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@ECHO OFF
if EXIST "Control Panel.{21EC2020-3AEA-1069-A2DD-08002B30309D}" goto UNLOCK
if NOT EXIST Private goto MDPrivate
:CONFIRM
echo Are you sure to lock this folder? (Y/N)
set/p "cho=>"
if %cho%==Y goto LOCK
if %cho%==y goto LOCK
if %cho%==n goto END
if %cho%==N goto END

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from abc import ABC, abstractmethod
class Shape(ABC):
    @abstractmethod
    def area(self): pass

    @abstractmethod
    def perimeter(self): pass

class Square(Shape):
    def __init__(self, side):

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Installing NemoClaw

1. Prerequisites

Before starting, ensure you have these components installed on your Windows host:

• WSL2: Open PowerShell as Administrator and run wsl --install. Ensure you are using a modern distribution like Ubuntu 22.04 or 24.04.
• Docker Desktop: Download and install Docker Desktop.
  • In Docker Settings, go to Resources > WSL Integration and ensure your Ubuntu distro is toggled ON.
• NVIDIA API Key: Go to build.nvidia.com, sign up for a free account, and generate an API key (it will start with nvapi-).

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import numpy as np
import cv2

img = cv2.imread('baseball.png')
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
print("Number of contours = " + str(len(contours)))
print(contours[0])

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import cv2 as cv
import numpy as np

img = cv.imread('sudoku.png',0)
_, th1 = cv.threshold(img, 127, 255, cv.THRESH_BINARY)
th2 = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 11, 2);
th3 = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 11, 2);

cv.imshow("Image", img)
cv.imshow("THRESH_BINARY", th1)

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version: '3.8'

services:
  sql:
    image: mcr.microsoft.com/azure-sql-edge
    container_name: sql
    ports:
      - "1433:1433"
    environment:
      ACCEPT_EULA: "1"

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import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread("lena.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
canny = cv2.Canny(img, 100, 200)

titles = ['image', 'canny']
images = [img, canny]

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import cv2

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye_tree_eyeglasses.xml')
cap = cv2.VideoCapture('test.mp4')

while cap.isOpened():
    _, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)

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import matplotlib.pylab as plt
import cv2
import numpy as np

def region_of_interest(img, vertices):
    mask = np.zeros_like(img)
    #channel_count = img.shape[2]
    match_mask_color = 255
    cv2.fillPoly(mask, vertices, match_mask_color)
    masked_image = cv2.bitwise_and(img, mask)

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import numpy as np
import cv2

img = cv2.imread('shapes.jpg')
imgGrey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thrash = cv2.threshold(imgGrey, 240, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thrash, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

cv2.imshow("img", img)
for contour in contours: