def testInvalidParameters():
with pytest.raises(SyntaxError):
ed.edge_detect("", "")
with pytest.raises(SyntaxError):
ed.edge_detect(fin, "")
with pytest.raises(SyntaxError):
ed.edge_detect("", fout)
def angle_detect(img):
up, down, left, right = edge_detect(img)
up = up[0]
down = down[0]
left = left[1]
right = right[1]
center = []
center.append(img.shape[0] / 2)
center.append(img.shape[1] / 2)
left1 = search_v_edge(center[1], 0, 0.8 * up + 0.2 * down, img)
left2 = search_v_edge(center[1], 0, 0.2 * up + 0.8 * down, img)
return math.atan((left1 - left2) / (0.6 * up - 0.6 * down))
def testNormalCase():
# fout=tempfile.TemporaryFile()
ed.edge_detect(fin, fout)
assert os.path.isfile(fout)
os.remove(fout)
with open('defects.txt','r') as f:
for line in f.readlines():
#find the coordinate of the defect
m=re.match(r'(.*?)(a.*?)_(\d+)_(\d+)',line)
#if it is the first time a defect appear on the picture, make a copy of the picture and rotate it, remove black
if not os.path.isfile(m.group(2)+'marked.bmp'):
shutil.copy(m.group(2)+'.bmp',m.group(2)+'marked.bmp')
orgin=cv2.imread(m.group(2)+'marked.bmp',0)
ang=angle_detect(orgin)
#rotate the picture so that the backlight panel is put straight
rows,cols=orgin.shape
M0 = cv2.getRotationMatrix2D((cols/2,rows/2),-ang*180/math.pi,1)
orgin = cv2.warpAffine(orgin,M0,(cols,rows))
#detect the border of the backlight panel in the rotated picture
up,down,left,right=edge_detect(orgin)
#select the region of backlight panel in the picture
orgin=orgin[int(round(up[0])):int(round(down[0])),int(round(left[1])):int(round(right[1]))]
orgin=cv2.cvtColor(orgin,cv2.COLOR_GRAY2BGR)
else:
orgin=cv2.imread(m.group(2)+'marked.bmp',1)
#get the defect part
if side*(int(m.group(3))+1)<=orgin.shape[0] && side*(int(m.group(4))+1)<=orgin.shape[1]:
defect=orgin[side*int(m.group(3))+1:side*(int(m.group(3))+1)-1,side*int(m.group(4))+1:side*(int(m.group(4))+1)-1]
elif side*int(m.group(3))<orgin.shape[0] && side*int(m.group(4))<orgin.shape[1]:
defect=orgin[orgin.shape[0]-side+1:orgin.shape[0]-1,orgin.shape[1]-side+1:orgin.shape[1]-1]
#add border to the defect part
defect=cv2.copyMakeBorder(defect,1,1,1,1,cv2.BORDER_CONSTANT,value=RED)
#put the defect part with border back into the picture
orgin[side*int(m.group(3)):side*(int(m.group(3))+1),side*int(m.group(4)):side*(int(m.group(4))+1)]=defect
from time import perf_counter
import edge_detect
import torch
import cv2
import math
#Create 10000x10000 grid (100m samples)
size = 10000
circle = edge_detect.edge_detect(size)
torch.set_printoptions(precision=10)
#Edge detection
t1 = perf_counter()
result = circle.calc_fine()
pi = circle.calc_pi()
t4 = perf_counter()
print("Pi =", pi)
print("Error:", math.pi - pi)
print("Total time for pi using edge detection:", t4 - t1)
#Calculating pi without edge detection
t5 = perf_counter()
pi = circle.calc_raw()
t6 = perf_counter()
print("Pi =", pi)
print("Error:", math.pi - pi)
print("Total time for pi without edge detection:", t6 - t5)
#Draw the detected edge
#edge_detect.draw(result)
# Open a copy of the depth image
# to change the contrast on the full-sized image
img2 = cv2.imread(depth_im, -1)
img2 = util.normalize_depth(img2)
img2 = util.clahe(img2, iter=2)
# crops the image
img2 = img2[mouse_Y[0]:mouse_Y[1], mouse_X[0]:mouse_X[1]]
