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惯例先放图:
首先准备一张图片,然后通过 地址 转成string
大概最大的难度就是在于如何保证编译器赋值这么长的string进去而不卡死吧,233
我用notepade++把string分成了长度为4000一段段进去(太长编译器也会报错)
然后是简单粗暴的代码
- #include<opencv2/opencv.hpp>
- #include<string>
-
- using namespace cv;
- static const std::string base64_chars =
- "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
- "abcdefghijklmnopqrstuvwxyz"
- "0123456789+/";
- static inline bool is_base64(unsigned char c) {
-
- return (isalnum(c) || (c == '+') || (c == '/'));
- }
- std::string base64_decode(std::string const& encoded_string) {
-
- int in_len = encoded_string.size();
- int i = 0;
- int j = 0;
- int in_ = 0;
- unsigned char char_array_4[4], char_array_3[3];
- std::string ret;
-
- while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) {
-
- char_array_4[i++] = encoded_string[in_]; in_++;
-
- if (i == 4) {
-
- for (i = 0; i < 4; i++) {
-
- char_array_4[i] = base64_chars.find(char_array_4[i]);
- }
-
- char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4);
- char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
- char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
-
- for (i = 0; (i < 3); i++) {
-
- ret += char_array_3[i];
- }
-
- i = 0;
- }
- }
-
- if (i) {
-
- for (j = i; j < 4; j++) {
-
- char_array_4[j] = 0;
- }
-
- for (j = 0; j < 4; j++) {
-
- char_array_4[j] = base64_chars.find(char_array_4[j]);
- }
-
- char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4);
- char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
- char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
-
- for (j = 0; (j < i - 1); j++) {
-
- ret += char_array_3[j];
- }
- }
-
- return ret;
- }
- Mat str2mat(const std::string& s) {
-
- // Decode data
- std::string decoded_string = base64_decode(s);
- std::vector<uchar> data(decoded_string.begin(), decoded_string.end());
-
- cv::Mat img = imdecode(data, IMREAD_UNCHANGED);
- return img;
- }
- int main() {
-
- std::stringstream os;
- std::string temp=
- "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"
