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weird error where all : and , deleted

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David PAGNON 2023-12-05 12:07:26 +01:00 committed by GitHub
parent 418f39143f
commit d3c5794506
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1 changed files with 32 additions and 32 deletions
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Pose2Sim/Utilities/synchronize_cams_draft.py
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@ -54,14 +54,14 @@ def convert_json2csv(json_dir):
json_files_names = fnmatch.filter(os.listdir(os.path.join(json_dir)), '.json') json_files_names = fnmatch.filter(os.listdir(os.path.join(json_dir)), '.json')
json_files_path = [os.path.join(json_dir, j_f) for j_f in json_files_names] json_files_path = [os.path.join(json_dir, j_f) for j_f in json_files_names]
json_coords = [] json_coords = []
for i, j_p in enumerate(json_files_path) for i, j_p in enumerate(json_files_path):
# if i in range(frames) # if i in range(frames)
with open(j_p) as j_f with open(j_p) as j_f:
try try:
json_data = json.load(j_f)['people'][0]['pose_keypoints_2d'] json_data = json.load(j_f)['people'][0]['pose_keypoints_2d']
except except:
print(f'No person found in {os.path.basename(json_dir)}, frame {i}') print(f'No person found in {os.path.basename(json_dir)}, frame {i}')
json_data = [0]75 json_data = [0]*75
json_coords.append(json_data) json_coords.append(json_data)
df_json_coords = pd.DataFrame(json_coords) df_json_coords = pd.DataFrame(json_coords)
return df_json_coords return df_json_coords
@ -73,17 +73,17 @@ def drop_col(df,col_nb):
return df_dropped return df_dropped
def speed_vert(df, axis='y'): def speed_vert(df, axis='y'):
axis_dict = {'x'0, 'y'1, 'z'2} axis_dict = {'x':0, 'y':1, 'z':2}
df_diff = df.diff() df_diff = df.diff()
df_diff = df_diff.fillna(df_diff.iloc[1]2) df_diff = df_diff.fillna(df_diff.iloc[1]*2)
df_vert_speed = pd.DataFrame([df_diff.loc[, 2k + axis_dict[axis]] for k in range(int(df_diff.shape[1]2))]).T df_vert_speed = pd.DataFrame([df_diff.loc[:, 2*k + axis_dict[axis]] for k in range(int(df_diff.shape[1]*2))]).T
df_vert_speed.columns = np.arange(len(df_vert_speed.columns)) df_vert_speed.columns = np.arange(len(df_vert_speed.columns))
return df_vert_speed return df_vert_speed
def speed_2D(df): def speed_2D(df):
df_diff = df.diff() df_diff = df.diff()
df_diff = df_diff.fillna(df_diff.iloc[1]2) df_diff = df_diff.fillna(df_diff.iloc[1]*2)
df_2Dspeed = pd.DataFrame([np.sqrt(df_diff.loc[,2k]2 + df_diff.loc[,2k+1]2) for k in range(int(df_diff.shape[1]2))]).T df_2Dspeed = pd.DataFrame([np.sqrt(df_diff.loc[:,2*k]*2 + df_diff.loc[:,2*k+1]*2) for k in range(int(df_diff.shape[1]*2))]).T
return df_2Dspeed return df_2Dspeed
def interpolate_nans(col, kind): def interpolate_nans(col, kind):
@ -100,12 +100,12 @@ def interpolate_nans(col, kind):
idx = col.index idx = col.index
idx_good = np.where(np.isfinite(col))[0] #index of non zeros idx_good = np.where(np.isfinite(col))[0] #index of non zeros
if len(idx_good) = 10 return col if len(idx_good) == 10: return col
# idx_notgood = np.delete(np.arange(len(col)), idx_good) # idx_notgood = np.delete(np.arange(len(col)), idx_good)
if not kind # 'linear', 'slinear', 'quadratic', 'cubic' if not kind: # 'linear', 'slinear', 'quadratic', 'cubic'
f_interp = interpolate.interp1d(idx_good, col[idx_good], kind=cubic, bounds_error=False) f_interp = interpolate.interp1d(idx_good, col[idx_good], kind=cubic, bounds_error=False)
else else:
f_interp = interpolate.interp1d(idx_good, col[idx_good], kind=kind[0], bounds_error=False) f_interp = interpolate.interp1d(idx_good, col[idx_good], kind=kind[0], bounds_error=False)
col_interp = np.where(np.isfinite(col), col, f_interp(idx)) #replace nans with interpolated values col_interp = np.where(np.isfinite(col), col, f_interp(idx)) #replace nans with interpolated values
col_interp = np.where(np.isfinite(col_interp), col_interp, np.nanmean(col_interp)) #replace remaining nans col_interp = np.where(np.isfinite(col_interp), col_interp, np.nanmean(col_interp)) #replace remaining nans
