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【代码】yolov8保存结果。

【代码】xml转yolov5。

将onnx读进来用C++导出trtmodel文件。

x方向的soble算子在横轴方向上求梯度属于高通，在y轴方向上低通，用于去噪。拉普拉斯展示的是全局梯度。按照高斯分布进行模糊，梯度大的地方漫模糊力度大，梯度小的地方模糊力度小。作用是将断断续续的连成一片。一、平滑算子均值滤波。

import cv2。

import osimport jsonimport cv2import base64version = '3.16.7'flags = {}lineColor = [0, 255, 0, 128]fillColor = [255, 0, 0, 128]for file in os.listdir('/media/lixuan/Data/MSCOCO/东鹏特饮/box/test'): if 'png' in file: dic = {} dic['.

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import osimport jsonimport cv2for file in os.listdir('/media/lixuan/workSpace/浙江中烟130106/已知场景'): if 'bvv4nhptub0lfvhip6jg.json' in file: img = cv2.imread('/media/lixuan/workSpace/test/images/train/{}.jpg'.format(file.split('.')[0])) .

import numpy as npimport matplotlib.pyplot as pltpredicts = np.load('/home/lixuan/workspace/project/recognition/cigarette_shelve_or_not/predict.npy')#labels = np.load('/home/lixuan/workspace/project/recognition/cigarette_shelve_or_not/label.npy')#x .

1. 已完成的意图娇子币数量打卡人数打卡次数新增用户数连续每周打卡人数打卡人数+打卡次数完美陈列次数打卡失败原因陈列不合格主要原因品牌下哪个系列陈列的最值情况陈列数据查询各省用户数量及占比省内各地市用户数量及占比成都市各区县市用户数量及占比打卡次数分布2. 访问网址http://10.0.0.19:6158/3. 示例语句娇子币数量11月发放了多少娇子币？最近30天发放多少娇子币？上周发放了多少娇子币今天发放了多少娇子币今年一共发放了多少娇子..

import jsonimport cv2import numpy as npf = open('/home/lixuan/下载/icecream_seg_12.6.json')for data in f.readlines(): data = data.strip() Dict = json.loads(data) content = Dict['content'] annotations = Dict['annotation'] w = annotat.

将COCO转json:import jsonwith open('/home/lixuan/fromLX/train.json') as f: jsdict = json.load(f) images = jsdict['images'] categories = jsdict['categories'] annotations = jsdict['annotations'] print(annotations[0]) exit() imag

import jsonimport osimport requestsfor file in os.listdir('../file'): f = open('../file/{}'.format(file)) for data in f.readlines(): data = data.strip() Dict = json.loads(data) content = Dict['content'] response.

import osimport cv2import numpy as npdef proces(img,mask): maskcopy = mask.copy() indexs = np.array(np.where(mask > 0)[:-1]) x1 = np.min(indexs[1]) y1 = np.min(indexs[0]) x2 = np.max(indexs[1]) y2 = np.max(indexs[0]) w .

contours, hierarchy = cv2.findContours(mmask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

from convertrle import torleimport jsonimport osimport cv2import numpy as npdef xyxy2xywh(x): # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right y = np.zeros_like(x) y[:, 0] = (x[:, 0] + x[:, 2.

import jsonimport cv2import numpy as np# with open('/media/lixuan/Data/MSCOCO/annotations_trainval2017/annotations/instances_train2017.json') as f:# datas = json.load(f)# """# info:{'description': 'COCO 2017 Dataset', 'url': 'http://coc.

import jsonimport cv2import numpy as npimport mathdef rad(x): return x * np.pi / 180def cos_dist(a, b): if len(a) != len(b): return None part_up = 0.0 a_sq = 0.0 b_sq = 0.0 for a1, b1 in zip(a, b): part_up += a.

https://zhuanlan.zhihu.com/p/104391192

def dilate_demo(gray): ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15))#定义结构元素的形状和大小 dst = cv2.dilate(binary, kernel)#膨胀操作 return dst...

import jsonwith open('/home/lixuan/fromLX/场景识别/新场景样本/休闲细支新主题陈列/场景识别/训练样本初始数据/1_json/annotations/instances.json') as f: Json = json.load(f) annotations = Json['annotations'] images = Json['images'] image_id_name_dict = {} image_id_wid.

