def ellipse(landmarks):
face = np.array([m[0] for m in landmarks if 'contour_' in m[1]])
data = [face[:, 0], face[:, 1]]
lsqe = el.LSqEllipse()
lsqe.fit(data)
center, width, height, phi = lsqe.parameters()
return [center[0], center[1], width, height, phi]
def proc_ellipse(fm, roi):
"""Process an image stencil to an ellipse fitted dataset.
Flipping x,y axes for the image."""
try:
data = np.where(fm)
lsqe = el.LSqEllipse()
lsqe.fit(data)
center, width, height, phi = lsqe.parameters()
res = [center[1], center[0], width * 2, 2 * height, np.rad2deg(phi)]
return res
except Exception as e:
raise FitError(e)
def fit_ellipse(pts):
if pts.shape[0] >= 6: # circle
n = pts.shape[0]
data = [pts[0:n - 1, 0], pts[0:n - 1, 1]]
lsqe = ellipses.LSqEllipse()
lsqe.fit(data)
center, width, height, phi = lsqe.parameters()
ellpts = cv2.ellipse2Poly((int(center[0]), int(center[1])),
(int(width), int(height)),
int(phi / 3.14159 * 180), 0, 360, 5)
ellpts = np.append(ellpts, [[pts[n - 1, 0], pts[n - 1, 1]]], axis=0)
return ellpts
else: # rectangle
return pts
def ellipses(listasx,listasy):
#listasx = lista de los puntos en x
#listasy = lista de los puntos en y
data = np.ones(len(listasx)-2)
centros = np.ones((len(listasx)-2, 2))
anchos = np.ones(len(listasx)-2)
altos = np.ones(len(listasx)-2)
angulos = np.ones(len(listasx)-2)
for i in range(2,len(listasx)):
data = [listasx[i],listasy[i]]
lsqe = el.LSqEllipse()
lsqe.fit(data)
center, width, height, phi = lsqe.parameters()
centros[i-2] = np.asanyarray(center)
anchos[i-2] = width
altos[i-2] = height
angulos[i-2] = phi
allD = [data, centros, anchos, altos, angulos]
#retorna una lista de arreglos [(x,y),(xo.yo),[semi_mayor],[semi_menor],[ángulos]]
return allD
def EllipseFit_LS(xy):
lsqe = el.LSqEllipse()
lsqe.fit([xy[:, 0], xy[:, 1]])
if np.iscomplex(lsqe.width):
print "WARN: COMPLEX"
lsqe.width = np.nan
if np.iscomplex(lsqe.height):
print "WARN: COMPLEX"
lsqe.height = np.nan
if np.iscomplex(lsqe.phi):
print "WARN: COMPLEX"
lsqe.phi = np.nan
if np.iscomplex(lsqe.center).any():
print "WARN: COMPLEX"
lsqe.center = np.array([np.nan, np.nan])
# "width" and "height" are really half-width and half-height.
smaja = np.max((lsqe.width, lsqe.height))
smina = np.min((lsqe.width, lsqe.height))
angle = lsqe.phi # CCW angle b/t x-axis and "width"
if smaja == lsqe.height:
if angle <= 0:
angle += np.pi / 2.
else:
angle -= np.pi / 2.
angle *= 180 / np.pi
# Assuming N-S is direction straight to river
ls_width = 2 * (lsqe.width**2 * np.cos(np.pi / 2. - lsqe.phi)**2 +
lsqe.height**2 * np.sin(np.pi / 2. - lsqe.phi)**2)**.5
ls_length = 2 * (lsqe.width**2 * np.sin(np.pi / 2. - lsqe.phi)**2 +
lsqe.height**2 * np.cos(np.pi / 2. - lsqe.phi)**2)**.5
"""
if np.abs(angle) < np.pi/4.:
width = 2 * lsqe.height # semi-minor axis
else:
width = 2 * lsqe.width # semi-major axis
"""
return lsqe.center[0], lsqe.center[1], smaja, smina, angle, ls_width, \
ls_length
load_dict = json.load(load_f)
img = cv2.imread(os.path.join(root, load_dict["imagePath"]))
if img is None:
print('image file does not exist')
ih, iw, ic = img.shape
smallImg = cv2.resize(
img,
(int(img.shape[1] / show_scale), int(img.shape[0] / show_scale)))
objectNum = len(load_dict['shapes'])
for i, shape in enumerate(load_dict['shapes']):
pts = np.array(shape['points'])
if pts.shape[0] >= 6: # circle
n = pts.shape[0]
data = [pts[0:n - 1, 0], pts[0:n - 1, 1]]
lsqe = ellipses.LSqEllipse()
lsqe.fit(data)
center, width, height, phi = lsqe.parameters()
cv2.ellipse(smallImg, (int(center[0]), int(center[1])),
(int(width), int(height)), phi / 3.14159 * 180, 0,
360, (255, 0, 0), 2)
ellpts = cv2.ellipse2Poly((int(center[0]), int(center[1])),
(int(width), int(height)),
int(phi / 3.14159 * 180), 0, 360, 5)
ellpts = np.append(ellpts, [[pts[n - 1, 0], pts[n - 1, 1]]],
axis=0)
x2, y2 = np.max(ellpts, 0)
x1, y1 = np.min(ellpts, 0)
else: # rectangle
x2, y2 = np.max(pts, 0)
import ellipses as el
