",
" if C1 || C2 || C3 || C4\n",
" A[idx] = 0\n",
" end\n",
" end\n",
"\n",
" A, rhos, thetas\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3×3 Array{Int64,2}:\n",
" 1 2 1\n",
" 0 0 0\n",
" -1 -2 -1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gaussian_kernel = [\n",
" 1 4 6 4 1\n",
" 4 16 24 16 4\n",
" 6 24 36 24 6\n",
" 4 16 24 16 4\n",
" 1 4 6 4 1\n",
"] ./ 256\n",
"\n",
"sobel_x_kernel = [\n",
" -1 0 1\n",
" -2 0 2\n",
" -1 0 1\n",
"]\n",
"sobel_y_kernel = rotl90(sobel_x_kernel)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"ename": "InexactError",
"evalue": "InexactError: trunc(Int32, -3.439150402e9)",
"output_type": "error",
"traceback": [
"InexactError: trunc(Int32, -3.439150402e9)",
"",
"Stacktrace:",
" [1] trunc at ./float.jl:675 [inlined]",
" [2] round at ./float.jl:361 [inlined]",
" [3] _broadcast_getindex_evalf at ./broadcast.jl:624 [inlined]",
" [4] _broadcast_getindex at ./broadcast.jl:607 [inlined]",
" [5] getindex at ./broadcast.jl:557 [inlined]",
" [6] macro expansion at ./broadcast.jl:887 [inlined]",
" [7] macro expansion at ./simdloop.jl:73 [inlined]",
" [8] copyto! at ./broadcast.jl:886 [inlined]",
" [9] copyto! at ./broadcast.jl:841 [inlined]",
" [10] copy at ./broadcast.jl:817 [inlined]",
" [11] materialize at ./broadcast.jl:797 [inlined]",
" [12] backwardTransform(::String, ::LinearAlgebra.Adjoint{Float64,Array{Float64,2}}, ::Array{RGB{Normed{UInt8,8}},2}, ::Tuple{Int64,Int64}) at ./In[3]:13",
" [13] projectiveTransform(::Array{RGB{Normed{UInt8,8}},2}, ::Array{Array{Int64,2},1}, ::Tuple{Int64,Int64}) at ./In[3]:43",
" [14] top-level scope at In[35]:14"
]
}
],
"source": [
"img_file = \"IPCV-TestImage/IMG_9056.JPG\"\n",
"rm(\"output/$(img_file)\", recursive=true, force=true)\n",
"mkpath(\"output/$(img_file)\")\n",
"\n",
"img = load(img_file)\n",
"A4size = (3508, 2480)\n",
"A4row, A4col = A4size\n",
"points = [\n",
" [3060 492 1 1],\n",
" [3257 2199 A4col 1],\n",
" [536 970 A4col A4row],\n",
" [978 2572 1 A4row]\n",
"]\n",
"img = projectiveTransform(img, points, A4size)\n",
"\n",
"display(img)\n",
"\n",
"img_gray = Float64.(Gray.(img))\n",
"sobel_x_kernel = Float64.(sobel_x_kernel)\n",
"sobel_y_kernel = Float64.(sobel_y_kernel)\n",
"\n",
"img_blur = conv2(img_gray, gaussian_kernel)\n",
"img_edge_x = conv2(img_blur, sobel_x_kernel)\n",
"img_edge_y = conv2(img_blur, sobel_y_kernel)\n",
"\n",
"img_edge = round.(Int32, sqrt.((img_edge_x .^ 2) .+ (img_edge_y .^ 2)))\n",
"img_edge[img_edge .> 0] .= 1\n",
"\n",
"# img_canny = canny(img, (Percentile(90), Percentile(10)))\n",
"# display(img_canny)\n",
"\n",
"\n",
"img_bool = zeros(Bool, size(img_edge))\n",
"img_bool[img_edge .> 0] .= true\n",
"display(Gray.(img_edge))\n",
"display()"
]
},
{
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"source": [
"acc, rhos, thetas = hough(img_edge)\n",
"for i in 1:50\n",
" r_idx, t_idx = Tuple(argmax(acc))\n",
" acc[r_idx, t_idx] = 0\n",
" display((rhos[r_idx], rad2deg(thetas[t_idx])))\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"Figure(PyObject <Figure size 640x480 with 1 Axes>)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"acc, rhos, thetas = hough(img_edge)\n",
"rows, cols = size(img)\n",
"my_dpi = 96\n",
"\n",
"plt = PyPlot\n",
"# PyPlot.svg(true)\n",
"plt.figure(figsize=(cols/my_dpi, rows/my_dpi), dpi=my_dpi)\n",
"\n",
"plot_img = plt.imread(img_file)\n",
"fig, ax = plt.subplots()\n",
"# fig = plt.figure(figsize=(cols/2, rows/2))\n",
"# fig.set_size_inches(cols,rows)\n",
"\n",
"xs = []\n",
"ys = []\n",
"\n",
"ax.imshow(plot_img)\n",
"while true\n",
" r_idx, t_idx = Tuple(argmax(acc))\n",
" if (acc[r_idx, t_idx] < 800)\n",
" break\n",
" end\n",
" acc[r_idx, t_idx] = 0\n",
"\n",
