2022 한국인공지능학회 & 네이버 추계 공동학술대회 참가

KCCV 2022. Korean Conference on. Computer Vision. 참가

(NIA 데이터셋 과제준비) Oxford-IIIT Pet Dataset

# Images: https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz # Annotations: https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz from IPython.display import Image, display from tensorflow.keras.preprocessing.image import load_img import PIL from PIL import ImageOps import os from tensorflow.keras import layers input_dir = "/root/yj/yj/Kaggle/data/images" target_dir = "/r..

Brain MRI Images for Brain Tumor Detection

def load_data(dir_list, image_size): # load all images in a directory X = [] y = [] image_width, image_height = image_size for directory in dir_list: for filename in listdir(directory): image = cv2.imread(directory+'/'+filename) image = crop_brain_contour(image, plot=False) image = cv2.resize(image, dsize=(image_width, image_height), interpolation=cv2.INTER_CUBIC) # normalize values image = imag..

2022_Ukraine Russia War visualization

import numpy as np import pandas as pd import plotly import plotly.graph_objs as go import plotly.express as px from plotly.subplots import make_subplots import seaborn as sns ru_losses_per = pd.read_csv('/root/yj/yj/Kaggle/data/Ukraine_war/russia_losses_personnel.csv') ru_losses_eq = pd.read_csv('/root/yj/yj/Kaggle/data/Ukraine_war/russia_losses_equipment.csv') x, y = ru_losses_per['date'], ru_..

Age, Gender & Ethnicity Prediction

import numpy as np import pandas as pd import tensorflow as tf import tensorflow.keras.layers as L import matplotlib.pyplot as plt import plotly.graph_objects as go import plotly.express as px from sklearn.model_selection import train_test_split Loading Dataset data = pd.read_csv("/root/yj/yj/Kaggle/data/age_gender/age_gender.csv") data['pixels'] = data['pixels'].apply(lambda x : np.array(x.spli..

(딥러닝) Face Mask Detection

import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.patches as mpatches import seaborn as sns from collections import Counter import os import xmltodict import torch from torchvision import datasets,transforms,models from torch.utils.data import Dataset,DataLoader from PIL import Image import sys import torch.optim as optim import xmltodict print("PyTorch Ver..

(컴퓨터비전) Yolov5, Midea Pipe 신체 감지 만들기

def calculate_angle(a,b,c): a = np.array(a) # First b = np.array(b) # Mid c = np.array(c) # End radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0]) angle = np.abs(radians*180.0/np.pi) if angle >180.0: angle = 360-angle return angle cap = cv2.VideoCapture("./final.mp4") counter = 0 stage = None time = 0 left_elbow_list = [] right_elbow_list = [] left_knee_list = [] right..

(computer vision) IOU 구하기

## IOU 구하기 ## 입력인자로 후보 박스와 실제 박스를 받아서 IOU를 계산하는 함수 생성 import numpy as np def compute_iou(cand_box, gt_box): # Calculate intersection areas x1 = np.maximum(cand_box[0], gt_box[0]) y1 = np.maximum(cand_box[1], gt_box[1]) x2 = np.minimum(cand_box[2], gt_box[2]) y2 = np.minimum(cand_box[3], gt_box[3]) intersection = np.maximum(x2 - x1, 0) * np.maximum(y2 - y1, 0) cand_box_area = (cand_box[2] - cand_..

(computer vision) selective search Object Detection

# opencv의 rectangle()을 이용하여 시각화 # rectangle()은 이미지와 좌상단 좌표, 우하단 좌표, box컬러색, 두께등을 인자로 입력하면 원본 이미지에 box를 그려줌. green_rgb = (125, 255, 51) img_rgb_copy = img_rgb.copy() for rect in cand_rects: left = rect[0] top = rect[1] # rect[2], rect[3]은 너비와 높이이므로 우하단 좌표를 구하기 위해 좌상단 좌표에 각각을 더함. right = left + rect[2] bottom = top + rect[3] img_rgb_copy = cv2.rectangle(img_rgb_copy, (left, top), (right, bottom), ..