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๐Ÿ’ป Programming97

[SQL] MySQL ๊ธฐ๋ณธ ๋ฌธ๋ฒ• ๋ฐ ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค ๋ฌธ์ œ ์ •๋ฆฌ SQL(Structured Query Language) ๋Š” ๊ด€๊ณ„ํ˜• ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์‹œ์Šคํ…œ(RDBMS) ์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ์„ค๊ณ„๋œ ํŠน์ˆ˜ ๋ชฉ์ ์˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด์ด๊ณ , MySQL์€ ์„ธ๊ณ„์—์„œ ๊ฐ€์žฅ ๋งŽ์ด ์“ฐ์ด๋Š” ์˜คํ”ˆ์†Œ์Šค์˜ ๊ด€๊ณ„ํ˜• ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ด€๋ฆฌ์‹œ์Šคํ…œ์ž…๋‹ˆ๋‹ค. MySQL SELECT ์˜ ๊ธฐ๋ณธ์ ์ด์ง€๋งŒ ์ž์ฃผ ์‚ฌ์šฉํ•˜๋Š” ๋ฌธ๋ฒ•์„ ์ •๋ฆฌํ•ฉ๋‹ˆ๋‹ค. SELCET SELECT [์ปฌ๋Ÿผ๋ช…] FROM [ํ…Œ์ด๋ธ”๋ช…]; ์–ด๋–ค ๋ฐ์ดํ„ฐ๋ฅผ ์–ด๋”” ํ…Œ์ด๋ธ”์—์„œ ๊ฐ€์ ธ์˜ฌ์ง€ SELECT * FROM [ํ…Œ์ด๋ธ”๋ช…]; → *(astarisk) ๋ชจ๋“  ์ปฌ๋Ÿผ์„ ๊ฐ€์ ธ์˜ด SELECT [์ปฌ๋Ÿผ๋ช…] FROM [ํ…Œ์ด๋ธ”๋ช…] WHERE [์กฐ๊ฑด] ORDER BY [์ปฌ๋Ÿผ๋ช…] DESC / ASC LIMIT ์ˆซ์ž N; → ํ…Œ์ด๋ธ”์˜ ์„ ํƒ๋œ ์ปฌ๋Ÿผ์„ ํŠน์ • ์กฐ๊ฑด์—์„œ ์ปฌ๋Ÿผ ๋ฐ์ดํ„ฐ์˜ ๋‚ด๋ฆผ์ฐจ์ˆœ / .. 2022. 8. 1.
[python] OpenCV, PIL, Numpy, PyTorch ํƒ€์ž… ๋ถ„์„, ํƒ€์ž… ๋ณ€ํ™˜ ์ •๋ฆฌ # PIL RGB ํƒ€์ž…์œผ๋กœ ์ด๋ฏธ์ง€ ์ฝ์Œ torchvision.transforms ๋ชจ๋“ˆ์—์„œ ์ง€์›ํ•˜๋Š” ๋ฐ์ดํ„ฐ ํƒ€์ž…์ด PIL์˜ Image array ์ด๋ฏ€๋กœ pytorch ํ”„๋ ˆ์ž„์›Œํฌ ์‚ฌ์šฉ์‹œ PIL ๋กœ ์ด๋ฏธ์ง€๋ฅผ ๋กœ๋”ฉํ•˜๋Š” ๋ฐฉ์‹์„ ๋งŽ์ด ์‚ฌ์šฉ numpy array ์ธ๋ฑ์‹ฑ ๋ถˆ๊ฐ€๋Šฅ → ์ƒํ™ฉ์— ๋”ฐ๋ผ numpy array๋กœ ๋ฐ”๊พธ๊ธฐ ์œ„ํ•œ ์ž‘์—… ํ•„์š” ์‚ฌ์ด์ฆˆ ํ™•์ธ์‹œ .size() ๋ฉ”์„œ๋“œ๋กœ ํ™•์ธ ๊ฐ€๋Šฅํ•œ๋ฐ, (W,H)๋กœ ์ถœ๋ ฅ๋จ. ํ•˜์ง€๋งŒ, numpy array ๋กœ ๋ณ€ํ™˜์‹œ (H,W,C)๋กœ ๋‚˜์˜ค๋‹ˆ๊นŒ ์ฃผ์˜. ์ฆ‰, .size()๋กœ ํ™•์ธ ์‹œ์—๋งŒ (W,H)๋กœ ๋‚˜์˜ค๋Š” ๊ฒƒ. from PIL import Image image = Image.open("image.jpg").convert('RGB') image.show() image.save("saved.. 2022. 7. 21.
