soura-b

soura-b

import cv2
import numpy as np
image = cv2.imread('images_09_03/Lot1_RT.jpeg', 1)
img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# apply adaptive gaussian thresholding
thresh = cv2.adaptiveThreshold(img_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)

cv2.imshow('primary', image)
cv2.imshow('gray', img_gray)

soura-b

import cv2
import numpy as np
# https://learnopencv.com/read-display-and-write-an-image-using-opencv/
# https://learnopencv.com/contour-detection-using-opencv-python-c/
image = cv2.imread('foot2.jpeg', 1) # 1 for color, 0 for greyscale
# stored as <class 'numpy.ndarray'>

img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# apply binary thresholding

soura-b

import cv2
image = cv2.imread('foot2.jpeg', 1) # 1 for color, 0 for greyscale
img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# ******** #
# Dilation #
# ******** #
from skimage.morphology import (erosion, dilation)
from skimage.morphology import disk
footprint = disk(1)

soura-b

# using socket
import socket
import time

HOST = 'data.pr4e.org'
PORT = 80
mysock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
mysock.connect((HOST, PORT))
mysock.sendall(b'GET http://data.pr4e.org/cover3.jpg HTTP/1.0\r\n\r\n')
count = 0