" rho, theta = rhos[r_idx], thetas[t_idx]\n",
" theta = rad2deg(theta)\n",
" # display(theta)\n",
" if (theta == 90 || theta == -90)\n",
" y = rho / sind(theta)\n",
" ax.axhline(y, linewidth=1)\n",
" push!(ys,y)\n",
" elseif (theta == 0)\n",
" x = rho\n",
" ax.axvline(x, linewidth=1)\n",
" push!(xs,x)\n",
" else\n",
" continue\n",
" end\n",
"end\n",
"savefig(\"output/$(img_file)/hough.svg\", dpi=my_dpi * 20)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3-element Array{Any,1}:\n",
" 2\n",
" 2482\n",
" 2484"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"12-element Array{Any,1}:\n",
" 1.0\n",
" 2.0\n",
" 3.0\n",
" 3.0\n",
" 4.0\n",
" 4.0\n",
" 5.0\n",
" 6.0\n",
" 3510.0\n",
" 3511.0\n",
" 3512.0\n",
" 3513.0"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"Figure(PyObject <Figure size 640x480 with 1 Axes>)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs = sort(xs)\n",
"ys = sort(ys)\n",
"\n",
"display(xs)\n",
"display(ys)\n",
"\n",
"function get_arround(arr)\n",
" out = Float64[]\n",
" tmp = Float64[]\n",
" last = 0\n",
" for val in arr\n",
" if last == 0\n",
" last = val\n",
" continue\n",
" end\n",
" push!(tmp, val)\n",
" if abs(val - last) > 10\n",
" # push!(out, round(Int32,mean(tmp)))\n",
" push!(out, tmp[1])\n",
" tmp = []\n",
" end\n",
" last = val\n",
" end\n",
" Int32.(out)\n",
"end\n",
"\n",
"xss = get_arround(Float64.(xs))\n",
"yss = get_arround(Float64.(ys))\n",
" \n",
"# display(xss)\n",
"# display(yss)\n",
" \n",
"plt.figure(figsize=(cols/my_dpi, rows/my_dpi), dpi=my_dpi)\n",
"plot_img = plt.imread(img_file)\n",
"fig, ax = plt.subplots()\n",
"ax.imshow(plot_img)\n",
"for y in yss\n",
" ax.axhline(y, linewidth=1)\n",
"end\n",
"for x in xss\n",
" ax.axvline(x, linewidth=1)\n",
"end\n",
"savefig(\"output/$(img_file)/better_hough.svg\", dpi=my_dpi * 20)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"sub_imgs = []\n",
"for i in 4:length(xss)-1\n",
" mkpath(\"output/$(img_file)/$(i-1)\")\n",
" # display((i, !(i in [3,4,5,7])))\n",
"# if (!(i in [3,4,5,7,8]))\n",
"# continue\n",
"# end\n",
" for j in 6:length(yss)-1\n",
"# if j < 4\n",
"# continue\n",
"# end\n",
" c = xss[i-1]:xss[i]\n",
" r = yss[j-1]:yss[j]\n",
" sub_img = img[r,c]\n",
" push!(sub_imgs, sub_img)\n",
" display(sub_img)\n",
" # display(\"======================\")\n",
" save(\"output/$(img_file)/$(i-1)/$(j-1).png\", sub_img)\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# sub_img = sub_imgs[28]\n",
"for sub_img in sub_imgs\n",
" display(sub_img)\n",
" sub_img_f = Float64.(Gray.(sub_img))\n",
" gray_point = vec(sum(sub_img_f, dims=1))\n",
" gray_maximum = maximum(gray_point)\n",
" gray_point[gray_point .< 10] .= 0\n",
" gray_point[abs.(gray_point .- gray_maximum) .< 10] .= gray_maximum\n",
"\n",
" # display(gray_point)\n",
" plt.plot(gray_point)\n",
"\n",
" digit_start = 1\n",
" digit_end = length(gray_point)\n",
" slide = 0\n",
" window_size = 0\n",
"\n",
" for (index, value) in enumerate(gray_point)\n",
" if value == 0 || value == gray_maximum\n",
" if window_size > 2\n",
" digit_end = index\n",
" break\n",
" elseif slide == 1\n",
" slide = 0\n",
" end\n",
" continue\n",
" end\n",
" if slide == 0\n",
" digit_start = index - 1\n",
" slide = 1\n",
" window_size = 1\n",
" elseif index - digit_start > 1\n",
" digit_start = index\n",
" window_size = 1\n",
" end\n",
" window_size += 1\n",
" end\n",
"\n",
" if (digit_start < 11)\n",
" digit_start = 11\n",
" end\n",
" display((digit_start, digit_end))\n",
" display(sub_img[:, digit_start-10:digit_end])\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.2.0-DEV",
"language": "julia",
"name": "julia-1.2"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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