def make_mark(self, x, y):
wins = self.para['win']
rect = {
'type': 'rectangle',
'body': (x, y, wins * 2, wins * 2),
'color': self.para['color']
}
mark = {'type': 'layer', 'body': [rect]}
r = 2 if self.status == 'local_brush' else self.para['r'] / 2
mark['body'].append({
'type': 'circle',
'body': (x, y, r),
'color': self.para['color']
})
mark['body'].append({
'type':
'text',
'body': (x - wins, y - wins,
'S:%s W:%s' % (self.para['ms'], self.para['win'])),
'pt':
False,
'color':
self.para['color']
})
return GeometryMark(mark)
def run(self, ips, imgs, para=None):
sc, sr = ips.get_rect()
print(sc, sr)
c, r, w, h = sr.start, sc.start, sr.stop - sr.start, sc.stop - sc.start
roi = imgs[0][r:r + h, c:c + w].copy()
track_window = (c, r, w, h)
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
n = len(imgs)
locs, mark = [], {'type': 'layers', 'body': {}}
for i in range(n):
prgs = (i, n)
frame = imgs[i].copy()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
ret, track_window = cv2.meanShift(dst, track_window, term_crit)
x, y, w, h = track_window
img2 = cv2.rectangle(frame, (x, y), (x + w, y + h), 255, 2)
locs.append([x, y, w, h])
layer = {'type': 'layer', 'body': []}
layer['body'].append({
'type': 'rectangle',
'body': (x + w / 2, y + h / 2, w, h)
})
mark['body'][i] = layer
ips.mark = GeometryMark(mark)
IPy.show_table(pd.DataFrame(locs, columns=['X', 'Y', 'W', 'H']),
ips.title + '-region')
def run(self, ips, imgs, para = None):
inten = ImageManager.get(para['inten'])
if not para['slice']:
imgs = [inten.img]
msks = [ips.img]
else:
msks = ips.imgs
imgs = inten.imgs
if len(msks)==1:
msks *= len(imgs)
buf = imgs[0].astype(np.uint16)
strc = ndimage.generate_binary_structure(2, 1 if para['con']=='4-connect' else 2)
idct = ['Max','Min','Mean','Variance','Standard','Sum']
key = {'Max':'max','Min':'min','Mean':'mean',
'Variance':'var','Standard':'std','Sum':'sum'}
idct = [i for i in idct if para[key[i]]]
titles = ['Slice', 'ID'][0 if para['slice'] else 1:]
if para['center']: titles.extend(['Center-X','Center-Y'])
if para['extent']: titles.extend(['Min-Y','Min-X','Max-Y','Max-X'])
titles.extend(idct)
k = ips.unit[0]
data, mark = [],{'type':'layers', 'body':{}}
# data,mark=[],[]
for i in range(len(imgs)):
n = ndimage.label(msks[i], strc, output=buf)
index = range(1, n+1)
dt = []
if para['slice']:dt.append([i]*n)
dt.append(range(n))
xy = ndimage.center_of_mass(imgs[i], buf, index)
xy = np.array(xy).round(2).T
if para['center']:dt.extend([xy[1]*k, xy[0]*k])
boxs = [None] * n
if para['extent']:
boxs = ndimage.find_objects(buf)
boxs = [( i[1].start+(i[1].stop-i[1].start)/2, i[0].start+(i[0].stop-i[0].start)/2, i[1].stop-i[1].start,i[0].stop-i[0].start) for i in boxs]
for j in (0,1,2,3):
dt.append([i[j]*k for i in boxs])
if para['max']:dt.append(ndimage.maximum(imgs[i], buf, index).round(2))
if para['min']:dt.append(ndimage.minimum(imgs[i], buf, index).round(2))
if para['mean']:dt.append(ndimage.mean(imgs[i], buf, index).round(2))
if para['var']:dt.append(ndimage.variance(imgs[i], buf, index).round(2))
if para['std']:dt.append(ndimage.standard_deviation(imgs[i], buf, index).round(2))