def func(listTemp, n): for i in range(0, len(listTemp), n): yield listTemp[i:i + n]

def getbox(cnt): rect = cv2.minAreaRect(cnt) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度） box = cv2.boxPoints(rect) # 获取最小外接矩形的4个顶点坐标(ps: cv2.boxPoints(rect) for OpenCV 3.x) # box = np.int(box) return [int(np.min(box[:,0])),int(np.min(box[:,1])),int(n

def Nrotate(degrees,x,y,cx,cy): angle = degrees/180*3.1415926 nRotatex = (x-cx)*math.cos(angle) - (y-cy)*math.sin(angle) + cx nRotatey = (x-cx)*math.sin(angle) + (y-cy)*math.cos(angle) + cy return nRotatex, nRotatey

import cv2import numpy as npimport osdef find_max_region(mask_sel): __, contours, hierarchy = cv2.findContours(mask_sel, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) # 找到最大区域并填充 area = [] for j in range(len(contours)): area.append(c.

import cv2import numpy as np# import jsonfrom PIL import Imageimport osimport jsonimport randomfrom math import *count = 0def convrgba(img): threshold = 254 dist = 5 # np.asarray(img) is read only. Wrap it in np.array to make it mod.

maskimg = cv2.imread('m1.png') locs = np.where(maskimg==255) x0 = np.min(locs[1]) x1 = np.max(locs[1]) y0 = np.min(locs[0]) y1 = np.max(locs[0])

import cv2from PIL import Imageimport numpy as npimport osfor file in os.listdir('/home/lixuan/Downloads/payactions/scanandpay/images/train'): imgpath = '/home/lixuan/Downloads/payactions/scanandpay/images/train/{}'.format(file) txtpath = '/h.

import cv2from PIL import Imageimport numpy as npimport osfor file in os.listdir('/home/lixuan/Downloads/payactions/scanandpay/images/train'): imgpath = '/home/lixuan/Downloads/payactions/scanandpay/images/train/{}'.format(file) txtpath = '/h.

import cv2import numpy as npimport osimport jsoncount = 0def seamless(src,dst,P,center): src_mask = np.zeros(src.shape, src.dtype) poly = np.array(P, np.int32) cv2.fillPoly(src_mask, [poly], (255, 255, 255)) output = cv2.seamlessClon.

from skimage import drawfrom skimage import ioimport numpy as npimport urllib.requestimport jsonimport loggingimport osimport sys################### INSTALLATION NOTE ############################################################################.

import cv2import numpy as npimport jsonP = []with open('test.json') as f: datas = f.readlines() for data in datas: data = data.strip() annotations = json.loads(data)['annotation'] annotation = annotations[0] po.

import cv2vs = cv2.VideoCapture('/home/lixuan/桌面/cailiao/scanandpay.mp4')fourcc = cv2.VideoWriter_fourcc(*'XVID')fps = 10size = (int(vs.get(cv2.CAP_PROP_FRAME_WIDTH)), int(vs.get(cv2.CAP_PROP_FRAME_HEIGHT)))out = cv2.VideoWriter('scanandpay.mp4', fo.

cv2.namedWindow('image',0)

"""根据大json写小json"""with open('pay.json') as f: datas = f.readlines() for data in datas: data = data.strip() j = json.loads(data) name = j['content'].split('/')[-1].replace('jpg','json') fw = open('drinks/{}'.form.

# import os# from PIL import Image# import random# import cv2# import numpy as np## shopdir = '/media/lixuan/Data/sku_data/sku110k/sku110k_yolov5_style/images/train'# handdir = '/home/lixuan/Arduino/hand_detection/hand'# bank = os.listdir(shopdir).

sudo apt-get install python-opengl

import cv2import randomimport oscount = 0for ind in range(3): for dir in os.listdir('/home/lixuan/桌面/back/coco/labels/train'): try: image = cv2.imread('/home/lixuan/桌面/back/coco/images/train/{}'.format(dir.replace('txt','jpg').

from PIL import Imageimport osfor dir in os.listdir('/home/lixuan/Arduino/car/images'): img = Image.open(os.path.join('/home/lixuan/Arduino/car/images',dir)) img.save(os.path.join('/home/lixuan/data/t/train/data/dataset/annotations',dir)) W.

from PIL import Imagecount = 0for dir in os.listdir('/home/lixuan/Arduino/outputs'): path = os.path.join('/home/lixuan/Arduino/outputs',dir) f = open(path) datas = json.load(f) outputs = datas['outputs'] path = datas['path'] if o.