def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
python类PatchCollection()的实例源码
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def plot_hex(fig, centers, weights):
"""Plot an hexagonal grid based on the nodes positions and color the tiles
according to their weights.
Args:
fig (matplotlib figure object): the figure on which the hexagonal grid will be plotted.
centers (list, float): array containing couples of coordinates for each cell
to be plotted in the Hexagonal tiling space.
weights (list, float): array contaning informations on the weigths of each cell,
to be plotted as colors.
Returns:
ax (matplotlib axis object): the axis on which the hexagonal grid has been plotted.
"""
ax = fig.add_subplot(111, aspect='equal')
xpoints = [x[0] for x in centers]
ypoints = [x[1] for x in centers]
patches = []
if any(isinstance(el, list) for el in weights) and len(weights[0])==3:
for x,y,w in zip(xpoints,ypoints,weights):
hexagon = RegularPolygon((x,y), numVertices=6, radius=.95/np.sqrt(3) ,
orientation=np.radians(0),
facecolor=w)
ax.add_patch(hexagon)
else:
cmap = plt.get_cmap('viridis')
for x,y,w in zip(xpoints,ypoints,weights):
hexagon = RegularPolygon((x,y), numVertices=6, radius=.95/np.sqrt(3) ,
orientation=np.radians(0),
facecolor=cmap(w))
patches.append(hexagon)
p = PatchCollection(patches)
p.set_array(np.array(weights))
ax.add_collection(p)
ax.axis('off')
ax.autoscale_view()
return ax
def load(self):
"""loads shapefile onto graphical representation of data using basemap and fiona"""
shape = fiona.open("data/shapefiles/chicago.shp")
bounds = shape.bounds
extra = 0.01
lower_left = (bounds[0], bounds[1])
upper_right = (bounds[2], bounds[3])
coords = list(chain(lower_left, upper_right))
width, height = coords[2] - coords[0], coords[3] - coords[1]
self.base_map = Basemap(
projection="tmerc",
lon_0=-87.,
lat_0=41.,
ellps="WGS84",
llcrnrlon=coords[0] - extra * width,
llcrnrlat=coords[1] - extra + 0.01 * height,
urcrnrlon=coords[2] + extra * width,
urcrnrlat=coords[3] + extra + 0.01 * height,
lat_ts=0,
resolution='i',
suppress_ticks=True
)
self.base_map.readshapefile(
"data/shapefiles/chicago",
'chicago',
color='none',
zorder=2
)
self.data_map = pd.DataFrame({
'poly': [Polygon(xy) for xy in self.base_map.chicago],
'community_name': [ward['community'] for ward in self.base_map.chicago_info]})
self.data_map['area_m'] = self.data_map['poly'].map(lambda x: x.area)
self.data_map['area_km'] = self.data_map['area_m'] / 100000
self.data_map['patches'] = self.data_map['poly'].map(lambda x: PolygonPatch(x,
fc='#555555',
ec='#787878', lw=.25, alpha=.9,
zorder=4))
plt.close()
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111, axisbg='w', frame_on=False)
self.ax.add_collection(PatchCollection(self.data_map['patches'].values, match_original=True))
self.base_map.drawmapscale(
coords[0] + 0.08, coords[1] + 0.015,
coords[0], coords[1],
10.,
barstyle='fancy', labelstyle='simple',
fillcolor1='w', fillcolor2='#555555',
fontcolor='#555555',
zorder=5)
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in anns:
c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
if 'keypoints' in ann and type(ann['keypoints']) == list:
# turn skeleton into zero-based index
sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
kp = np.array(ann['keypoints'])
x = kp[0::3]
y = kp[1::3]
v = kp[2::3]
for sk in sks:
if np.all(v[sk]>0):
plt.plot(x[sk],y[sk], linewidth=3, color=c)
plt.plot(x[v==1], y[v==1],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
plt.plot(x[v==2], y[v==2],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
ax.add_collection(p)
p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def plot_map(m, coords, df_map, info, savefig=False):