if para['sum']:dt.append(ndimage.sum(imgs[i], buf, index).round(2))
layer = {'type':'layer', 'body':[]}
xy=np.int0(xy).T
texts = [(i[1],i[0])+('id=%d'%n,) for i,n in zip(xy,range(len(xy)))]
layer['body'].append({'type':'texts', 'body':texts})
if para['extent']: layer['body'].append({'type':'rectangles', 'body':boxs})
mark['body'][i] = layer
data.extend(list(zip(*dt)))
IPy.show_table(pd.DataFrame(data, columns=titles), inten.title+'-region statistic')
inten.mark = GeometryMark(mark)
inten.update = True
def run(self, ips, imgs, para=None):
pts = (np.random.rand(200) * 512).reshape((100, 2))
ips.mark = GeometryMark({
'type': 'points',
'color': (255, 0, 0),
'lw': 1,
'body': pts
})
def run(self, ips, imgs, para=None):
f = open(para['path'])
geo = json.load(f)
f.close()
if geo['type'] == 'layers':
body = geo['body']
for i in list(body.keys()):
body[int(i)] = body.pop(i)
ips.mark = GeometryMark(geo)
def run(self, ips, imgs, para=None):
sc, sr = ips.get_rect()
print(sc, sr)
c, r, w, h = sr.start, sc.start, sr.stop - sr.start, sc.stop - sc.start
roi = imgs[0][r:r + h, c:c + w].copy()
track_window = (c, r, w, h)
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
n = len(imgs)
locs = []
locs, mark = [], {'type': 'layers', 'body': {}}
for i in range(n):
prgs = (i, n)
frame = imgs[i].copy()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
ret, track_window = cv2.CamShift(dst, track_window, term_crit)
pts = cv2.boxPoints(ret)
pts = np.int0(pts)
layer = {'type': 'layer', 'body': []}
temp = [tuple(i) for i in pts]
layer['body'].append({
'type':
'line',
'body': [(pts[0][0], pts[0][1]), (pts[1][0], pts[1][1]),
(pts[2][0], pts[2][1]), (pts[3][0], pts[3][1])]
})
layer['body'].append({
'type':
'line',
'body': [(pts[0][0], pts[0][1]), (pts[3][0], pts[3][1])]
})
mark['body'][i] = layer
locs.append([
pts[0][0], pts[0][1], pts[1][0], pts[1][1], pts[2][0],
pts[2][1], pts[3][0], pts[3][1]
])
ips.mark = GeometryMark(mark)
IPy.show_table(
pd.DataFrame(locs,
columns=[
'P1_X',
'P1_y',
'P2_X',
'P2_y',
'P3_X',
'P3_y',
'P4_X',
'P4_y',
]), ips.title + '-region')
def preview(self, ips, para):
grayimg = ips.img if ips.img.ndim == 2 else ips.img.mean(axis=-1)
grayimg /= grayimg.max()
pts = blob_doh(grayimg,
min_sigma=para['min_sigma'],
max_sigma=para['max_sigma'],
num_sigma=para['num_sigma'],
threshold=para['threshold'],
overlap=para['overlap'],
log_scale=para['log_scale'])
ips.mark = GeometryMark({'type': 'circles', 'body': pts[:, [1, 0, 2]]})
def run(self, ips, imgs, para=None):
marks = {'type': 'layers', 'body': {}}
for i, img in enumerate(imgs):
tmp = img.copy().astype('float32') / 255.0
out = self.net(tmp[None, None, :, :])
pred = np.argmax(out, -1)
marks['body'][i] = {
'type': 'text',
'color': (255, 0, 0),
'body': (2, -2, str(pred))
}
ips.mark = GeometryMark(marks)
def preview(self, ips, para):
grayimg = ips.img if ips.img.ndim == 2 else ips.img.mean(axis=-1)
grayimg /= grayimg.max()
pts = blob_dog(grayimg,
min_sigma=para['min_sigma'],
max_sigma=para['max_sigma'],
sigma_ratio=para['sigma_ratio'],
threshold=para['threshold'],
overlap=para['overlap'],
exclude_border=para['exclude_border'])
pts[:, 2] *= np.sqrt(2)
ips.mark = GeometryMark({'type': 'circles', 'body': pts[:, [1, 0, 2]]})
def draw(self,ips):
mark = {'type':'layers', 'body':{}}
layer = {'type':'layer', 'body':[]}
layer['body'].append({'type':'points', 'body':self.buf})
if len(self.buf)>1:
lines=route_through(ips, ips.img,[self.buf],self.para)
lst=[]
for line in lines:lst.append([(j,i) for i,j in line])
layer['body'].append({'type':'lines', 'body':lst})
mark['body'][0] = layer
ips.mark = GeometryMark(mark)
ips.update()
def update(self, ips):
ips.mark = GeometryMark({
'type':
'layer',
'body': [{
'type': 'points',
'body': [(i[0], i[1]) for i in ips.data['adjc']]
}, {
'type': 'circles',
'body': [(i[0], i[1], i[2]) for i in ips.data['adjc']]
}, {
'type':
'texts',
'body':
[(i[0], i[1], 'z=%.1f' % i[3]) for i in ips.data['adjc']]
}]
})
ips.update()
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
data, sid, fid, mark = [], [], [], {'type': 'layers', 'body': {}}
for i in range(len(imgs)):
grayimg = imgs[i] if imgs[i].ndim == 2 else imgs[i].mean(axis=-1)
grayimg /= grayimg.max()
pts = blob_log(grayimg,
min_sigma=para['min_sigma'],
max_sigma=para['max_sigma'],
num_sigma=para['num_sigma'],
threshold=para['threshold'],
overlap=para['overlap'],
log_scale=para['log_scale'],
exclude_border=para['exclude_border'])
pts[:, 2] *= np.sqrt(2)
sid.extend([i] * len(pts))
fid.extend(range(1, len(pts) + 1))
data.append(pts)
layer = {
'type': 'layer',
'body': [{
'type': 'circles',
'body': pts[:, [1, 0, 2]]
}]
}
if para['showid']:
layer['body'].append({
'type':
'texts',
'body': [(x, y, 'id=%d' % i)
for (x, y), i in zip(pts[:, 1::-1], fid)]
})
mark['body'][i] = layer
ips.mark = GeometryMark(mark)
df = pd.DataFrame(np.vstack(data) * ips.unit[0],
columns=['X', 'Y', 'R'])
df.insert(0, 'FID', fid)
if para['slice']: df.insert(o, 'SliceID', sid)
IPy.show_table(df, ips.title + '-dohblob')
def run(self, ips, snap, img, para=None):
# 人脸检测
# 获取当前工作路径
path = os.getcwd()
face_cascade = cv2.CascadeClassifier(
path + '\menus\Opencv\Filters\haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
ips.mark = GeometryMark({
'type': 'rectangles',
'color': (255, 0, 0),
'lw': 2,
'fcolor': (0, 0, 0),
'fill': False,
'body': [(x, y, w, h)]
})
# 用下面这句会影响到原图像，导致不能再编辑，可以用上面的标记
# cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
IPy.show_table(
pd.DataFrame([[x, y, w, h]],
index=['矩形'],
columns=['x', 'y', 'w', 'h']), '人脸参数')
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
k = ips.unit[0]
titles = ['Slice', 'ID'][0 if para['slice'] else 1:] + [
'Center-X', 'Center-Y', 'N', 'Neighbors'
]
buf = imgs[0].astype(np.uint32)
data, mark = [], {'type': 'layers', 'body': {}}
for i in range(len(imgs)):
if para['labled']: buf = imgs[i]
else: label(imgs[i], generate_binary_structure(2, 1), output=buf)
conarr = connect(buf, 1 if para['con'] == '4-connect' else 2,
not self.para['nozero'])
conmap = mapidx(conarr)
ls = regionprops(buf)
dt = [[i] * len(ls), list(range(1, 1 + len(ls)))]
if not para['slice']: dt = dt[1:]
layer = {'type': 'layer', 'body': []}
