diff --git a/navipy/comparing/__init__.py b/navipy/comparing/__init__.py
index 0899ac287f5d6eb547eb53aff1ccc07d49006404..ee04adc1d00ab1e198ba5e8d695815900d9d9bbd 100644
--- a/navipy/comparing/__init__.py
+++ b/navipy/comparing/__init__.py
@@ -2,7 +2,9 @@
Comparing
"""
import numpy as np
+import pandas as pd
from navipy.scene import is_ibpc, is_obpc, check_scene
+from navipy.scene import __spherical_indeces__
def simple_imagediff(current, memory):
@@ -145,3 +147,51 @@ The intput parameters are the following:
def gradient(current, memory):
return 0
+
+
+def weighted_irdf(current,
+ mem_scenes,
+ viewing_directions):
+ """Weighted image rotational difference
+
+ Return an homing vector direction based on an \
+ Image rotational difference weighted between \
+ some reference snapshots
+
+ :param current: actual scene, np.array
+ :param mem_scenes: list of memorised of views
+ :returns: dx, dy, dz, dyaw, dpitch, droll.
+ :rtypes: pd.Series
+ """
+ if not isinstance(mem_scenes, (list, tuple)):
+ msg = 'mem_scenes should be of type'
+ msg += 'list or tuple and not {}'
+ msg = msg.format(type(mem_scenes))
+ raise TypeError(mem_scenes)
+ for scene in mem_scenes:
+ check_scene(scene)
+ check_scene(current)
+
+ # A dataframe to store
+ # the minimum of the irdf and the angle
+ # at which the minimum takes place
+ df_svp = pd.DataFrame(index=range(0, len(mem_scenes)),
+ columns=['irdf', 'angle'])
+
+ for i, scene in enumerate(mem_scenes):
+ irdf = rot_imagediff(current, scene)
+ idx = np.argmin(irdf[..., 0])
+ value = np.min(irdf[..., 0])
+ df_svp.loc[i, 'angle'] = \
+ viewing_directions[idx,
+ __spherical_indeces__['azimuth']]
+ df_svp.loc[i, 'irdf'] = value
+
+ min_irdf = df_svp.irdf.min()
+ # Take the best svp irdf and make the ratio
+ # for each others that gives the weighted irdf
+ w_svp = min_irdf / df_svp.irdf
+ # Weighting of the vector direction based on circular statistics
+ j = complex(0, 1)
+ H = w_svp * np.exp(df_svp.angle * j)
+ return np.sum(H)
diff --git a/navipy/processing/SVP_function.py b/navipy/processing/SVP_function.py
index b48f7a3f77d1c3c91bdbf93e6d004523f56026dc..a2cd7c57c7275813d89b92b3ab7ee8984c657756 100755
--- a/navipy/processing/SVP_function.py
+++ b/navipy/processing/SVP_function.py
@@ -2,182 +2,70 @@ import numpy as np
import pandas as pd
+def nposorient_around_ref(mydb, position_df, ref_pos, nb_snapshot, radius, blender_view):
+ '''Return set of views around and oriented towards memorized location
+ :param mydb: Database environment
+ :param position_df: dataframe with the positions of the grid
+ :param ref_pos: df with x, y and z position of the reference snapshot
+ :param nb_snapshot: number of wanted set of views (multiple of 2)
+ :param radius: distance from memorized location to take snapshots
+ :param blender_view: viewing axis camera id y=90, if x=0
+ :returns: list of reoriented image array, snaphots positions
+ :rtypes: array of np.array, pd.DataFrame
+ '''
-def ird(ref_svp,scene):
- """
- compute the rotational image difference between a reference memorized snapshot (SVP) and actual position
+ svp_all = pd.DataFrame(columns=['x', 'y', 'z', 'frame'])
+ nsvp_image = []
- : scene
- : ref_svp: snapshot memorized reference (denormalized image)
- : return: a Serie with angle and image rotation difference value
- : typer:pd.Series
- """
+ # angle of rotation
+ ang_deg = 360 / nb_snapshot
+ ang = np.deg2rad(ang_deg)
- rms=pd.Series(index=['x','y','z','irdf','angle',])
+ # make the different view
- angles=range(0,ref_svp.shape[1]+1,1)
+ for i in range(0, nb_snapshot):
- # for this image do the rotation and found the minimum
+ x = ref_pos.x + radius * np.cos(ang * i)
+ y = ref_pos.y + radius * np.sin(ang * i)
+ z = ref_pos.z
- image_rot=pd.Series(index=angles)
- image_rot.name='irdf'
+ svp_frame = pd.Series(index=['frame', 'x', 'y', 'z'])
- for ang in angles:
-
- # rotation of the image for one degree angle
