OpenJudge 东方14ACM小组 / 20170123 02 岛屿
- 总时间限制:
- 40000ms
- 单个测试点时间限制:
- 4000ms
- 内存限制:
- 128000kB
- 描述
-
从前有一座岛屿,这座岛屿是一个长方形,被划为N*M的方格区域,每个区域都有一个确定的高度。不幸的是海平面开始上涨,在第i年,海平面的高度为t[i]。如果一个区域的高度小于等于海平面高度,则视为被淹没。那些没有被淹没的连通的区域够成一个连通块。现在问第i年,这样的连通块有多少个。
例如:第一年海平面高度为1,有2个连通块。
第二年海平面高度为2,有3个连通块。
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KC7Jzih79eO7QbDwBYHHPJ4KeXSU+ACICSeWC2+mOb0D82jXQStrNb+uSXnoBk7D3Ij1vn4W+7/Yxf34b3j7zIOb01/KZhwxadWdWyXZdOr40KvVrhNDLzanezSMm8U7UTIAKAvKhAN1/741nL329yvwDvHA3em2bAmNInv/QE5DP13qO9ssNnofmCf090rqN3cImSdvGGlYE/gKUmfAcAOHIls8KVA8tK717I1tGubtVOgAja5M3jvc1W7pulsdEvwGuJV60NuetN+uSXnoB8RtZ7xF5IQpcZvtDr+qiV7i3Ni+4rzM+5eeLAnIY2Bj63tElrLwBoPdOllf4PTMql378GAMzq3MKiaifwiKp/GRHjJnz76zLCvEemem4Z39biQf0V5GWnh4eObVrP2oCRpU9+6Qk8jtgJYCzn95RkXIkEEHstsxR2YpLK+33Vpx7BSQh+ffGjD07ZMsLA4a069FvY8UD218M76ddYftTeOQDw+aZPOhv4pyfpCTwg4d0nPXK2f+7JJa79A2MRaO/96IPzIiYaMrb0yS89gQqkTADJ33tuHfpSpVKpVKoanSbFAzjk0caifMA3wsBHGV7Y+dH3T75UbhODfuoCAFi0HrrJz3xZpxbDF26NiL145UrSiRCfPkMC0GDUuuM+PeoZOr78BCS++wTZ2z9+q2Z27BMfbW5r2F1O0ie/9AQgewJI/pBp28dfUfylhG41NCVlqJTI96nqdB7348WhKacPhf0StDYx165L729DT/VqZ19D0InNkhOQ+O4TZG//tqMVZbSs4JA++Y0gAckTgDs4ZDOvo9Z8OEHz4QSTTYBIFumTX3oC8hjZsQZERGQC2HuIiEg09h4iIhKNvYeIiERj7yEiItHYe4iISDT2HiIiEo29h4iIRGPvISIi0SrtugY3btyYM2dOZb3a88rOzt69e3dycrKU6MXFxQA+/fRTc3NzKQkkJCQUFxcPGjRISnQAhYWFu3fvzs3NlRI9MzNTq63qd9p6Kq1WGxAQYGdnJyV6YmJiQkJC9+7dpUQHkJ6enpGRIWv+63Q6ABMmTKhZU9iN7ypITEw0MzOT+Of3xo0bL/CsSus9iqJIrH+dTldWVpaamiolemlpKYDr169LiQ6guLhYURRZq1+utLRU1gQw8cZTTuL2v3nzZl5eXl5enpToAIqLi+vVqyfrk59KpQJQs2ZNWQk0aNBA4rsPQFFe5O4LldZ7mjRpsmTJksp6tecVEhLSvXt3f38518VLTk5+5513jhw5Ymkp8sLnD7z33ntZWVnHjh2TEh1A27ZtBw4c6OPjIyV6fHz83r17pYQ2EhYWFjNnzmzTpo2U6AsWLIiKitqxY4eU6AB69OjRvXv3rVu3Somu1WqrV68eERFRu7aB7/n1BGvXrt2zZ4/EP78nTpx4gWfx9x4iIhKNvYeIiERj7yEiItHYe4iISDT2HiIiEo29h4iIRGPvISIi0dh7iIhINPYeIiISjb2HiIhEY+8hIiLRjKj3aNN+C1gfUyA4alnOpd/Wz/1YrVar1f2mrtgVd7NEbAK6nOTDG/2G91SrO37wRcDPJ1LzdSaWAABZ7z7dI237yy1A3Z2kff/w6u+kUjXsNtJvw+GUPNHVJzuBe8RPAPm9R9HmZ6ZE714xTtO4j1fcLZErr8sM92vpsSWny6R1ScnJSTFBb6d6DnzTySPkfKGgBLJOLXfrEXi+2eD5m48e3DCueczMYb3ad5534EapiSQg8d0nyN7+cgtQuXXkG01rv7hXh6/Yfzlxxwz1yVF/cbRuPCX0mqjJLzsBSJ0AkntP/DqVWbPeU1afLGvW3kF08LRD33qULVvi6da5qbWFSmVh4zR4fqgvEP5V/zVR+YaPr7uy88vg5itOB43r017dtKljl/e8gk+t/wy5m8Z2X3lawAXpZScg9d0n6dtfagHqkrd4rHAMuRoyY4Bzy+YOTm/9zW9n2p6pyFgx8JWFkQJuRCU9AdkTQHLvaTtaUdJP/bRoopumpej7XmUmHv4PNnm9syzy1v2x6urOXgAQEHvZ4DfDKIrdM63LiN5Nq+uNmdm//dkyZwCBX4Ulv8g9MV6qBGS++yR9+0stwMKo7cM0EwY41NAbM2vYz3OTC4Cvp/6aZNDoxpAAZE8A+fvcpKlhVb/832r6d3y6exekF7sb0nNJTVyMpe5d3ghJLNMbVanbDwGApP1JL3IvwJcqATJpUgswJdYbc/vYvrIqrsLkb+XsAQDRoecMfR9G6QlIV2n3jnv5WHedeHxHt3TbTh1tHgxevxAAAENeta/+pOdVstzsgmKg1oOBGlYuQCTCsgXs9jOKBMg0GUMBZtzOLwKsHgxY1nYF9mPbbUG3f5eegDwm3HsAy0YdNI30B3QXogIAwNu9W0ODR2/x1+2+/w3OdhvUoZb+sFKUFwkAbvUNfRNE6QmQiZNYgG0+OB547IesT4a/bqU/rBTm7gcAd7s6ho0vPwHpTLr3VKQUJITM98mAW9DhMZ1rGj6eWX3nz/2dH8niYvQiAHDp69To0edUrQSI9AgtQJWdZto6zSMpJJyYCwCubh2aVfUEpGPvQdbJgDFTg6IzAMB1wd5zw5xqqaQlU3jmoE8ugJkzXB2kZCE9ATI1xlOAhad3jskAsGj+QEfTTEAkEz7W4B6bNzxDDifEx57at+aLgrn92322KiZL0vldJQnbZy0GXP0Pjuki4JuXESZApsdYCrAkbsPEWcBHG897vVXr2YtXwQTEYu+BytzC0tKyprV9q77T1v7HB0e+G+S8+Hi2+ETyY9aO8k3q6bc7cLCjqCMdjCsBMkXGUYB5J5cNmBj97sozm0a0ljL5pScgGntPBZYd+y5pBmC1++bfi4RGLk3dNXfQkq7+B1ePbCPlM4/0BIhkFWDp5W2TND49NiRun9TR6tmLV8EEJDDl3lNWkJ1T/PCXe4d24wEAi2MuGfzs0vt0t49818uzzpoTgYMdLYVFNaYEyAQZSQEqmQdmqz+2Cf1j00gnKTuapScgh8n2nqIzq1q269LptVGhVyucxGZe7e7hFymZdwSlkh+3zsPfdvsZv74N7x/6kXN6a/hNQfHlJ0Cmx1gKMC8q0M3X/njW8veb3J/8d44G700TEdwoEpDFZHtPasJ3AIAjVzIrXLewrPTuZXQd7eqKyEN7ZYfPQvMF/57oXEfv4B4l7eINKzEfgaQnQCbJKApQm7x5vLfZyn2zNDb6k/9a4lVrMXu+pCcgkZH1HoNfyOa+Jq29AKD1TJdW+r9uKJd+/xoAMKtzCwuDJ6HLDF/odX3USveW5kX3Febn3DxxYE5DGwGndkpPoAJx7z49jtDtL78AdRlh3iNTPbeMb2vxYO4X5GWnh4eObVrP2rDBjSKBh4ktQGM5v6ck40okgNhrmaWwE5GUVYd+CzseyP56eCf99zg/au8cAPh80yedDf67R97vqz71CE5C8OuLH31wypYRho4vP4F7hL/7VIGM7S+7AHNPLnHtHxiLQHvvRx+cFzHRoMGNIgE9UgpQ8veeW4e+VKlUKpWqRqdJ8QAOebSxKB/wjTDwQZYWrYdu8jNf1qnF8IVbI2IvXrmSdCLEp