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\n", 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NXxLnHW/vRktdFfVW8vkUpPhL4Ze+FwovI4tyn7Ms37sgYyMkFsHv82eeMVvy+EdFk/sgC08tNJk8y1SNiAD159fmCtk8kjk8nOhp2o2+qWIurckRsSpVsblI7HBOPX6EVbrvpwoyjzQSsz0nm2KxEYz9q2raO/dW/J+py3iP4f6L4mvlvbrz4LkLteS2ZVMg7bsg5I6Z6447DGhovhbR9B06WxsrNTFOoW4MvztNxj5s9R146cnAGa2a43xd4gvtJvY4rWURp5QkbCBiSWI759Kmq6dH3+W7bS03behy47Oa2HwqjVnJwVkkvw6r/gFX/hUfhv7Z53mX/l+Zv8jzl2Yznbnbux26598810d54W0e98OjQns1SxRcRLHw0R7Mp/vck5Oc5Oc5OeD/AOE31f8A5/G/79R/4Uf8Jxq//P43/fqP/CkqVWN7YaWvkv8AM8+rxjVquLqe0fLqttH3+Lc6HSvhf4f0q+gvEa8nmgmWaMzSjAK5xwoHGSD9VHbIPQwaBp9v4iutdjjYX1zCsMjFztwMdB6nao/4COmTnz7/AITfVv8An9b/AL8p/hSf8Jxq3/P63/fpP8KcadaOiw0/uX+ZNbi6dZuVT2jurdNt7fEehT6Bp9x4itddkjY31tC0MbBztwc9R6jcw/4EeuBi9dW0N7ZzWlwm+CeNo5FyRuVhgjI56GvL/wDhONW/5/T/AN+o/wDCnR+NNYkkCreNn3iT/Cic6tKEpyoTSWr0Xz6nNPiWDSlKM/dWm2i3/mO98O+HbHwxpf2Cw80xmQyO8rbmZjgZOAB0AHAHT1zVTxF4M0nxPeWdzqCy77bIxEQvmrkHa5xnHB6EY3H1rZsJ2utOtrhwA8sSOwXpkgHirFaqMJwStofQ08dXdT6zCb5nrfrqFFFFaGAUUUUAFFFFIAooopgfN3iiymu/H+q/Z18yX7TOix4J35yD3HYniqPhuGT/AITLSJ5pNz/2hap8sYXpKnYfTFbGv3c1l8QNSuYVV5UvJNi4zyTgcd+tQ6cblvGekNqEbfa5NRtXZtqqCDIpzgKASeOfr3oA+jaKKKAOH+I//Hra/wC5N/Ja8pr1b4j/APHra/7k38lrymvCn/vVX5f+ko9rhH+Ni/8AFH/0hBRRRVH2oUUUUDJbb/j6h/31/nXt/hT/AJFqz/4H/wChtXiFt/x9Q/76/wA69v8ACn/Is2n/AAP/ANDanhf98/7df5o+A4k/5G1L/r3L/wBKibVc54i8QS6bIllaw7rmWPcshxhcnHA79D146da6OvPtanNz4quNzZWBQicdOBn9Sa1zfEzoYe8HZt2PCxfPL2dGnLlc5KN10Tu3bzsnbzKHkSXNw9zeuZZn+Y59f8/hVqiivim23d7n1eBy7D4GnyUI2vu+rfdvq/6QUUUtI7Q0nUToWp7m5s7jAlAXO084x9M/kTXoilXTcvevN5olmiKN36H0PrW94S1bdGdKuXVZof8AVEk5decjn0/l24r6HJMdyy+rzej29e39fqfF4jD/ANm4r2S/hVG3Hylu4+j3j80dXXnXxC/5CUf/AF7J/wChtXotedfEL/kJR/8AXsn/AKG1fRV/4lH/AK+Q/wDSkednv+7L/FH8ziKKKK+3PnAooooAKmtv+Phfx/kahqa2/wCPhfx/ka4M1/3Cv/gl/wCkszrfw5ejPadH/wCQJYf9e0f/AKCKu1S0f/kCWH/XtH/6CKu181R/hR9EfdYf+DD0X5BRRRWpsFFFFABRRRQAUUUUAfPOuXsmn+P9Xu441Z47qVl3dFy2M8H3/X8ag057mbxho9xfRyLcXGo2sq8YXaZR0Hpn+Rqzrly1p481eRbfz3W8kZV57MCOx7gH8B9DXs7651DxzpFxdrtlbUbX93ggL+9XoD+f1JoA+jKKKKAOH+I3/Hraf7k38lrymvVviP8A8etr/uTfyWvKa8Kf+9Vfl/6Sj2+Ef42L/wAUf/SEFFFFUfaBRRRQA+FxHPG5zhWBOPrXtng+dZvDsSKCDC7I2e5zu4/BhXiFez+B/wDkCzf9fJ/9BWig7YuFuqa/I+E4qio5jhpreUZp+icGvzOnrzMky6xqEj8sJWGfbJ/wr0yvLrV2ea5M4Iui5aTcMHPfjtzmufiF+5TXmzw4tPMcNCT6yfldRaXz10+Zbooor5c+0ClpKKACrXh6IXHipXDbfs8TORj72eMf+P8A6VVrS8HwiXWL65VsCNFj2kdcnrn/AID+td2Ww58XTXn+Wv6Hz3EMrwoUl1mvuim/waR2ledfEL/kJR/9eyf+htXotedfEL/kJR/9eyf+htX2tf8AiUf+vkP/AEpHzee/7sv8UfzOIooor7c+cCiiigAqa2/4+F/H+RqGprb/AI+F/H+RrgzX/cK/+CX/AKSzOt/Dl6M9p0f/AJAlh/17R/8AoIq7VLR/+QJYf9e0f/oIq7XzVH+FH0R91h/4MPRfkFFFFamwUUUUAFFFFIAooopgfOniaf7P421ptu7ddSK3Y43A8Ht0qLSZPN8UaAzRtHm/tWjXHDKZh82e/I7DHBq1r3k/8J7qvnWkt0gupf3MZILc8cjPHfp2qvpktxceM9InvPMW4Oo2v7to9oVfMUDAzwBgADFAH0dRRRQBw3xIIFnaknAEc2SfoteOtqGS3lQs6r1bpXsXxGgkudPt1QfeSVNx6AsFxk/gfyrzWDQ5oYVQNECBzgnk/lXzeIrQp4uopO235I7uHc5y3Lq2KWNqKLlKNk9NORalGKVJow6HIP6U+rB0K4iRxbtCGc5+ZjgfQYqFNB1JHV/tULEn5lLHH4cVP12ivtH03+tGRyu1ior1v/kNoqSbQtRkmbZcwrGcdzkfpSnQtQEaxpcxEfxOxO78OP60/rtH+YP9Z8ksv9qh+P8AkRV7P4H/AOQJN/18n/0Fa8eg0O+hch54ZIz3JOR+ldtN4luPDfgK4ubEI11JffZ1dsHyiYw27Hc4HAPqDyODeHxNOWJjJPZSv+B81m2LwucZphKOX1FUaU722V+S1+x6lXnHjjU9LtvEVtbxXGNTdcSogG1Rj5dx7MemOcjHTjPD6R461y01e1u01C/1O4nkZbiwmB8ttzcLHhjye2FXB4AIyK7rwXFHqXxB8WajeRrLd2tyIYJWHMaZdMAdM7UUZ64zzyc92IcMbT9ja1393W52ZlwwqdCo8VK6UbxcdGp3UU9ddG7369Opl2etR3GoSWMyCC4xviG/cJU9R054OR/OtSr2r+BmudVW7sWt4kRt8YYshjbuBgH5T6dOcY4rkI7LUfE9nJf6Zd3v2eNzGPszbAr7QSMcM3UHv14r5ivgKtOfK4v81p1RxYPO/wB0li6U4uOkpRi5Rb6Nct2rpXs0utr2OgoJABJOAOpNc1EdVuPDl39mvGumMhAvVXCx5IJXIBweTj03DGOKsadpWqzadqWqXd5JOtvbvLJk7Y8om7YqjjJx1x79+cIYeUnyx1fRfr6G1fPMGouGGnz1W7RhaUW3pr7yVo92+z3NWL7TqN4LOxAZmGGfsB3Oew9/yqvp/jix0NJZtM0XUr7TA6pdakw2KCDj5VwRjDAgMQTuwccUzwN4f1jxBbDVNQ1OWDSpd8X2KE4W5jO5WDYOAMkjJy3HBXCmtTWLoeGIbfwR4Y0hbi8vbZn3zsm1s5DMwbh2IR+uAMKMEfLXv4DAPDwVaXxdOv3LzLwmUwWMcse1Wrrom4wprXmSemqW769ddDc1Dx9o9j4Vg11WaRbpT9mtz8skjg4Kn0APBbkDtnIzxOva1d61/pV/pM+lSKqxCGcnLDdw3Kr1Jx07V1Hhv4d2em6Xpq6n++vrK7e7DRSnYHOAAOASMJGee69cEg8d4h1pvG9pNc21q1tGG+zxGVs7wrBsnA4OGHAzj1rsxledFU61TpKLt6NN37HnZ9hsDLDqFO/IqkVKo9oxcrKyvrpq3+Rzcus2kTlQXkx3QcfrV6ORJo1kjYMjDIIpbPRvstt5eI2Zh+8J53H8ulJDpEtnFMtuyHc25FcnC/jjP+fxr36HEFFNSrVotNapXvH/AD/zPl8dWyKalSwUpRlFpJyd1NbN7LltuujV72