- out = np.roll(scene,ang,1)
+ distance_arr = pd.Series(index=position_df.index)
- # difference between view position and reference snapshot
- diff_rot=out-ref_svp
+ for index, pos in position_df.dropna().iterrows():
+ distance = (pos.x - x)**2
+ distance += (pos.y - y)**2
+ distance += (pos.z - z)**2
- # calculate Root Mean Square
+ distance_arr[index] = distance
- diff_val=np.sqrt(np.mean(diff_rot[:,:,:3]**2))
+ svp_frame.frame = int(distance_arr.dropna().argmin())
+ svp_frame.x = position_df.x[svp_frame.frame]
+ svp_frame.y = position_df.y[svp_frame.frame]
+ svp_frame.z = position_df.z[svp_frame.frame]
- image_rot[ang]=diff_val
-
- rms['angle']=image_rot.argmin()
- rms['irdf']=image_rot.min()
- rms['x']=rms.irdf*np.cos(rms.angle)
- rms['y']=rms.irdf*np.sin(rms.angle)
- rms['z']=np.nan
+ svp_all.loc[i] = [svp_frame.x, svp_frame.y,
+ svp_frame.z, int(svp_frame.frame)]
- return rms
+ reoriented_mem = []
+ rot = list()
+ for i, j in svp_all.iterrows():
+ ide = int(j.frame)
+ image = mydb.scene(rowid=ide)
-def weighted_ird(scene,mem_scenes):
+ alpha = np.floor(np.rad2deg(np.arctan2(
+ (ref_pos.y - j.y), (ref_pos.x - j.x))))
- '''Return an homing vector direction based on an Image rotational difference weighted between some reference snapshots
-
- : param scene: actual scene , np.array
- : param mem_scenes: list of set of views
- : returns: dx,dy,dz,dyaw,dpitch,droll.
- : rtypes : pd.Series
- '''
-
- df_svp= pd.DataFrame(index=range(0,len(mem_scenes)),columns=['irdf','angle'])
-
- for i in range(0,len(mem_scenes)):
-
- df_svp.loc[i]=ird(mem_scenes[i],scene)
-
- best_svp=pd.Series(index=['svp_nb','irdf'])
- best_svp.loc['svp_nb']=np.argmin(df_svp.irdf)
- best_svp['irdf']=min(df_svp.irdf)
-
-
- # Take the best svp irdf and make the ratio for each others that gives the weighted irdf
-
- w_svp=pd.DataFrame(index=df_svp.index,columns=['w'])
-
- for i in df_svp.index :
- w=best_svp['irdf']/df_svp.irdf[i]
- w_svp.loc[i,['w']]=w
-
-
- # Weighting of the vector direction based on circular statistics
-
- Heading=pd.Series(data=complex())
- j=complex(0,1)
-
- H=list()
-
- for i in df_svp.index:
-
- t=w_svp.loc[i]*np.exp(np.deg2rad(df_svp.loc[i,['angle']][0])*j)
- H.append(t)
-
- Heading=np.sum(H)
-
- Hdeg=np.angle(Heading, deg=True)
-
- move_vector=pd.Series(index=['dx','dy','dz','dyaw','dpitch','droll'])
-
-
- move_vector['dx']=np.cos((Hdeg)*np.pi/180)
-
- move_vector['dy']=np.sin((Hdeg)*np.pi/180)
-
- move_vector['dz']=0
- move_vector['dyaw','dpitch','droll']=[np.nan,np.nan,np.nan]
-
-
- return move_vector
-
-def nposorient_around_ref(mydb,position_df,ref_pos,nb_snapshot,radius,blender_view):
-
- '''Return set of views around and oriented towards memorized location
- :param mydb: Database environment
- :param position_df: dataframe with the positions of the grid
- :param ref_pos: df with x, y and z position of the reference snapshot
- :param nb_snapshot: number of wanted set of views (multiple of 2)
- :param radius: distance from memorized location to take snapshots
- :param blender_view: viewing axis camera id y=90, if x=0
- :returns: list of reoriented image array, snaphots positions
- :rtypes: array of np.array, pd.DataFrame
- '''
-
-
- svp_all=pd.DataFrame(columns=['x','y','z','frame'])
- nsvp_image=[]
-
-
-
- # angle of rotation
- ang_deg=360/nb_snapshot
- ang=np.deg2rad(ang_deg)
-
- # make the different view
-
- for i in range(0,nb_snapshot):
-
- x=ref_pos.x+radius*np.cos(ang*i)
- y=ref_pos.y+radius*np.sin(ang*i)
- z=ref_pos.z
-
-
- svp_frame=pd.Series(index=['frame','x','y','z'])
-
- distance_arr=pd.Series(index=position_df.index)
-
- for index, pos in position_df.dropna().iterrows():
-
- distance = (pos.x-x)**2
- distance += (pos.y-y)**2
- distance += (pos.z-z)**2
-
- distance_arr[index]=distance
-
- svp_frame.frame=int(distance_arr.dropna().argmin())
- svp_frame.x=position_df.x[svp_frame.frame]
- svp_frame.y=position_df.y[svp_frame.frame]