P["img2"] = img2
# *********************************** SECTION 1 *****************************************
# FIND DEPTH / CURVATURE DISCONTINUITIES.
curve_disc, depth_disc, edgelist = edge_detect(P)
#CREATES LINE SEGMENTS
seglist = line_seg(edgelist, tol=5)
if context.ShowEdgeListWnd:
draw.draw_edge_list(seglist, P)
line_pairs = []
cntr_pairs = []
img_size = copy.deepcopy(P["img_size"])
height = img_size[0]
width = img_size[1]
blank_im = np.zeros((height, width, 3), np.uint8)
print("img size", img_size)
#find the coordinate of defects
image2 = 'b' + re.match(r'^a(\S*?).bmp', image).group(1) + '.png'
img2 = cv2.imread(image2, 0)
defect = imdiff(img, img2)
#find the angle of the backlight panel with respect to the border of the picture
ang = angle_detect(img)
#rotate the picture so that the backlight panel is put straight
rows, cols = img.shape
M0 = cv2.getRotationMatrix2D((cols / 2, rows / 2),
-ang * 180 / math.pi, 1)
img = cv2.warpAffine(img, M0, (cols, rows))
#detect the border of the backlight panel in the rotated picture
up, down, left, right = edge_detect(img)
#select the region of backlight panel in the picture
img = img[int(round(up[0])):int(round(down[0])),
int(round(left[1])):int(round(right[1]))]
#transformation matrix
tm = np.array([[
math.cos(ang),
math.sin(ang),
(1 - math.cos(ang)) * cols / 2 - math.sin(ang) * rows / 2
],
[
-math.sin(ang),
math.cos(ang),
math.sin(ang) * cols / 2 +
(1 - math.cos(ang)) * rows / 2
from os import listdir, getcwd
from os.path import join, isfile
import edge_detect as edge
import cv2
import datetime as dt
images_path = join(getcwd(), "../images")
images = [f for f in listdir(images_path) if isfile(join(images_path, f))]
for img_path in images:
img = cv2.imread(join(images_path, img_path), 0)
start_time = dt.datetime.now()
edges = edge.edge_detect(img)
end_time = dt.datetime.now()
time = (end_time - start_time).microseconds
print img_path + ":", time
for j in range(N):
x = 0
for i_prime in range(height / M):
for j_prime in range(width / N):
if d[i * height / M + i_prime, j * width / N + j_prime] != 0:
x += 1
if M * N * x / (height * width * 1.0) > T:
motion[i, j] = 255
return motion
# main loop
while cap.isOpened():
ret, frame = cap.read()
frame_filt = cv2.filter2D(frame, -1, kernel)
frame_edge = edge.edge_detect(frame_filt)
# cv2.imshow('normal', frame) # normal video
cv2.imshow("edge", frame_edge) # edge detection video)
motion = detect_motion(frame_edge, last_frame_edge)
cv2.imshow("motion", cv2.resize(motion, (640, 480))) # motion detection video
# cv2.imshow('difference', difference(frame_edge, last_frame_edge))
# update frames
last_frame_edge = frame_edge
# listen for exit
if cv2.waitKey(1) & 0xFF == ord("q"):
break
cap.release()
for i in range(M):
for j in range(N):
x = 0
for i_prime in range(height/M):
for j_prime in range(width/N):
if d[i*height/M+i_prime, j*width/N+j_prime] != 0:
x += 1
if M*N*x/(height*width*1.0) > T:
motion[i, j] = 255
return motion
# main loop
while cap.isOpened():
ret, frame = cap.read()
frame_filt = cv2.filter2D(frame, -1, kernel)
frame_edge = edge.edge_detect(frame_filt)
# cv2.imshow('normal', frame) # normal video
cv2.imshow('edge', frame_edge) # edge detection video)
motion = detect_motion(frame_edge, last_frame_edge)
cv2.imshow('motion', cv2.resize(motion, (640, 480))) # motion detection video
# cv2.imshow('difference', difference(frame_edge, last_frame_edge))
# update frames
last_frame_edge = frame_edge
# listen for exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
from os import listdir, getcwd
from os.path import join, isfile
import edge_detect as edge
import cv2
import datetime as dt
images_path = join(getcwd(), '../images')
images = [f for f in listdir(images_path) if isfile(join(images_path, f))]
for img_path in images:
img = cv2.imread(join(images_path, img_path), 0)
start_time = dt.datetime.now()
edges = edge.edge_detect(img)
end_time = dt.datetime.now()
time = (end_time - start_time).microseconds
print img_path + ":", time