guissalustiano

import requests
import re
from functools import cache

MOODLE_SESSION = ''
FILENAME = 'readme.md'


@cache
def file_content():

guissalustiano

######################################################
# Por: Guilherme S Salustiano                        #
# Eng da Computação 2020                             #
######################################################

using Measurements

temperatura_da_agua_graus = 30 ± 0.5
distancia_tubos_pilot_centros = 5.4e-3
distancia_tubos_pilot_parede_centro = distancia_tubos_pilot_centros/2

guissalustiano

import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np

img = cv.imread('passarinho.jpg',0)
sobel = cv.Sobel(img, -1, 1, 1)

fig, ax = plt.subplots(ncols=2,figsize=(15,5))
ax[0].imshow(img, cmap = 'gray')
ax[0].set_title('Original')

guissalustiano

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

def rgbToGray(img):
    # Baseado em https://pillow.readthedocs.io/en/3.2.x/reference/Image.html#PIL.Image.Image.convert
    return np.matmul(img, [0.2989, 0.5870, 0.1140]).astype(np.uint())

def convolution(img: np.ndarray, filt: np.ndarray):
    iw, ih = img.shape

guissalustiano

def gaussian_kernel(size, sigma=1):
  center = size//2
  x, y = np.mgrid[-center:center+1, -center:center+1]
  return np.exp(-((x**2 + y**2) / (2*sigma**2)))

guissalustiano

def convolution(img: np.ndarray, filt: np.ndarray):
    iw, ih = img.shape
    kw, kh = filt.shape
    assert(kw % 2 == 1 and kh % 2 == 1) # Garante que o filtro tenha um centro
    kcx, kcy = kw//2, kh//2 # Calcula a posição central
    out = np.zeros(img.shape)
    for x, y in np.ndindex(*out.shape):
         #Pega os pixels que serão usados na operação com o filtro
        sub_img = img[max(0, x-kcx): min(iw, x-kcx + (kw)),
                      max(0, y-kcy): min(ih, y-kcy + (kh))]