eg6ioItP1dZw0k1q0ZPK88DPbj+dPuLDVGkfyJbZYyBt3ZyPXtWq4oy7k5uZ+ltTleDoKsqX1qnqr35nbdK3w3vrfbZOxJU1t/x8L+P8jVX7BqgtVQS2zTEnLnIwO2OOTUunWd/DNuvJYZAOQUJyPbGBXJj+IsBWw1SlGWsoS+9xdl6mGJw1GOGqTjiKcmrpJN3l0uk0tPWza6HsPhXVrXVtCiNsWzan7LKrLgq6AA/UdCPY+uRW1Xknh/xQvhjwHq2oxQi4ll1iWKAZ+TcY1ILH+7gE8deBxnIz9N+KWsWuowXepXcF7Zzs/m2UMOx7cZ4IJUZPoNzZHBIPI86ji4RpQU97I/TsPw9iqtPmor3Uktd27Jtbfnbte57XRRRXeeIFFFFABRRRQAUUUUAfOniVpE8c6qy+Z/x/SbljzuYbuQMD0pmmFpfGelTSLKu/Vbdo45AcqpmB6kdif51N4jmit/G2utMu5WnlVeAeSwHc+mah0qZZfFOiqu5fL1O3VI5DlgvnL7AADAGPegD6OooooAqapp1vq+l3On3a7oLiMo3AJGehGQRkHBB7ECvOrOx+Ifhixl0TSbOzvLKNn+z3bFFYBucgFxyCScMDznkjFeoUVlUpKbvdp+R34XMJ4eDpuMZxbvaSur991r0POE+Gl3N4MutOvdT36pNdm980O7R+YAVAbPJyCSWAByR97aMsW1+JetTWVvdyQaRFbMsj3MbqWlIwPmVWO49TtwqnnPavQ7u7gsoDNcTJFGOrOcZOM4A7njoKxbfxjpE27zJJoAMY8yPO76bc1y1XhqUlGU+X52v6iq8Teyly4hwcm21zJaXVnbpbbe60Klh4dvrb4l6rr0nlfYrm0SOMhvmLfICMY7eWf++hjPOM/xb4X1o+J7TxR4cFq15bxhZIHAUynJGcng5ViDkggKMHOMdVBrulXOPLv4cnoHbaT+BxWlW8YUqkWou+t9H1Chm9T2irQalaKjbdONrWfqjiPDtl42uvEn9ra9cxWVosZiOnxMGV+BggAkDk53ZLcY6Gsmx+G99L4BudEvZ4obwX7XVuyNuQkKEG7jOGAJ45GQeoK16bRT+rwas7vf8Tf+2K8ZXpqMfh2Vvhvb83e92zzVbX4l61NZW93JBpEVsyyPcxupaUjA+ZVY7j1O3Cqec9qt654a1vSPFTeJfCcUE810pjurOUKq8gZYcrwSoJ5B3c8gkDvXdY0Z3YKiglmY4AHqaxv+Eq0Qj/j9x/2yf/Csaio0larOz6XeunYyq8QRoTTkqcE01y2STT3vd3f36dDmNI0DxVrHiy11/wATPFaR2W4Q2cEp67cZG1iACTk5Yk7dpG3GKMOheNfBzX2neGYYL7TbhvNimmKCSFiMd2XLDA6gqcA4GSK7b/hK9E/5/f8AyE/+FH/CV6J/z+/+Qn/wrP2mEt/FV+/MrmX+tVG+sqbhZLl05dG2ut73b6631OPsfA+sWvwx1fQnEDX1xcmaJVk+VgrJjnHBbyyRn1Gcc46/+xrj/hBf7D3xfaf7N+yb8nZv8rZnOM4z7fhVeXxrpUcpRFuZVHR0QAH8yD+lR/8ACcaZ/wA8Lv8A75X/AOKqY4vBQ0VRbWOPEcVUK0vfqx+Lm076L7tCz4L0m60Twjp+n3oVbmJWLqrZClnZsZ9RuwccZ9ah1eLxIfFukTaZHZnS1VlunkA3gEgsMn5sEKu3b/EPm4Apn/Cc6Z/zwu/++V/+Kq/pXiGy1iV4IFlSRF3bZFAyOhxgn2/OtKeLwtTlpQqa9PkRSz7DVsVKopRlKfNo9ryvt9+hqyiQwuIWVJSp2M67lB7EgEZHtkfWuI0XQ/EOseFdW0nxWkEQuWJgKKgZWJ3FyIyFI34b1J3Z4IruqK7JU1J3Z3UMXOjBxgldtNO2qa10fTzPLdO1/wAX+FNLbQpPDF1qM1pujgvI/MeMr1Xop3AZ6ArxgYBFOufB3inWvA1zDq160mqte/a47eWRWUAKV2BhwpO4kAEqMKPlyxr1Cs2817S9Pn+z3F0EkAB2hWOM+uAcVzTp06cf3s/d21djsrZ/Cg/bKEKcm03J9X83onrdI80gfX9TvNPj0nwJYaTcwSJLNcz2KojYI6FkBUZOcKS/oeDnqdN0fUIfizrOqyWzLYzWSLHMSNrEiMYHv+7b6ceoztDxVomf+P7/AMhP/hVmPXNLlXcmoWwH+1IFP5GopyoPVVE7PujCpxHQrKSpciTi46Su9Wnu35adji/FWl61o3jWDxXoenteRGEJeQxOxaQ/d5UckbdmMAgFMkcc3vDuseMNb8SfarvSv7M0NYyjQXC4fcACCCQGJJI5wFwCOozXZQXMF0he3mjmQHBaNwwz6cVLW8aS5uaMtHqaf2pGph1CVOMmo8qlq3b77XXR9DyKx8Ca1efDW50yWD7NfR6k11FDKR+9CoExnOBk7sE8HA7HNED6/qd5p8ek+BLDSbmCRJZrmexVEbBHQsgKjJzhSX9Dwc+u0VP1WKtZ2Ov+36snJ1IJ3ba3sr76X1+YUUUV1HghRRRQAUUUUAFFFFAHzzr1xHaeP9SuJI1l2X0jKrDIzu4yuRn/ACe1Jb6j/bHjnSr3y44N2o2qrGoO5gJV+Zj0J/8ArelR+JZ1t/G2tMy/8vUi/KcFfmByDj2x+JpmmTW1x4s0BrdZOLy0WTzMcsJFyR1OPqf/AKwB9HUUUUAFYWt+JLbSQ0S4mu+0QPAB7k9vp16Ve1m5W00W8lMhiYQsFZc5DEYGMe5FePzSFmKDoDjHrXlZhjKlKSpUt2r37fIwVLE4zErB4VqLtdyevKr20XVvpey0Zd1LWbrUrjzLiVpDk7Rn5V6fdHbp+nes7c2c7jk+9JRXiqCu29W92+p9hgMjweBpuEI8zl8UpauXr/lsSCZxj5qs2up3VsxEE8kO/G4xyFc49cdetUqKl0YbpWfloY4nhvLMRFr2MYt9Yrla89Lf15Hrnh7Vxq2mq7YE8WElBIznH3sDsf55HativINF1WTTL+O4QBiowyZIDqeo/wAPoK9at7iG7t0ngkEkTjKsO9e/l2LdaDp1Pjj+K7/5nyNB1qNSWDxP8SHX+ZdJL169nuV9X/5Al/8A9e0n/oJrxS7lkW4cK7AADgH2r2vV/wDkCX//AF7Sf+gmvEbz/j5k/D+QrDHJPFRuvsv80eNmyTxkLr7L/NDPOl/56v8A99H3pfOl/wCej/8AfR9qi/z/ADpf8/yrL2cOyOL2cOyAsWOWJJ9T9KWm/wCf0p1WlYtaDa7v4ef8hB/+vd//AENa4Su7+HX/ACEX/wCvZ/8A0Nazl/Fpf4l+TFH/AHij/iX5M9HoormfGGsTaVYxxwHy2mD7pe6AYzj3569v5e1XrKjTc2r+n4fifUYrERw1J1Z7Lt9y/Eb4k8Ux6UrW9sym5A+Z25EX+Lf5Oa8pu9Tmu5C26RSxLFi3zE+pPemX17JezF3dsZyQxySc8k+9Va+hyrIVdYrHRvPpHdRX5OXd9Onc46WElXmsRi1r0j0iv1fd/cSi5nBBE0nHqxqT+0Lr/nr/AOOj/Cq1Fe5UyvA1dalGD9Yp/odUsFhp/FTi/kjqfDfiSfT7oOhyxGJIycLKv9D7/wBM16xYX8GpWa3Vs+9G4x0KnupHY18/AlSCCQRyCK7HwdrNzHqsCIcCWVIpQfuupOAfqP8APBxXxubZU8rn9Yw6/cyesf5W3a68m+nTpoedKEstnzU9aMnqv5W3uvJvddOh61RRRWJ7QUUUUAFFFFABRRRSAKKKKYHzp4ml8rxtrX73yv8ASn+bns6k9OeQCKnivLbUPF/h2e1+4l1aRNuGCzCbJOO4wRzUHiZY28ba15zbUW6kbofmO7px/n3FM069bUPGWhy+UkSLfWqLHGuFUCVew9Tk/jQB9HUUUUAc74ynMWgNGBkTTKh9urf+y15cxyxPqa9D8eNMLO0RAfJLsW443YG3n8W/yK876V81jpc2Ll5JL9f1O3hxRlj8TNvWKhFd7at/JtrXumgooorA+zAUUUUCHxDMq84r1vw3EIfD1mobdlN2cY6knH615NCMy59BmvYdJha30i0ib76QqD9cV2ZWr4mUu0fzf/APh84aqZu2vsU4r5ylJ/kl/VxdX/5Al/8A9e0n/oJrxG8/4+ZPoP5V7dq//IEv/wDr2k/9BNeI3n/Hy/1H8hXRjP8Aeo/4X+aPlc1/3yH+F/miH/P86P8AP8qT/P8AOl/z/KoOQT/P6U6m/wCf0p1ADa7v4d/8hCT/AK9n/wDQ1rhK6vwL/wAh61/3pP8A0A1lN2qU3/eX46fqTflrUpf3l+On6nq9cL8RnzBbL6RSk/iB/hXdVwvxFUeRbN6xyj8sV62Mt7Nf4o/+lI97Nv8AdH6x/wDSkeXUUUV+oHphRRRQAlbfhubyNSWcnhJo3/Jj/hWLWz4bi8/UFhPSSWNPzYivA4nt/ZdS/eH/AKXE83Nv90frH/0pHuVFFFfMHohRRRQAUUUUAFFFFABRRRQB88a9JbRePNXa6VWi+1S7l2BicnHHoec5z2qDTZbKXxforWcDRRDUbZfmIyw81cEgcA9ent9ata3dQWnjnXGuLSO6R7mRNrAHYSwO4A8EgAjt161Q0cW//CWaL9n+4dRtvlyTt/fLjr/9f6mgD6TooooApalp8Op2MlrOq/MDtYjOxscMK8k1Kylsr2SCZPmRtrDt9foRXtFcn4u0L7bF9ttYg0ycSIiEtIvAHTqR/LvwK8jNMK5JV6a96O/mv+Ack6tTA4iOPoq7jpJfzQ6r1W68/uPN6Ksz2U9vMYpEaNl6rIMMO/QioTE4GdteKq0G7XPs6PEGV13aFeN+zdnr5Ozv5DKKXa2cbTk+1aWk6Jd6nMUgj34wWJ4VM9yf6D0PBqnNaJat7JdR5jneDwVNTlLmcvhjHVy9PTq9iPSbVbjUreGTcqSSJGSvoSM4/OvZax9H8PWekqGVRLP1M0gGRxj5fQdfzrYr3cuws6EZSqbyt8rHx1OVetXqYrEJKU7aLWySslfr3fmylq//ACBL/wD69pP/AEE14lef8fL/AOe1e26v/wAgS/8A+vaT/wBBNeJXn/Hy/wDntWON/wB7j/hf5o8TNf8AfIf4X+aIP8/zo/z/ACpP8/zpf8/yqDkEp1N/z+lOoAbXWeBv+Q9a/wC9J/6Aa5Ous8C/8h61/wB6T/0A1lV+Kn/ij+ZnL+JS/wAcfzR6tXNeMNGm1WxjkgAdoA26Lu4OM49+Onf9D0tFe1XoqtTcG7em67M+rxWHjiaTpT2fb71+J8+3lo1u/rGfusR+hqtXrfinwst4kt5ZxZkP+thH/LT3X/a/n9evl1zYSxSYjVnXswHI9vr719Dk2fOT+qY5pVFtLZTXfyfdfNHDhsZOlP6ti3aS2lspLv690VaKl+zT/wDPGT/vk0ot5yQBDJz6qRX0TxuGSu6kfvR6DxNFfbX3ohALEAAkngAV2Hg/RrqTVYZEXPlypJK38KAHOPqef/1DNL4W8KS6hOZGJEKEeZLjp/srnqfft/P1O1tYbO3jtrZAkaDAAPT/ABr4rNc0ea/uaDtRTV3bWbTvZX2in13b8jzJOWZNcjtRTTv1k076dkn169Ceiiiuc9gKKKKACiiigAooopAFFFFMD528Q+X/AMJzq6zKuxrqYfNkDvj9cVDpaW6eL9C+z/c+3Wu5d24BvNUHB9OAfbNP8TlE8ba1u2/8fUm1mGQp3DkjvxmotIhVPFmiyQtG0Taja/NH0yZFJGDypyDx/TBoA+kaKKKACiiigCKaGOaNo5o1lQ9VYAj8jWXJ4X0aVwzWYH+47KPyBrZorOpRp1Pjin6kThGatNXXn5nOz+DtJmwUSaAL18uTr/31mtq1tLaxhEFtAscQ7KOpxjJ9Tx1NWKKilha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}, "metadata": {} } ], "source": [ "import cv2\n", "import numpy as np\n", "import gradio as gr\n", "from PIL import Image\n", "\n", "def detect_shapes(image):\n", " \"\"\"\n", " Detect shapes in an image and return the annotated result\n", " \"\"\"\n", " # Convert PIL Image to OpenCV format\n", " img = np.array(image)\n", " img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n", "\n", " # Create a copy for drawing contours\n", " img_contour = img.copy()\n", "\n", " # Convert to grayscale\n", " gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", "\n", " # Apply Gaussian blur\n", " blur = cv2.GaussianBlur(gray, (5, 5), 1)\n", "\n", " # Edge detection\n", " edges = cv2.Canny(blur, 50, 150)\n", "\n", " # Find contours\n", " contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n", "\n", " shapes_detected = []\n", "\n", " # Process each contour\n", " for cnt in contours:\n", " area = cv2.contourArea(cnt)\n", " if area > 500: # Filter small contours\n", " # Approximate contour to polygon\n", " epsilon = 0.02 * cv2.arcLength(cnt, True)\n", " approx = cv2.approxPolyDP(cnt, epsilon, True)\n", "\n", " # Get bounding rectangle\n", " x, y, w, h = cv2.boundingRect(approx)\n", "\n", " # Determine shape based on number of vertices\n", " nb_sommets = len(approx)\n", " shape = \"Indéfini\"\n", "\n", " if nb_sommets == 3:\n", " shape = \"Triangle\"\n", " elif nb_sommets == 4:\n", " ratio = w / float(h)\n", " shape = \"Carré\" if 0.95 < ratio < 1.05 else \"Rectangle\"\n", " elif nb_sommets > 6:\n", " shape = \"Cercle\"\n", "\n", " # Draw contour and label\n", " cv2.drawContours(img_contour, [approx], 0, (0, 255, 0), 2)\n", " cv2.putText(img_contour, shape, (x, y - 10),\n", " cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n", "\n", " shapes_detected.append({\n", " 'shape': shape,\n", " 'vertices': nb_sommets,\n", " 'area': int(area),\n", " 'position': f\"({x}, {y})\"\n", " })\n", "\n", " # Convert back to RGB for display\n", " img_contour = cv2.cvtColor(img_contour, cv2.COLOR_BGR2RGB)\n", "\n", " # Create summary text\n", " summary = f\"Détecté {len(shapes_detected)} forme(s):\\n\"\n", " for i, shape_info in enumerate(shapes_detected, 1):\n", " summary += f\"{i}. {shape_info['shape']} - {shape_info['vertices']} sommets - Aire: {shape_info['area']} pixels\\n\"\n", "\n", " return img_contour, summary\n", "\n", "# Create Gradio interface\n", "def create_interface():\n", " with gr.Blocks(title=\"Détecteur