def main():
img = extension.load_data()
extension.visualize(img, 'gray')
detector = ced.cannyEdgeDetector(img, sigma=1.4, kernel_size=5, lowthreshold=0.09, highthreshold=0.17, weak_pixel=100)
final_img = detector.detect()
extension.visualize(final_img, 'gray')
def CannyEdgeDetection():
if request.method == 'POST':
data = request.get_json()
# print(data)
x = data["base64Image"].split(",")
base64_img = x[1]
size, imgs = utils.load_singleImage(base64_img)
detector = ced.cannyEdgeDetector(imgs, sigma=1.4, kernel_size=5, lowthreshold=0.09, highthreshold=0.17, weak_pixel=100)
imgs_final, base64_img_string = detector.detect(convertBase64=True)
return_dict = {
"base64Image": base64_img_string,
"size": size
}
print("OK")
# print(locations)
return(json.dumps(return_dict))
def canny(input):
edge_gray = rgb2gray(input)
detector = ced.cannyEdgeDetector(edge_gray, sigma=1.4, kernel_size=5, lowthreshold=0.09, highthreshold=0.17, weak_pixel=100)
return detector.detect()
# Script to generate doge-like ascii levels from images
# https://github.com/FienSoP/canny_edge_detector
from canny_edge_detector import cannyEdgeDetector
from utils.utils import load_data
from utils.utils import visualize
# https://github.com/RameshAditya/asciify
from asciify import do
from PIL import Image
import numpy as np
img = load_data("imgs")
ced = cannyEdgeDetector(img, sigma=1, kernel_size=5, weak_pixel=75, strong_pixel=255, lowthreshold=0.05, highthreshold=0.15)
imgs = ced.detect()
# visualize(imgs)
j = 1
for i in imgs:
f = open('level' + str(j) + '.txt','w')
f.write( do(Image.fromarray(i)) )
f.close()
j = j + 1
# convert from array to image
# img_obj = Image.fromarray(imgs[3])
# imgtxt = do(img_obj)
import cv2
import canny_edge_detector as ced
import numpy as np
cap = cv2.VideoCapture(0)
while True:
ret, img = cap.read()
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# blur = cv2.GaussianBlur(gray, (5, 5), 0)
# canny = cv2.Canny(blur, 10, 70)
# ret, mask = cv2.threshold(canny, 70, 255, cv2.THRESH_BINARY)
detector = ced.cannyEdgeDetector([img],
sigma=1.4,
kernel_size=5,
lowthreshold=0.09,
highthreshold=0.17,
weak_pixel=100,
useCVConvolution=True)
mask = detector.detect()
print(mask)
mask = np.array(mask, dtype=np.uint8)
cv2.imshow('Video feed', mask)
if cv2.waitKey(1) == 13:
break
cap.release()
cv2.destroyAllWindows()
import traceback
import cv2, sys, os
import numpy as np
from matplotlib import pyplot as plt
# Read image
img = cv2.imread('circles.png', cv2.IMREAD_GRAYSCALE)
import canny_edge_detector as ced
detector = ced.cannyEdgeDetector(img,
sigma=1.4,
kernel_size=5,
lowthreshold=0.09,
highthreshold=0.17,
weak_pixel=100)
img = detector.detect()
# cv2.imshow("canny",img)
# cv2.waitKey(0)
param1 = 80
param2 = 44
minRadius = 30
maxRadius = 50
radius = np.arange(minRadius, maxRadius, 1)
# fi_range = [0,45,90,135,180,225,270,315]
fi_range = range(0, 360, 10)
H = np.zeros(img.shape + (maxRadius, )) #3D matrix