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import numpy as np | |
import cv2 | |
import math | |
def grayscale(img): | |
"""Applies the Grayscale transform | |
This will return an image with only one color channel | |
but NOTE: to see the returned image as grayscale | |
you should call plt.imshow(gray, cmap='gray')""" |
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""" | |
Project Title : Lane Detection. | |
Developed By :- Vidit Shah, | |
Vivek Shah, | |
Jash Rele, | |
Deep Thakker | |
""" | |
import cv2 |
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""" | |
Mini Project: | |
Developed by: | |
Vidit Shah | |
Vivek Shah | |
Jash Rele | |
Deep Thakker | |
""" | |
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mport matplotlib.pyplot as plt | |
import matplotlib.image as mpimg | |
import numpy as np | |
# Read in the image | |
image = mpimg.imread('test.jpg') | |
# Grab the x and y sizes and make two copies of the image | |
# With one copy we'll extract only the pixels that meet our selection, | |
# then we'll paint those pixels red in the original image to see our selection |
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#!/usr/bin/env python3 | |
# Chapter 1 -- The ``__init__()`` Method | |
# ----------------------------------------- | |
# .. sectnum:: | |
# | |
# .. contents:: | |
# |
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class Car{ | |
park(){ | |
return "stopped"; | |
} | |
drive(){ | |
return "Vroom"; | |
} | |
} | |
let car ; |
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pragma solidity ^0.4.0; | |
contract Ballot { | |
struct Voter { | |
uint weight; | |
bool voted; | |
uint8 vote; | |
} | |
struct Proposal{ | |
uint voteCount; |
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pragma solidity ^0.4.17; | |
contract Auction { | |
// Data | |
//Structure to hold details of the item | |
struct Item { | |
uint itemId; // id of the item | |
uint[] itemTokens; //tokens bid in favor of the item | |
} |
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#include <algorithm> // for sort | |
#include <fstream> | |
#include <iostream> | |
#include <sstream> | |
#include <string> | |
#include <vector> | |
using namespace std; | |
enum class State {kEmpty, kObstacle, kClosed, kPath, kStart, kFinish}; |
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from sklearn.model_selection import StratifiedKFold | |
from sklearn.base import clone | |
skfolds = StratifiedKFold(n_splits =3,random_state=42) | |
for train_index,test_index in skfolds.split(X_train,y_train_5): | |
clone_clf = clone(sgd_clf) | |
X_train_folds = X_train[train_index] | |
y_train_folds = y_train_5[train_index] | |
X_test_fold = X_train[test_index] |
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