de Formes Géométriques\", theme=gr.themes.Soft()) as interface:\n", " gr.Markdown(\"# 🔍 Détecteur de Formes Géométriques\")\n", " gr.Markdown(\"Téléchargez une image pour détecter et identifier les formes géométriques (triangles, carrés, rectangles, cercles)\")\n", "\n", " with gr.Row():\n", " with gr.Column():\n", " input_image = gr.Image(\n", " type=\"pil\",\n", " label=\"📸 Image d'entrée\",\n", " height=400\n", " )\n", "\n", " detect_btn = gr.Button(\n", " \"🔍 Détecter les formes\",\n", " variant=\"primary\",\n", " size=\"lg\"\n", " )\n", "\n", " gr.Markdown(\"### Instructions:\")\n", " gr.Markdown(\"\"\"\n", " - Téléchargez une image contenant des formes géométriques\n", " - Les formes doivent être suffisamment grandes (aire > 500 pixels)\n", " - Fonctionne mieux avec des formes aux contours nets\n", " - Supporte: triangles, carrés, rectangles, cercles\n", " \"\"\")\n", "\n", " with gr.Column():\n", " output_image = gr.Image(\n", " label=\"🎯 Formes détectées\",\n", " height=400\n", " )\n", "\n", " output_text = gr.Textbox(\n", " label=\"📊 Résumé de détection\",\n", " lines=8,\n", " max_lines=15\n", " )\n", "\n", " # Examples section\n", " gr.Markdown(\"### 📝 Exemples\")\n", " gr.Examples(\n", " examples=[\n", " # You can add example images here if you have them\n", " # [\"path/to/example1.jpg\"],\n", " # [\"path/to/example2.jpg\"],\n", " ],\n", " inputs=input_image,\n", " label=\"Cliquez sur un exemple pour le tester\"\n", " )\n", "\n", " # Event handlers\n", " detect_btn.click(\n", " fn=detect_shapes,\n", " inputs=input_image,\n", " outputs=[output_image, output_text]\n", " )\n", "\n", " input_image.change(\n", " fn=detect_shapes,\n", " inputs=input_image,\n", " outputs=[output_image, output_text]\n", " )\n", "\n", " return interface\n", "\n", "# Launch the app\n", "if __name__ == \"__main__\":\n", " interface = create_interface()\n", " interface.launch(\n", " share=True,\n", " server_name=\"0.0.0.0\",\n", " server_port=7860\n", " )\n" ] }, { "cell_type": "code", "source": [], "metadata": { "id": "tWDnF68M-7b5" }, "execution_count": null, "outputs": [] } ] }