8+QADQYte64T496ho0O4MLOj75/8sVqmxj+c4/0BCS++wTZ219uAcZv1cyOfeKjzW0Nvr9PegKQPQEkf8i07eOvKP6SgqvqdB7348WhKacPhf0StDYx165L729DT/VqZ19DyGnVrYampAwVEchoE5D67pP07S+zANuOVpTRBo9izAlA9gQw+R0c5nXUmg8naD6cIDsRIlPEAjRVRnasARERmQD2HiIiEo29h4iIRGPvISIi0dh7iIhINPYeIiISjb2HiIhEY+8hIiLR2HuIiEg0laJUwsVLly9fPn36dDMzaZ2srKwMgLm5uZToiqLodLryCyFJSUCn0wGQuP3LE5C1/XU6nbm5uVYr7mZ/xsbGxsbKysrCwvCXXn+cnJycrKwsWe8+ZJd/eQJmZmayyl/66ut0uqCgIE9Pz+d6VuVcUyc9Pd3c3Hzp0qWV8movYNasWW+++eaECXIuzJGVlTV58uRp06bJKv4tW7Zcv359+vTpUqID8Pf379y586hRo6REj4mJCQ4OlhLaSFSvXj0oKMjR0VFK9GPHji1btuzbb7+VEh3AlClT7OzsJk2aJCV6cXGxt7e3j49PgwYNpCSwdu3ahIQEiX9+Z8yYcePGjed9VqVdz61mzZoDBw6srFd7XgsWLFCr1cOGDZMS/fr165MnTx4/frylpZwbTp89e/bWrVuyag9AUFBQixYtZE2AWrVqbdy4UUpoI2FmZta2bds2bdpIiZ6RkWFrayur+gDMnj1b4vQrKCjw9vZ2dXV1cHCQksDhw4cvXrwo8c/v3LlzX+BZ/L2HiIhEY+8hIiLR2HuIiEg09h4iIhKNvYeIiERj7yEiItHYe4iISDT2HiIiEo29h4iIRGPvISIi0Yyg9+hykg9v9BveU63u+MEXAT+fSM3XSchCm/ZbwPqYAvGBy3Iu/bZ+7sdqtVqt7jd1xa64myXik3hAe2Hz4Kn704TFM7LVNzm6O0n7/uHV30mlathtpN+Gwyl5MqoPsgrQyKaf6OqDzC0guffosk4td+sReL7Z4Pmbjx7cMK55zMxhvdp3nnfgRqmYBBRtfmZK9O4V4zSN+3jF3RI89XWZ4X4tPbbkdJm0Lik5OSkm6O1Uz4FvOnmEnC8Um8g92ou/zPOJvl0s5orQRrf6Jka5deQbTWu/uFeHr9h/OXHHDPXJUX9xtG48JfSaoOqD1AI0uukntvogewtI7T26Kzu/DG6+4nTQuD7t1U2bOnZ5zyv41PrPkLtpbPeVp/MMHj9+ncqsWe8pq0+WNWsv4yqAaYe+9ShbtsTTrXNTawuVysLGafD8UF8g/Kv+a6LyxedTmhL6zawTwsIZ2+qbGF3yFo8VjiFXQ2YMcG7Z3MHprb/57UzbMxUZKwa+sjAyV0QKUgvQ2Kaf4OqD9C0gs/cUxe6Z1mVE76bV9cbM7N/+bJkzgMCvwpIr4c5CT9V2tKKkn/pp0UQ3TUs7A8d6jMzEw//BJq93lkXeuj9WXd3ZCwACYi+LvhtN2eWdS9O7ibsNg5GtvqkpjNo+TDNhgEMNvTGzhv08N7kA+Hrqr0kCcpBZgEY2/URXH+RvAZm9JzVxMZa6d3kjJLFMb1Slbj8EAJL2Jz33HSFeLjWs6pf/W03/pk937+VXKff0ew5lV3cvy5owope1sIhGtfqmJyXWG3P72L6yKq5C9bVy9gCA6NBzqZISE8Sopp/46oP8LVBp9+95cbnZBcVArQcDNaxcgEiEZVfx/S7WXSce39Et3bZTR5sHg9cvBADAkFftqz/peQZQlrrXP23M9241Lp0WFtOIVt+EZdzOLwKsHgxY1nYF9mPbbSF73eQxoukno/ogfwvI7D0t/rrd97/B2W6DOtTSH1aK8iIBwK1+bTl5iWPZqIOmkf6A7kJUAAB4u3drKC4N3R/7lqR+vvg9S4jd02Akq2+a2nxwPPDYD1mfDH/dSn9YKczdDwDudnXk5CWOkUw/WdUH2VtAZu8xq+/8ub/zw6PKxehFAODS16nRo8+pypSChJD5PhlwCzo8pnNNYWF1Nw4EpHjMf1dcxMeTtPqmSmWnmbZO8/CoknBiLgC4unVoJj4nmUy8+iB+CxjBPreKCs8c9MkFMHOGq4NKdjJiZJ0MGDM1KDoDAFwX7D03zKmWuDXXpR0KSHL/+1/lzX2pq08VFJ7eOSYDwKL5Ax1l5yKIiVcf5G0BIzi3VF9JwvZZiwFX/4NjuhjBJwExbN7wDDmcEB97at+aLwrm9m/32aqYLEEn+CkZ4cvjP/FwrvXsRQ1G4upTBSVxGybOAj7aeN7rLZkzQigTrz7I2wJG1XvyY9aO8k3q6bc7cLCjCf3UrDK3sLS0rGlt36rvtLX/8cGR7wY5Lz6ebfjAys3DP5wZ5FFxh79w0lafKsg7uWzAxOh3V57ZNKI1q8/wgY2j+iBvCxhP7ylN3TV30JKu/gdXj2xjMh+6HmHZsbYb6osAAAt/SURBVO+SZgBWu2/+vciwoZRbR1ZGu43pKvS4zmcQuPqkr/Tytkkanx4bErdP6ij9j6E0Jl59EFuARtJ7dLePfNfLs86aE4GDHS1lJyNMWUF2TvHD324d2o0HACyOuWTQ416yT6ze7NTL7mayvpTUmwAupF5KTk5OTv4j26AZSF19ekDJPDBb/bFN6B+bRjqZzJ5uU68+SC9AozjWID9unYe/7fYzE5zr3P+RK+f01uhXP/6Lvcy8DKrozKo2H34H9Aw6vMGt+YMf98yr3X1PUjLvAIY727sgPyv950Uzf64wmHctGkBG8KKZB2oDLjPXemnqGSa87NWne/KiAt187Y9neWts7r8Nd44GH285qn/VPdBU9vSTXH2QvwWMofdor+zwWWi+4N9j2+t/11fSLt6waiMtKQFSE74DABy5klmI5g/2MpaV3r2OrKNdXUPGb9L3++19Hx5M3v7OO18mu/iu/fH9VwwZXP7qEwBAm7x5vLfZyn2znPV3/ijXEq9ad5KWlACyp5/k6oP8LSB9n5suM3yh1/VRK91bmhfdV5ifc/PEgTkNbUSfWyr0OhpNWnsBQOuZLq30f95SLv3+NQBgVucWFiLzEczEV98o6DLCvEemem4Z39biQfEV5GWnh4eObVpPwi8R4gqQ00/+FpD6vSfv91WfegQnIfj1xY8+OGXLCGGJlGRciQQQey2zFHaCNolVh34LOx7I/np4J/0iz4/aOwcAPt/0SWfxP3yVFuYlA4i8nVcGmD9z8T/DGFfftOSeXOLaPzAWgfbejz44L2KiyFyEF6AxTj+B1Qdj2AIyv/dc2PnR90++Wm4Tw3/wunXoS5VKpVKpanSaFA/gkEcbi/IB3wjDH2Jo0XroJj/zZZ1aDF+4NSL24pUrSSdCfPoMCUCDUeuO+/Qw3J7ex8g9tUytVqtbvf93AMDXA1qq1Wr1ckPex8KYVt8UxW/VzI594qPNbUXs8ZRYgMY0/SRUH4xgC8j83tNqaErKUInxYdvHX1H85cVX1ek87seLQ1NOHwr7JWhtYq5dl97fhp7q1c6+hugT+63f9EpJ8RIc1HhW3xS1Ha0ooyXnILUAjWf6Sak+SN8C8o81MHXmddSaDydoPpwgOxE5THz1SS5OP3lbwEjO7yEiIhPC3kNERKKx9xARkWjsPUREJBp7DxERicbeQ0REorH3EBGRaOw9REQkGnsPERGJVmnXNSgoKAgJCamsV3ted+7cOX/+/KpVq6REz8rKArB582YLCzkXvz1//nxJSYnE7V9SUpKYmCgrgdjYWJ1OxB3mjZZOp4uJiSmfh+LFx8fn5uYeO3ZMSnQAxcXF58+flzX9iouLAfz66692dnLuN3Xu3DmtViux/AsLC1/gWSpFqYQLl69cuXLq1Knm5ga/+OqTaLVaALL+9Ot0urKysvKLIMpKAICZmbRvsXITUO6REt0Y2NjY1K9fv0aNGlKi5+XlZWZmvvrqq1KiA0hJSdFqtXL//sj64wOgrKxMp9PJTWD58uWenp7P9azK+d7TrFkzFxeXiIiISnm1F9CkSZPu3bv7+8u5LmF6erpGo4mNja1dW/Qth8pNmjQpIiLi7NmzUqIDeO2119zc3JYsWSIl+oEDB8aPHy8ltJGwtLTctWtXmzZy7rYYFhbm6+t76tQpKdEBuLu7u7i4TJ48WUp0rVZbvXr127dvyyr/tWvX7tmzZ/v27VKiA+jZs2fz5s2f91n8vYeIiERj7yEiItHYe4iISDT2HiIiEo29h4iIRGPvISIi0dh7iIhINPYeIiISjb2HiIhEY+8hIiLR2HuIiEg0I+o92rTfAtbHFAiOqstJPrzRb3hPtbrjB18E/HwiNV/wBZFNPAHpq08AZFUfAN2dpH3/8OrvpFI17DbSb8PhlDyh009qdNNOQH7vUbT5mSnRu1eM0zTu4xV3S+Ts12WdWu7WI/B8s8HzNx89uGFc85iZw3q17zzvwI1SJlDloxOkVh8A5daRbzSt/eJeHb5i/+XEHTPUJ0f9xdG68ZTQayImgNzoTKDS7t/zYuLXqdrN6Tr0cw/3t9o7ANEiY+uu7PwyuPmK09PU5Veeb9rUK/hN52/e9Ngwtrvt1rhpXQ19UVoTT0D66ps8mdUHQJe8xWOFY8jVea3KJ0BzB7+dad2mN3o3aOAr9kdz5rlYV93oTED69562oxUl/dRPiya6aVoKvu9SUeyeaV1G9G5aXW/MzP7tz5Y5Awj8KizZ0HeDMfEEpK8+Saw+AIVR24dpJgxw0L/nkFnDfp6bXAB8PfXXpCocnQlAeu+RKDVxMZa6d3kjJLFMb1Slbj8EAJL2J91gAlU3OkmXEuuNuX1sX1kVV2ECtHL2AIDo0HOpVTc6E4Ap9567crMLiisM1LByAYCw7HwmUOWjk3QZt/OLKgxY1nYFgG23c6t8dNNOQPLvPRK1+Ot23/8GZ7sN6lBLf1gpyosEALf6hv65wcQTkL76JFebD44HHvsh65Phr1vpDyuFufsBwN2uTtWNzgRgyr3HrL7z5/7OD48qF6MXAYBLX6dGTKDqRifpVHaaaes0D48qCSfmAoCrW4dmVTc6EwD3uT2k8MxBn1wAM2e4OqiYgIlFJ+kKT+8ckwFg0fyBjqYW3dQSYO/RU5KwfdZiwNX/4JguNZmAiUUn6UriNkycBXy08bzXW7WevXiVim56CbD33Jcfs3aUb1JPv92Bgx2rP3txJlClopN0eSeXDZgY/e7KM5tGtBY/AeRGN8UETPf3nopKU3fNHbSkq/9B/8GOlkzAxKKTdKWXt03S+PTYkPivkU7iv/PKjW6iCbD3ANDdPvJdL886a07M69tQygYx8QSkrz7JpWQemK3+2Cb0j2XvNxE/AeRGN90EuM8N+XHrPPxtt5/x0/vDl3N6a/hNJmAC0Um6vKhAN1/741nL9f7w3TkavDfNBKKbcgKm3nu0V3b4LDRf8O+JznX0jqtS0i7esBLzzdPEE5C++iSXNnnzeG+zlftmaWz0J8C1xKvWVk9+VtWIbuIJGFnvEXsNL11m+EKv66NWurc0L7qvMD/n5okDcxraCDi30cQTkL76VIHwK+jpMsK8R6Z6bhnf1uLB+1+Ql50eHjq2aT2DX0tTanQmYCy710syrkQCiL2WWQo7MUnl/b7qU4/gJAS/vvjRB6dsGcEEqnJ00iOh+gDknlzi2j8wFoH23o8+OC9iYlWOzgSkf++5dehLlUqlUqlqdJoUD+CQRxuL8gHfiGzDhr6w86Pvn3yp1iaG/9hh4glIX32SWH0A4rdqZsc+8dHmtnWrcHQmAOnfe2z7+CuKv5TQrYampAyVEpkJyI9OkFp9KL+Dw2hZwSVHZwKQ/r2HiIhMEHsPERGJxt5DRESisfcQEZFo7D1ERCQaew8REYnG3kNERKKx9xARkWjsPUREJBp7DxERiVY519RRqVRHjhyZPHlypbzaC8jJyQkNDY2Li5MSvaSkBMCHH35obm4uJYGUlBStVtuvXz8p0QEUFxfv3LkzNvbJ14cypIyMDJ1OJyW0kcjKypo+fXqLFi2kRL927doff/whsfxjYmJu3LgRHx8vJbpOp6tWrZqXl5eFhYWUBBITE1NTUyVu/6NHj3p7P+aCpE+nUpRKuHJ6bm7uDz/8ILH+r169mpycLCs6gMzMTDs7O1nRi4uLi4uL69SpIyuBrKwsa2vratWkXR6wU6dOS5culRVdum3btp0/f152FmSiLCwsJkyYULfu811+tHJ6DxER0f+Ov/cQEZFo7D1ERCQaew8REYnG3kNERKKx9xARkWjsPUREJBp7DxERicbeQ0REorH3EBGRaOw9REQkGnsPERGJxt5DRESisfcQEZFo7D1ERCQaew8REYnG3kNERKKx9xARkWjsPUREJBp7DxERicbeQ0REorH3EBGRaP8f8nScUNW2XfIAAAAASUVORK5CYII=" alt="" width="553" height="235" />
- 输入
- 第一行包含两个数N,M。
接下来是一个N*M的矩阵,第i行第j列表示这个格子的高度h[i][j]
接下来是一个数T,表示有T天,
最后一行有T个数,第i个数表示第i天的水位高度。(保证是递增的) - 输出
- 输出包含一行T个数,第i个数表示第i天的连通块个数。
- 样例输入
-
4 5
1 2 3 3 1
1 3 2 2 1
2 1 3 4 3
1 2 2 2 2
5
1 2 3 4 5 - 样例输出
-
2 3 1 0 0
- 提示
- 对于50%的数据: 1 <= n*m <= 1000, 1<= T <=3000
对于100%的数据:1<= n <= 3000 , 1<= m <= 3000 , 1<=T<=100000
1<= h[i][j] <=10^9 -
#include<iostream>
#include<cstring>
#include<cstdio>
#include<algorithm>
using namespace std;
int t[],ans[],f[],n,m,tot,si;
struct node{
int x,y,high;
}a[];
bool vis[];
int cmp(node i,node j){return i.high>j.high;}
int dx[]={,,,-};
int dy[]={-,,,};
int find(int x){
if(x==f[x]) return x;
else return f[x]=find(f[x]);
}
void solve(){
int sum=,pos=;
sort(a+,a+tot+,cmp);
for(int p=si;p>=;p--){
for(int i=pos;i<=tot;i++){
int h=a[i].high,x=a[i].x,y=a[i].y;
if(h<=t[p]){ pos=i;break; }
if(h>t[p]&&!vis[(x-)*m+y]) {
sum++;vis[(x-)*m+y]=true;
for(int j=;j<;j++){
int nx=x+dx[j],ny=y+dy[j];
int k1=find((nx-)*m+ny),
k2=find((x-)*m+y);
if(nx>&&nx<=n&&ny>&&ny<=m
&&vis[k1]&&k1!=k2){
sum--;f[k1]=k2;
}
}
}
}
ans[p]=sum;
}
for(int i=;i<=si;i++) printf("%d ",ans[i]);
}
int main()
{
scanf("%d%d",&n,&m);
for(int i=;i<=n;i++)
for(int j=;j<=m;j++){
scanf("%d",&a[++tot].high);
a[tot].x=i;a[tot].y=j;
}
for(int i=;i<=tot;i++) f[i]=i;
scanf("%d",&si);
for(int i=;i<=si;i++) scanf("%d",&t[i]);
solve();
return ;
}神一样的倒序并查集。。。。
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