[C++] ํ–‰๋ ฌ๊ณฑ ์—ฐ์‚ฐ ์ตœ์ ํ™” ๋ฐ ์†๋„ ๋น„๊ต C++ ์—์„œ ํ–‰๋ ฌ๊ณฑ ์—ฐ์‚ฐ์„ ๊ตฌํ˜„ํ•  ๋•Œ, ์—ฌ๋Ÿฌ ๋ฐฉ๋ฒ•์— ๋”ฐ๋ผ ์†๋„ ์ฐจ์ด๊ฐ€ ๋งŽ์ด ๋‚œ๋‹ค๊ณ  ํ•ด์„œ ๊ฐ„๋‹จํ•œ ์‹คํ—˜์œผ๋กœ ์†๋„ ๋น„๊ต๋ฅผ ํ•ด๋ณด์•˜์Šต๋‹ˆ๋‹ค. ํ–‰๋ ฌ๊ณฑ ์—ฐ์‚ฐ ๊ณผ์ •์€ ๋”ฐ๋กœ ์„ค๋ช…ํ•˜์ง€ ์•Š๊ณ , ๊ฐ€์žฅ ๋‚˜์ด๋ธŒํ•œ ๋ฐฉ์‹์—์„œ ์†๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋ช‡๊ฐ€์ง€ ๋ฐฉ๋ฒ•๋“ค์„ ๋‹จ๊ณ„์ ์œผ๋กœ ์„ค๋ช…ํ•ฉ๋‹ˆ๋‹ค. ์‚ฌ์‹ค ํ–‰๋ ฌ ์—ฐ์‚ฐ ์‹œ ๋งŽ์ด ์‚ฌ์šฉํ•˜๋Š” Eigen ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ์“ฐ๋ฉด ์ตœ์ ํ™”๊ฐ€ ์ž˜๋˜์–ด ์žˆ์–ด์„œ ์‹ค๋ฌด์—์„œ ํ–‰๋ ฌ ์—ฐ์‚ฐ์„ ์ง์ ‘ ๊ตฌํ˜„ํ•ด์•ผํ•  ์ƒํ™ฉ์ด ์–ผ๋งˆ๋‚˜ ์žˆ๋Š”์ง€๋Š” ์ž˜ ๋ชจ๋ฅด๊ฒ ์Šต๋‹ˆ๋‹ค. ์•„์ง ๊ฒฝํ—˜์ด ๋งŽ์ด ์—†์–ด์„œ... ใ…  Baseline (naive version) /* @input mat_x: m x k size matrix mat_y: k x n size matrix @output mat_z: m x n size matrix */ void matmult_baseline.. 2022. 6. 30.
[pytorch] COCO Data Format ์ „์šฉ Custom Dataset ์ƒ์„ฑ Object Detection๊ณผ Segmentation ์—์„œ ํ”ํžˆ ์‚ฌ์šฉ๋˜๋Š” COCO dataformat ์ „์šฉ Customdataset์„ ์ƒ์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ํ”ํžˆ ์•Œ๊ณ  ์žˆ๋Š” COCO ๋ฐ์ดํ„ฐ์…‹์ด ์žˆ๊ณ , ๋งŽ์€ ๋ฐ์ดํ„ฐ์…‹๋“ค์ด COCO data format ์„ ๋”ฐ๋ฅด๋Š”๋ฐ, ์ด๋Ÿฌํ•œ ๋ฐ์ดํ„ฐ์…‹์„ ์‚ฌ์šฉํ•˜๊ธฐ ์œ„ํ•ด Customdataset์„ ๊ตฌ์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ COCO API ์ธ Pycocotools ์‚ฌ์šฉ๋ฒ•์„ ์„ค๋ช…ํ•œ๋‹ค. COCO Data Format Detection task์—์„œ๋Š” Bounding box์˜ ์œ„์น˜์™€ class label์ด ํ•„์š”ํ•˜๊ณ  segmentation task ์—์„œ๋Š” segment mask ์ •๋ณด๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ annotation ์ •๋ณด๋“ค์€ json ํ˜•ํƒœ๋กœ ์ œ๊ณต๋˜๊ณ , JSON ํŒŒ์ผ์—๋Š” Info, Licen.. 2022. 6. 4.
