Faster R-CNN<\/strong>: \uc815\ud655\ub3c4\uac00 \ub192\uc544 \uac1d\uccb4 \uac80\ucd9c\uc5d0\uc11c \uac00\uc7a5 \ub9ce\uc774 \uc0ac\uc6a9\ub418\ub294 \ubc29\ubc95 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n5. \ud504\ub85c\uc81d\ud2b8 \uc608\uc81c: \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\uc6a9 \uac1d\uccb4 \uac80\ucd9c \uc2dc\uc2a4\ud15c \uad6c\ucd95<\/h2>\n
\uc774 \uc139\uc158\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\uc6a9 \uac1d\uccb4 \uac80\ucd9c \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \uacfc\uc815\uc744 \ub2e8\uacc4\ubcc4\ub85c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
5.1 \uac1c\ubc1c \ud658\uacbd \uc124\uc815<\/h3>\n
\uba3c\uc800, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \uac1c\ubc1c \ud658\uacbd\uc744 \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \ud504\ub85c\uc81d\ud2b8\uc5d0\ub294 Python, TensorFlow\/Keras, OpenCV, NumPy \ub4f1\uc758 \ub77c\uc774\ube0c\ub7ec\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \ud658\uacbd\uc744 \uc124\uc815\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install tensorflow opencv-python numpy<\/code><\/pre>\n5.2 \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc<\/h3>\n
\uc55e\uc11c \uc5b8\uae09\ud55c COCO \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud55c \ud6c4, \ud559\uc2b5\uc5d0 \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\ub97c \ubd84\ub9ac\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
!wget http:\/\/images.cocodataset.org\/zips\/train2017.zip\n!unzip train2017.zip<\/code><\/pre>\n5.3 \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\uc774\uc81c YOLO \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4 \uac80\ucd9c \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. YOLO \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
import cv2\nimport numpy as np\n\n# YOLO \ubaa8\ub378\uacfc \uac00\uc911\uce58 \ud30c\uc77c \ub85c\ub4dc\nnet = cv2.dnn.readNet(\"yolov3.weights\", \"yolov3.cfg\")\nlayer_names = net.getLayerNames()\noutput_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n\n# \uc774\ubbf8\uc9c0 \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac\nimg = cv2.imread(\"example_image.jpg\")\nimg = cv2.resize(img, None, fx=0.4, fy=0.4)\nheight, width, channels = img.shape\n\n# \ub370\uc774\ud130 \uc900\ube44\nblob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\nnet.setInput(blob)\noutputs = net.forward(output_layers)\n<\/code><\/pre>\n5.4 \uac1d\uccb4 \uac80\ucd9c \ubc0f \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\ubaa8\ub378\uc744 \ud1b5\ud574 \uac80\ucd9c\ub41c \uac1d\uccb4\ub97c \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud574 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4!<\/p>\n
for out in outputs:\n for detection in out:\n scores = detection[5:]\n class_id = np.argmax(scores)\n confidence = scores[class_id]\n if confidence > 0.5:\n center_x = int(detection[0] * width)\n center_y = int(detection[1] * height)\n w = int(detection[2] * width)\n h = int(detection[3] * height)\n\n # \uacbd\uacc4 \uc0c1\uc790 \uc88c\ud45c\n x = int(center_x - w \/ 2)\n y = int(center_y - h \/ 2)\n\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)\n cv2.putText(img, str(classes[class_id]), (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n\n# \uacb0\uacfc \uc774\ubbf8\uc9c0 \ucd9c\ub825\ncv2.imshow(\"Image\", img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()<\/code><\/pre>\n6. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\uc758 \uac1d\uccb4 \uac80\ucd9c \uc2dc\uc2a4\ud15c\uc740 \uc55e\uc73c\ub85c \ub354\uc6b1 \ub9ce\uc740 \uc5f0\uad6c\uac00 \ud544\uc694\ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \uacf5\uac1c\ub41c \ub370\uc774\ud130\uc14b\uc744 \ud1b5\ud574 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uace0, \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc801\uc6a9\ud558\uc5ec \uc2e4\uc81c \uc2dc\uc2a4\ud15c\uc5d0\uc11c\uc758 \uc751\uc6a9 \uac00\ub2a5\uc131\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774 \uae00\uc5d0\uc11c \uc18c\uac1c\ud55c \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud55c \ud504\ub85c\uc81d\ud2b8 \uc608\uc81c\ub97c \ud1b5\ud574 \uc790\uc728 \uc8fc\ud589 \uae30\uc220\uc758 \uae30\ucd08\uc640 \ub370\uc774\ud130\uc14b \ud65c\uc6a9 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0c1\uc138\ud788 \uc774\ud574\ud560 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc55e\uc73c\ub85c\uc758 \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28 \uae30\uc220\uc774 \uc5b4\ub5a4 \ubc29\ud5a5\uc73c\ub85c \ubc1c\uc804\ud574 \ub098\uac08\uc9c0 \uae30\ub300\uac00 \ub429\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\ub294 \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \ube44\uc57d\uc801\uc73c\ub85c \ubc1c\uc804\ud558\uc600\uc73c\uba70, \uadf8 \ud575\uc2ec \uae30\uc220 \uc911 \ud558\ub098\ub294 \uac1d\uccb4 \uac80\ucd9c(Object Detection)\uc785\ub2c8\ub2e4. \uac1d\uccb4 \uac80\ucd9c \uc2dc\uc2a4\ud15c\uc740 \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\uac00 \uc8fc\ubcc0 \ud658\uacbd\uc744 \uc774\ud574\ud558\uace0 \uc548\uc804\ud558\uac8c \uc8fc\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uac1d\uccb4 \uac80\ucd9c \uc2dc\uc2a4\ud15c\uc758 \uc6d0\ub9ac, \ud544\uc694\ud55c \ub370\uc774\ud130\uc14b, \uadf8\ub9ac\uace0 \uc2e4\uc81c \ud504\ub85c\uc81d\ud2b8 \uc608\uc81c\ub97c \ud1b5\ud574 \uc5b4\ub5bb\uac8c \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0\ub97c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. … \ub354 \ubcf4\uae30 “\ud559\uc2b5 \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud55c \ud504\ub85c\uc81d\ud2b8 \uc608\uc81c, \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\uc6a9 \uac1d\uccb4 \uac80\ucd9c \uc2dc\uc2a4\ud15c”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[199],"tags":[],"class_list":["post-39633","post","type-post","status-publish","format-standard","hentry","category-199"],"yoast_head":"\n\ud559\uc2b5 \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud55c \ud504\ub85c\uc81d\ud2b8 \uc608\uc81c, \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\uc6a9 \uac1d\uccb4 \uac80\ucd9c \uc2dc\uc2a4\ud15c - 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