texts = [(i.centroid[::-1]) + ('id=%d' % i.label, ) for i in ls]
lines = [(ls[i - 1].centroid[::-1], ls[j - 1].centroid[::-1])
for i, j in conarr]
layer['body'].append({'type': 'texts', 'body': texts})
layer['body'].append({'type': 'lines', 'body': lines})
mark['body'][i] = layer
dt.append([round(i.centroid[1] * k, 1) for i in ls])
dt.append([round(i.centroid[0] * k, 1) for i in ls])
neibs = [conmap[i.label] if i.label in conmap else [] for i in ls]
dt.extend([[len(i) for i in neibs], neibs])
data.extend(list(zip(*dt)))
ips.mark = GeometryMark(mark)
IPy.show_table(pd.DataFrame(data, columns=titles),
ips.title + '-region')
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
k = ips.unit[0]
titles = ['Slice', 'ID'][0 if para['slice'] else 1:]
if para['center']: titles.extend(['Center-X', 'Center-Y'])
if para['area']: titles.append('Area')
if para['l']: titles.append('Perimeter')
if para['extent']: titles.extend(['Min-Y', 'Min-X', 'Max-Y', 'Max-X'])
if para['ed']: titles.extend(['Diameter'])
if para['ca']: titles.extend(['ConvexArea'])
if para['holes']: titles.extend(['Holes'])
if para['fa']: titles.extend(['FilledArea'])
if para['solid']: titles.extend(['Solidity'])
if para['cov']: titles.extend(['Major', 'Minor', 'Ori'])
buf = imgs[0].astype(np.uint16)
data, mark = [], {'type': 'layers', 'body': {}}
strc = generate_binary_structure(
2, 1 if para['con'] == '4-connect' else 2)
for i in range(len(imgs)):
label(imgs[i], strc, output=buf)
ls = regionprops(buf)
dt = [[i] * len(ls), list(range(len(ls)))]
if not para['slice']: dt = dt[1:]
layer = {'type': 'layer', 'body': []}
texts = [(i.centroid[::-1]) + ('id=%d' % n, )
for i, n in zip(ls, range(len(ls)))]
layer['body'].append({'type': 'texts', 'body': texts})
if para['cov']:
ellips = [
i.centroid[::-1] + (i.major_axis_length / 2,
i.minor_axis_length / 2, i.orientation)
for i in ls
]
layer['body'].append({'type': 'ellipses', 'body': ellips})
mark['body'][i] = layer
if para['center']:
dt.append([round(i.centroid[1] * k, 1) for i in ls])
dt.append([round(i.centroid[0] * k, 1) for i in ls])
if para['area']:
dt.append([i.area * k**2 for i in ls])
if para['l']:
dt.append([round(i.perimeter * k, 1) for i in ls])
if para['extent']:
for j in (0, 1, 2, 3):
dt.append([i.bbox[j] * k for i in ls])
if para['ed']:
dt.append([round(i.equivalent_diameter * k, 1) for i in ls])
if para['ca']:
dt.append([i.convex_area * k**2 for i in ls])
if para['holes']:
dt.append([1 - i.euler_number for i in ls])
if para['fa']:
dt.append([i.filled_area * k**2 for i in ls])
if para['solid']:
dt.append([round(i.solidity, 2) for i in ls])
if para['cov']:
dt.append([round(i.major_axis_length * k, 1) for i in ls])
dt.append([round(i.minor_axis_length * k, 1) for i in ls])
dt.append([round(i.orientation * k, 1) for i in ls])
data.extend(list(zip(*dt)))
ips.mark = GeometryMark(mark)
IPy.show_table(pd.DataFrame(data, columns=titles),
ips.title + '-region')
def preview(self, ips, para):
grid, lines, row, col = self.grid(ips, para)
lines = lines.reshape((-1, 2, 2))
polygons = {'type': 'lines', 'body': lines}
ips.mark = GeometryMark(polygons)
ips.update()