- "TksYt7hY334xuUd8ipLzJC3w28hHzGKzzuzp4f4o2ePLDNKINgYewrFEjRAmF8544rWNpsktBKD7Bc0vizRY5t0Jwc4OK385lzxkjmm4Mivv8l8du2f2rZ45JiCI5AV78VS2COhdLIuQKRuzmFgDtHrWhjkcZWGTzB6YwK8Pn4w9vIQfYZq0n7LpLoHfEHU7mGxsLa2ZAzgP8R54oTsFkiZ7mV/5TRsvlofxeufrRn1naG5srMtazu+xkDRIW2du4oZ0fSrq1kkjFpOSyEBZY2UZ+RxWlM5GVfsxvZOup2Ns3KSksxXGSvPZsVL28UkVtjO7LYJ+VI2lnfWgme20+QuTypQj9D60+sWuvuZjewvGlbJP8rABqxaR5NC33OeTzgqBfwGoC2uwqSJMuVPYnkj6UQRQXcSMj6bc+W3fA3VEajo12JHaPTrzy3GVYJnB9iPShRTTEZreNLU3FqRKsZ+JSOTUXPGbli3mLt3fGignI9uKe2+lawqsEsrtY8/EuzB/Kl7LR9RiuDKdMuJlxwJBtBPz75ogeLRY2qXgitLdWUMCoPbtwKhhcorbiCoY5zT3U1u5ILVorGS4kWMBgjAAHA4Oah5dI1W7Q79OVIs5wJwWNWxrTFZdSaa6Eao3lx/if0b6Ur5TXkMhYvEvsfX50hJol7dypH91kjRFwokwP3BpxNbalFbiKDT34AB/mLtPH9qEqnQKdbmQdNagqMRmLaPXJyKQ0OxFhpVhOwchIU3Njt8IqS6p0jVv+b9xHBos9xK5UCO1kUnvyeSOBTu00XWI42h+6Otrt5WVlLZ9gM0X9iGm5Azbs2rWlxC+6EibCzAcMAfhNe6Qjv1HYiSRZI0mQbVz8Lbhg8elEI6c1MafNGtqI93G1cBv749aR0Xpy907UYWOnSgeYpLM6/Dhh8Xf2zxUC4v6Fuvb0Pc+VIwyjELj2OKg9PQFZbm6clI/hVQMAip/rHRdR1TU0e3sD5cYwJTKo3flioi66U1hxxZuqYxsMyksflzVbsKUXfQ0e8V4WigBjQndgjkipzQ7WOfT2Z3ZWU8gjuaj7fpPVlCo+nfiIyXmHA/KpttP1FVWJLNEUnGDIMEcVbkoq2SOOTekI4eXzQrfCV7Coz7kbEfeHDhCCGccYzU/Fp11AhEluqg5GUk4A/SkP4Jcm3MW8MmSVXOT+dIflYounIcvEyyVpAfaxtqOlxi5Bt2SUsso+XZj8zS/S6LJ1LZyGXz0EowoBOxsftRFL0zcyWRiBGPZfhP617oHTk2l3kTm3jQbwWO/8HfsMUX+Tib/kT/Eyr0SHVZ82YqJNjBQeR3obs4XmhBcAYY/WiTX7aSa5MgRmBAHwihm/1CTSY1kW2MxVsMhBBx701ZccumKnhyJ7Q8NtLbxIwOyJmx8VOBFtl2O6se25R39sVA/84HubaWR4872DLEpyUPYU9tXZ4IZCrCUckGmqQmmjB0bJPdE3RLQBgc+aXZ/mc/hA9qf9Ra5a9M6d5kyFgSI0jjjyzuewAHrUJrvUN7ZOPLYpgAkFDgnPbdnv8uaaaH1Pf9VxtGlkdM8qUnzbj45CfdV9O/BJqJ7FaWkTsKLrFiBc2KWEshGINoeUrjnIIxU74WaO+j22qhwhM11vDKuM4UDHHHBoZl0byrgSveSx5UvKMgPKvqS/fHyFHfRsskmmvK0TRK0haOPvtTaAP9/51L2NxxuWwlLe/wC1JsTjuaS88+isawysccGjNPEWf0pGRsY7mvHmJJwpxSLSt32tioCuhPTuZLk//wBSnxb51FabI/8A1o7SQZj/AGFOzIxOdpoV0W1Y4Y4GfWkXG7Oe1eF2x2/StS7Y/CaIKtG4YLxWUmpZmxt49zWVVoCioI5AFx+9OI5TgAVGRy4Yge9OElwSfSkHRoeXNw0UUj5OVG7Aocl1SO5Dk5DNyAamrhw0L57FD/ag1E/lJhviDcjvVGbIics7UxooRgG3bvyxS+nWJstZW7CFlIOQT3JqPuLtY7cGN+cY+db2erebEA8hZT3XOTV2JLN6RYbpzgfzF3cfWtdS6yl0eRllCbRJsB78morofUM3bKvCMp2578elI9W6bDNfXcMkkkZmAkD54Q+9QZf6k7L1vKZVWMLjHtnmnlv1NduWB2jjIyKCNOhJaOMP5/lnG/PLD3oiaZFRZD/LYcZqgLY/uurL0AqNoAx2HrSS9YX9vchZWJRuQwUD9qZ3hinAkUgheWKmmck7ukUpXeG/DH2JPtVUgXOS6ZPjra5kn/lk4XgjZjNPo+pbp2TDICRyDjNAl51ZZWdwVMLwDaufMXkHIDKR7jIP0pSDUbS81WSKOXYUl8oOx4Y7QTg/nj8qiK+RhpcdUXEhj8spwSGBHPyptddV6jC/xIoUAEHb2zQxqlkZL0GC78hIxz6kmiDpfpu46q0jWrxNRjjGlRbpFKFvMwrNxyMdqJK3SCc2vZkXXl3DKqSrGysQQyjHHzpxL1w7sixRjOcMfSq80vqGG+htpWxsdd29uNvyox6K6YbrltYkstQitRYIssgaMvuB39uRj8BqtvRXN+ycj6lu3wECdskkcivZ+qrgJGY1TuQxPegDR+rre801bhJQJCCMMfSiDqGxaw8M7DrBtQRrOeZYhbBMEEyMn4s/6tVTIp0TydSTEfG6hsdiK8vOqZbSJZSBtBxgL3oLsOoLG9VZPMDS7tu0nt71JT3yXSRholaPHODyOeCBUsik2FfVmsvpmjfe4txA+M7BliMZ7etQ8fWs88hsw6tK8BnhlC/CyjA5/M0+6rY2Oi2ywKssvCqrHAYY5H1xVcymLp+5tQ04FqEdIy5+KNSNwU+2CMfmKqypylFhXpvXVxqtnaFrhIrm5h81UiOOw+Lb+f8Aek5Oo9R+9NF97aNRzuJB3HPag7orSZryzkSKUQzxQi3Erj/QjGS2fcntRFY6bbkQCGb7xb27eTJPvyxZeCT+dQDk2iO6w8S9U0BNPWJ5B97vUtnl2ghMn0onbW55YYHh1NsKSW5/F9c0I9Zae0WvaBbpIJ4ZLwylGQEEKASfy71NxSW8UrmYCNZQQmcDHvUuioybbJCDV9QXzpEu3SSQ5Uldw+uKdR9RXixwBXDu+dzuODj5VA2bNHflI3Z8jgDkgUQDS/PaExozDPIIxiglkjBXJmmEJzdRVi/Us8ttJI8Lku0ak7D8S844qJnivJ44Gae4d42yCzc/nRFqELSTiQqFKrjt3puZ4kPwnPuD71x/I/Ixh/A7WD8bPJuQ0trN/jMksrF/8bkgU8ZWULiQkgd80lJdblIGMgcUk04ZTg4PtXns/mzyvb0d7F4UMS0iSgnyu1iDgdzWwPsahFuHX4T+tO4LneME4rG8rZpWNfRJGUsvypSN1LZqOSTB75FJjUgkpWg+Vr2M+NVpE4HUn4qb3Fha3bZaNSTUS2plpAMfpU9pUH3nlh+opkfInHpiMmCFXJCFl0ZabjIIwrE54FSEnSNvKOwB9z3qZRFt4z3xSMl8o7GtkPyOWPbOS/FxzeogD1J4eyMhniYSyKpGW9vYe3OD+VQlhpUdhcTStGTIWIU55Ht/nVtrdrIhVsEH3oM6u0YwD73bAGNW3NHmvQ+F+S+X9Mj2cfyvA4fvBFctqcj3U9m7G3eCcl2HC7Wz9Rt+XyzVndFyf+hwRIsoMr8g5HftmgF2jaxngUMvn99qghSe/J/PvVgdNILfRbWNVEaheFAwBzXfRx4JxkTbueP/AK1q0ppLzPc1q7huxzTRzF2mOO3GO9IyTfDWhcAetJvICD7Co2AjXS/ht5Gz+KRm/enfmio7TX3WaknGWJ/c08Z1xxVIP2b+Yc1qZsDHrSe81qW7mrssWWbJI9e9ZSCtnOPWsoW2QpWMA85JOadoM4NRscmTwfb0p6khXFA0bbHUm3bgjIINCdwqWyPtbByRzRSrbsZ5FB06rPNOoZlZWOQR2GaF6M+U3SNPIZi2QFyCT2q8fFfTdP077O/T+p2mn2kN9Munh7iOFQ7blXd8QGeao42cdvpcjGQsHUg/CavvxZtZbz7NPR9pbRPPcTHS44ooxlnYhQABTYdGNvaK46Klm/i1sMfCOD+feibqqMNcoWU+WUxvA9aKOjPAPWLeGG41PVINPlYAi3jTzWU+xOQM/StPELwy6i0S1TULWSPWLO3BaWKFCswX1O3ndx7HPyNBwkP5xqrAvTbe0gVdhw+fUVIva/eJPKQggHnFRGnzxXmLpHDQsO49KI+nen9T6gv5YNGiVmC/FPOSI4z6Fj/kKD/QH9sFtdv4+mhK00fnEI77U7sFHI+vNQ99qpv7SdLdod9uV8xJyy4BGeGA4NXRdfZ6uNSQPedRQ2904JKRW25dxXBI3Nn/AOlAHWXg7rvQLDVJxbazpAn86Sa3BjaFiNu91P8ASB7E8nnFFwl2Jcvoq3qy5vW6fk1C0hbULmJVXzA2S3IABx7Z74zzV6dT+J/R+reEUemWNoItcMMSR6Y1uVktZgVyzHGBjk59fzqF1BobrRIXTO3zYiCvr8QOaW6o8PX0Xp"
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- "JAwTWVlAmK9E0szMQ6/AVxjFe37BYzK6+Y+CcsaysoJFEDMUeNpPLAJGcCvLVPNOc4IxjFZWUhjF2TVparOeTjHt60fdI2i+bEcn8QXFZWUhmtdlvxjYsYB/pFStixPHyrKykvsP7HEyhtxPtWVlZVMuPR//Z"
- ;
-
- cv::Mat image = str2mat(temp);
- imshow("test", image);
- waitKey();
-
- }
参考: github地址
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