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111, axisbg='w', frame_on=True)
# draw wards with grey outlines
norm = Normalize()
for i in xrange(5):
color = colormaps[i]
cmap = plt.get_cmap(color)
cond = (df_map['class'] == (i+1))
inx = df_map[cond].index
if cond.sum() > 0:
pc = PatchCollection(df_map[cond]['patches'],
match_original=True, alpha=0.75)
pc.set_facecolor(cmap(norm(df_map.loc[inx, 'cls_%d'%(i+1)].values)))
ax.add_collection(pc)
if (df_map['class'] == 0).sum() > 0:
pc = PatchCollection(df_map[df_map['class'] == 0]['patches'],
match_original=True, alpha=0.1
)
pc.set_facecolor('grey')
ax.add_collection(pc)
x, y = m(coords[0], coords[3]+0.006)
details = ax.annotate(info, xy=(x, y), size=20, color='k')
# Draw a map scale
m.drawmapscale(
coords[0]+0.02, coords[1]-0.004,
coords[0], coords[1],
2,
barstyle='fancy', labelstyle='simple',
fillcolor1='w', fillcolor2='#555555',
fontcolor='#555555', units='mi',
zorder=5)
legend_patches = []
for i in range(5):
legend_patches.append(mpatches.Patch(color='C%d' % i,
label=classes[i]))
ax.legend(handles=legend_patches, loc='upper right')
fig.set_size_inches(12, 12)
plt.tight_layout()
if savefig:
plt.savefig(savefig, dpi=200, alpha=True)
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0]:
datasetType = 'instances'
elif 'caption' in anns[0]:
datasetType = 'captions'
if datasetType == 'instances':
ax = plt.gca()
polygons = []
color = []
for ann in anns:
c = np.random.random((1, 3)).tolist()[0]
if type(ann['segmentation']) == list:
# polygon
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True,alpha=0.4))
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
else:
rle = [ann['segmentation']]
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
ax.add_collection(p)
elif datasetType == 'captions':
for ann in anns:
print ann['caption']
def cone(self, plunge, bearing, angle, segments=100, bidirectional=True,
**kwargs):
"""
Plot a polygon of a small circle (a.k.a. a cone) with an angular radius
of *angle* centered at a p/b of *plunge*, *bearing*. Additional keyword
arguments are passed on to the ``PathCollection``. (e.g. to have an
unfilled small small circle, pass "facecolor='none'".)
Parameters
----------
plunge : number or sequence of numbers
The plunge of the center of the cone in degrees.
bearing : number or sequence of numbers
The bearing of the center of the cone in degrees.
angle : number or sequence of numbers
The angular radius of the cone in degrees.
segments : int, optional
The number of vertices to use for the cone. Defaults to 100.
bidirectional : boolean, optional
Whether or not to draw two patches (the one given and its antipode)
for each measurement. Defaults to True.
**kwargs
Additional parameters are ``matplotlib.collections.PatchCollection``
properties.
Returns
-------
collection : ``matplotlib.collections.PathCollection``
Notes
-----
If *bidirectional* is ``True``, two circles will be plotted, even if
only one of each pair is visible. This is the default behavior.
"""
plunge, bearing, angle = np.atleast_1d(plunge, bearing, angle)
patches = []
lons, lats = stereonet_math.cone(plunge, bearing, angle, segments)
codes = mpath.Path.LINETO * np.ones(segments, dtype=np.uint8)
codes[0] = mpath.Path.MOVETO
if bidirectional:
p, b = -plunge, bearing + 180
alons, alats = stereonet_math.cone(p, b, angle, segments)
codes = np.hstack([codes, codes])
lons = np.hstack([lons, alons])
lats = np.hstack([lats, alats])
for lon, lat in zip(lons, lats):
xy = np.vstack([lon, lat]).T
path = mpath.Path(xy, codes)
patches.append(mpatches.PathPatch(path))
col = mcollections.PatchCollection(patches, **kwargs)
self.add_collection(col)
return col