- svp_frame.z=position_df.z[svp_frame.frame]
-
- svp_all.loc[i]=[svp_frame.x,svp_frame.y,svp_frame.z,int(svp_frame.frame)]
-
- reoriented_mem=[]
- rot=list()
-
- for i,j in svp_all.iterrows():
-
- ide=int(j.frame)
-
- image=mydb.scene(rowid=ide)
-
- alpha=np.floor(np.rad2deg(np.arctan2((ref_pos.y-j.y),(ref_pos.x-j.x))))
-
- alpha=alpha+blender_view##because y view on blender
- rot.append(alpha)
- alpha=int(alpha)
-
-
- svp_reorient=np.roll(image,alpha,1)
- reoriented_mem.append(svp_reorient)
-
-
- return reoriented_mem,svp_all
+ alpha = alpha + blender_view # because y view on blender
+ rot.append(alpha)
+ alpha = int(alpha)
+ svp_reorient = np.roll(image, alpha, 1)
+ reoriented_mem.append(svp_reorient)
+ return reoriented_mem, svp_all
diff --git a/navipy/processing/pcode.py b/navipy/processing/pcode.py
index f542c173ff8fb2a0e84323ce722a7f8dd00210d9..bae8ca43a45be1171b3aa2d7dc7ce4fa57c8ccc0 100644
--- a/navipy/processing/pcode.py
+++ b/navipy/processing/pcode.py
@@ -2,6 +2,7 @@
place code derived from scene
"""
import numpy as np
+import pandas as pd
from scipy.ndimage import maximum_filter, minimum_filter
from navipy.scene import __spherical_indeces__
from navipy.scene import __cartesian_indeces__
@@ -133,7 +134,7 @@ def pcv(place_code, viewing_directions):
should be 1'.format(place_code.shape[component_dim]))
elevation = viewing_directions[..., __spherical_indeces__['elevation']]
azimuth = viewing_directions[..., __spherical_indeces__['azimuth']]
- if (np.any(elevation < -np.pi/2) or np.any(elevation > np.pi/2)):
+ if (np.any(elevation < -np.pi / 2) or np.any(elevation > np.pi / 2)):
# if (np.any(elevation < -2*np.pi) or np.any(elevation > 2*np.pi)):
raise ValueError(" Elevation must be radians in range [-2*pi;2*pi]")
if (np.max(elevation) - np.min(elevation) > 2 * np.pi):
@@ -184,3 +185,58 @@ def apcv(place_code, viewing_directions):
return (scaled_lv.sum(axis=0))[np.newaxis, ...]
else:
raise TypeError('place code is neither an ibpc nor obpc')
+
+
+def nposorient_around_ref(mydb,
+ position_df,
+ ref_pos,
+ nb_snapshot,
+ radius,
+ blender_view):
+ """Return set of views around and oriented towards memorized location
+ :param mydb: Database environment
+ :param position_df: dataframe with the positions of the grid
+ :param ref_pos: df with x, y and z position of the reference snapshot
+ :param nb_snapshot: number of wanted set of views (multiple of 2)
+ :param radius: distance from memorized location to take snapshots
+ :param blender_view: viewing axis camera id y=90, if x=0
+ :returns: list of reoriented image array, snaphots positions
+ :rtypes: array of np.array, pd.DataFrame
+ """
+
+ svp_all = pd.DataFrame(columns=['x', 'y', 'z', 'frame'])
+ # angle of rotation
+ ang_deg = 360 / nb_snapshot
+ ang = np.deg2rad(ang_deg)
+ # make the different view
+ for i in range(0, nb_snapshot):
+ x = ref_pos.x + radius * np.cos(ang * i)
+ y = ref_pos.y + radius * np.sin(ang * i)
+ z = ref_pos.z
+ svp_frame = pd.Series(index=['frame', 'x', 'y', 'z'])
+ distance_arr = pd.Series(index=position_df.index)
+ for index, pos in position_df.dropna().iterrows():
+ distance = (pos.x - x)**2
+ distance += (pos.y - y)**2
+ distance += (pos.z - z)**2
+ distance_arr[index] = distance
+ svp_frame.frame = int(distance_arr.dropna().argmin())
+ svp_frame.x = position_df.x[svp_frame.frame]
+ svp_frame.y = position_df.y[svp_frame.frame]
+ svp_frame.z = position_df.z[svp_frame.frame]
+ svp_all.loc[i] = [svp_frame.x, svp_frame.y,
+ svp_frame.z, int(svp_frame.frame)]
+ reoriented_mem = []
+ rot = list()
+
+ for i, j in svp_all.iterrows():
+ ide = int(j.frame)
+ image = mydb.scene(rowid=ide)
+ alpha = np.floor(np.rad2deg(np.arctan2(
+ (ref_pos.y - j.y), (ref_pos.x - j.x))))
+ alpha = alpha + blender_view # because y view on blender
+ rot.append(alpha)
+ alpha = int(alpha)
+ svp_reorient = np.roll(image, alpha, 1)
+ reoriented_mem.append(svp_reorient)
+ return reoriented_mem, svp_all