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
data = el.make_test_ellipse()
lsqe = el.LSqEllipse()
lsqe.fit(data)
center, width, height, phi = lsqe.parameters()
plt.close('all')
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
ax.axis('equal')
ax.plot(data[0], data[1], 'ro', label='test data', zorder=1)
ellipse = Ellipse(xy=center,
width=2 * width,
height=2 * height,
angle=np.rad2deg(phi),
edgecolor='b',
fc='None',
lw=2,
label='Fit',
zorder=2)
ax.add_patch(ellipse)
plt.legend()
plt.show()
def callback(self, data):
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print(e)
#ROI cordinates
y = 0
x = 275
h = 225
w = 225
crop_frame = cv_image[y:y + h, x:x + w]
yellow_pix = 0
#cv2.imshow('frame',crop_frame)
#find the object (yellow) (not needed for pipline)
# hsv_obj = cv2.cvtColor(crop_frame, cv2.COLOR_BGR2HSV)
# lower_yellow = np.array([20,100,100]) #cvScalar(20, 100, 100), cvScalar(30, 255, 255)
# upper_yellow = np.array([30,255,255])
# mask_obj = cv2.inRange(hsv_obj, lower_yellow, upper_yellow)
# res_obj = cv2.bitwise_and(crop_frame,crop_frame, mask= mask_obj)
#find the shirt (white)
hsv_shirt = cv2.cvtColor(crop_frame, cv2.COLOR_BGR2HSV)
lower_white = np.array([0, 0, 160])
upper_white = np.array([255, 25, 255])
lower_yellow = np.array(
[20, 100, 100]) #cvScalar(20, 100, 100), cvScalar(30, 255, 255)
upper_yellow = np.array([30, 255, 255])
mask_shirt = cv2.inRange(hsv_shirt, lower_white, upper_white)
kernel = np.ones((3, 3), np.uint8)
opening = cv2.morphologyEx(mask_shirt, cv2.MORPH_OPEN, kernel)
#morphological opening to get rid of outlyer pixels
res_shirt = cv2.bitwise_and(crop_frame, crop_frame, mask=opening)
# fitt pixles in leastsquare ellipse format
datax = []
datay = []
for x in range(np.array(opening).shape[0]):
for y in range(np.array(opening).shape[0]):
if (opening[x, y] == 255):
datax.append(x)
datay.append(y)
data = []
data.append(datax)
data.append(datay)
# debug
# cv2.imshow('frame',crop_frame)
# cv2.imshow('obj',res_obj)
# cv2.imshow('shirt',opening)
#do least square from: https://github.com/bdhammel/least-squares-ellipse-fitting
#try:
if (len(datax) > 0):
lsqe = el.LSqEllipse()
lsqe.fit(data)
center, width, height, phi = lsqe.parameters()
#add the ellipse to the crop_frame
cv2.ellipse(crop_frame, (int(center[1]), int(center[0])),
(int(height), int(width)), np.rad2deg(-phi), 0, 360,
255, 5)
#debug
#cv2.ellipse(opening,(int(center[1]),int(center[0])),(int(height),int(width)),np.rad2deg(-phi),0,360,255,5)
#get the insifde of ellipse mask
e_mask = np.ones((w, h)) * 0
e_mask = e_mask.astype('uint8')
cv2.ellipse(e_mask, (int(center[1]), int(center[0])),
(int(height), int(width)), np.rad2deg(-phi), 0, 360,
255, -1)
#filter
res_elipse = cv2.bitwise_and(crop_frame, crop_frame, mask=e_mask)
res_elipse_hsv_obj = cv2.cvtColor(res_elipse, cv2.COLOR_BGR2HSV)
mask_obj_ellipse = cv2.inRange(res_elipse_hsv_obj, lower_yellow,
upper_yellow)
#count yellow pixels inside ellipse
yellow_pix = cv2.countNonZero(mask_obj_ellipse)
#add information to vision screen
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(crop_frame, str(yellow_pix), (10, 50), font, 1,
(255, 255, 255), 2, cv2.LINE_AA)
else:
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(crop_frame, str(0), (10, 50), font, 1, (255, 255, 255),
2, cv2.LINE_AA)
# Display the resulting frame (debug mode)
#cv2.imshow('frame',crop_frame)
#cv2.imshow('obj',res_obj)
#cv2.imshow('shirt',opening)
#cv2.imshow('emask',res_elipse)
#cv2.imshow('mask_yellow_ellipse',mask_obj_ellipse)
#if cv2.waitKey(1) & 0xFF == ord('q'):
# break
#except:
# print("ERROR")
# (rows,cols,channels) = cv_image.shape
# if cols > 60 and rows > 60 :
# cv2.circle(cv_image, (50,50), 10, 255)
# cv2.imshow("Image window", cv_image)
# cv2.waitKey(3)
try:
self.image_pub.publish(
self.bridge.cv2_to_imgmsg(crop_frame, "bgr8"))
self.area_pub.publish(yellow_pix)
except CvBridgeError as e:
print(e)