[python] ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค_์นด์นด์˜ค21_๋ฉ”๋‰ด ๋ฆฌ๋‰ด์–ผ (์™„์ „ํƒ์ƒ‰, combinations) ๋ฌธ์ œ ํ’€์ด itertools ์—์„œ combinations ์„ import ํ•ด์„œ ๋ฌธ์ž์—ด์˜ ์กฐํ•ฉ์„ ์ด์šฉํ•œ๋‹ค. combinations ๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด ์•„๋ž˜์ฒ˜๋Ÿผ ๊ฐ ํŠœํ”Œ์— ํ•˜๋‚˜์˜ ์กฐํ•ฉ์ด ๊ฐ ์›์†Œ๋ณ„๋กœ ์ชผ๊ฐœ์ ธ์„œ ์ถœ๋ ฅ๋˜๋ฏ€๋กœ, ํ•„์š”์— ๋”ฐ๋ผ ํ›„์ฒ˜๋ฆฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. a = 'abcd' b = list(combinations(a,2)) print(b) for i in range(len(b)): b[i] = ''.join(b[i]) print(b) ๋ฌธ์ œ๋Š” order ๋ฌธ์ž์—ด์„ ์ˆœํšŒํ•˜๋ฉด์„œ ๊ฐ ์ฃผ๋ฌธ๋ณ„๋กœ ๋ชจ๋“  ์กฐํ•ฉ์˜ ๊ฐœ์ˆ˜๋ฅผ foodMap์œผ๋กœ ์นด์šดํŠธํ•˜๊ณ , ์„ธํŠธ์˜ ๋ฉ”๋‰ด ๊ฐœ์ˆ˜๋ณ„ max๊ฐ’์„ ์นด์šดํŠธํ•ด๋‘”๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋งˆ์ง€๋ง‰์— course๋ฅผ ์ˆœํšŒํ•˜๋ฉด์„œ foodMap์—์„œ value๊ฐ’์ด maxCnt์™€ ๋™์ผํ•œ key๋งŒ answer์— appendํ•˜๊ณ  ๋งˆ์ง€๋ง‰์— .. 2022. 2. 21.
[python] ๋ฐฑ์ค€3190_๋ฑ€ (๊ตฌํ˜„) ๋ฌธ์ œ ํ’€์ด ์ด์ฐจ์› ๋ฆฌ์ŠคํŠธ์ƒ์˜ ๋งต์—์„œ ๋ฑ€์ด ์ด๋™ํ•˜๋„๋ก ๋ฆฌ์ŠคํŠธ์˜ ํŠน์ • ์œ„์น˜์—์„œ ๋™,๋‚จ,์„œ,๋ถ์˜ ์œ„์น˜๋กœ ์ด๋™ํ•˜๋Š” ๊ธฐ๋Šฅ์„ ๊ตฌํ˜„ํ•ด์•ผ ํ•œ๋‹ค. ๋˜ํ•œ, ๋ฑ€์˜ ๋จธ๋ฆฌ๊ฐ€ ๋ฑ€์˜ ๋ชธ์— ๋‹ฟ๋Š” ์ˆœ๊ฐ„ ์ข…๋ฃŒํ•ด์•ผ ํ•˜๋ฏ€๋กœ, ๋งค ์‹œ์ ๋งˆ๋‹ค ๋ฑ€์ด ์กด์žฌํ•˜๋Š” ์œ„์น˜๋ฅผ ํ•ญ์ƒ ์ด์ฐจ์› ๋ฆฌ์ŠคํŠธ์— ๊ธฐ๋กํ•ด์•ผ ํ•œ๋‹ค. n = int(input()) k = int(input()) data= [[0]*(n+1) for _ in range(n+1)] info = [] for _ in range(k): a,b = map(int,input().split()) data[a][b] = 1 l = int(input()) for _ in range(l): x,c = input().split() info.append((int(x),c)) # ๋™,๋‚จ,์„œ,๋ถ dx = [0,1,0,-.. 2022. 2. 15.
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