@ -113,12 +113,12 @@ def interpolate_nans(col, kind):
return col_interp #, idx_notgood return col_interp #, idx_notgood
def plot_time_lagged_cross_corr(camx, camy, ax): def plot_time_lagged_cross_corr(camx, camy, ax):
pearson_r = [camx.corr(camy.shift(lag)) for lag in range(-2fps, 2fps)] # lag -2 sec à +2 sec pearson_r = [camx.corr(camy.shift(lag)) for lag in range(-2*fps, 2*fps)] # lag -2 sec à +2 sec
offset = int(np.floor(len(pearson_r)2)-np.argmax(pearson_r)) offset = int(np.floor(len(pearson_r)*2)-np.argmax(pearson_r))
max_corr = np.max(pearson_r) max_corr = np.max(pearson_r)
ax.plot(list(range(-2fps, 2fps)), pearson_r) ax.plot(list(range(-2*fps, 2*fps)), pearson_r)
ax.axvline(np.ceil(len(pearson_r)2)-2fps,color='k',linestyle='--') ax.axvline(np.ceil(len(pearson_r)*2)-2*fps,color='k',linestyle='--')
ax.axvline(np.argmax(pearson_r)-2fps,color='r',linestyle='--',label='Peak synchrony') ax.axvline(np.argmax(pearson_r)-2*fps,color='r',linestyle='--',label='Peak synchrony')
plt.annotate(f'Max correlation={np.round(max_corr,2)}', xy=(0.05, 0.9), xycoords='axes fraction') plt.annotate(f'Max correlation={np.round(max_corr,2)}', xy=(0.05, 0.9), xycoords='axes fraction')
ax.set(title=f'Offset = {offset} frames', xlabel='Offset (frames)',ylabel='Pearson r') ax.set(title=f'Offset = {offset} frames', xlabel='Offset (frames)',ylabel='Pearson r')
plt.legend() plt.legend()
@ -136,16 +136,16 @@ json_dirs_names = [k for k in pose_listdirs_names if 'json' in k]
json_dirs = [os.path.join(pose_dir, j_d) for j_d in json_dirs_names] json_dirs = [os.path.join(pose_dir, j_d) for j_d in json_dirs_names]
df_coords = [] df_coords = []
for i, json_dir in enumerate(json_dirs) for i, json_dir in enumerate(json_dirs):
df_coords.append(convert_json2csv(json_dir)) df_coords.append(convert_json2csv(json_dir))
df_coords[i] = drop_col(df_coords[i],3) # drop likelihood df_coords[i] = drop_col(df_coords[i],3) # drop likelihood
b, a = signal.butter(42, cut_off_frequency(fps2), 'low', analog = False) b, a = signal.butter(42, cut_off_frequency(fps2), 'low', analog = False)
for i in range(len(json_dirs)) for i in range(len(json_dirs)):
df_coords[i] = pd.DataFrame(signal.filtfilt(b, a, df_coords[i], axis=0)) # filter df_coords[i] = pd.DataFrame(signal.filtfilt(b, a, df_coords[i], axis=0)) # filter
## Pour sauvegarder et réouvrir au besoin ## Pour sauvegarder et réouvrir au besoin
with open(os.path.join(pose_dir, 'coords'), 'wb') as fp with open(os.path.join(pose_dir, 'coords'), 'wb') as fp:
pk.dump(df_coords, fp) pk.dump(df_coords, fp)
# with open(os.path.join(pose_dir, 'coords'), 'rb') as fp # with open(os.path.join(pose_dir, 'coords'), 'rb') as fp
# df_coords = pk.load(fp) # df_coords = pk.load(fp)
@ -159,12 +159,12 @@ with open(os.path.join(pose_dir, 'coords'), 'wb') as fp
# Vitesse verticale # Vitesse verticale
df_speed = [] df_speed = []
for i in range(len(json_dirs)) for i in range(len(json_dirs)):
if speed_kind == 'y' if speed_kind == 'y':
df_speed.append(speed_vert(df_coords[i])) df_speed.append(speed_vert(df_coords[i]))
elif speed_kind == '2D' elif speed_kind == '2D':
df_speed.append(speed_2D(df_coords[i])) df_speed.append(speed_2D(df_coords[i]))
df_speed[i] = df_speed[i].where(df_speed[i]vmax, other=np.nan) df_speed[i] = df_speed[i].where(df_speed[i]*vmax, other=np.nan)
df_speed[i] = df_speed[i].apply(interpolate_nans, axis=0, args = ['cubic']) df_speed[i] = df_speed[i].apply(interpolate_nans, axis=0, args = ['cubic'])
@ -182,20 +182,20 @@ for i in range(len(json_dirs))
id_kpt_dict = {} id_kpt_dict = {}
if len(id_kpt)==1 and id_kpt != ['all'] if len(id_kpt)==1 and id_kpt != ['all']:
camx = df_speed[cam1_nb-1].loc[range(np.array(frames)),id_kpt[0]] camx = df_speed[cam1_nb-1].loc[range(np.array(frames)),id_kpt[0]]
camy = df_speed[cam2_nb-1].loc[range(np.array(frames)),id_kpt[0]] camy = df_speed[cam2_nb-1].loc[range(np.array(frames)),id_kpt[0]]
elif id_kpt == ['all'] elif id_kpt == ['all']:
camx = df_speed[cam1_nb-1].loc[range(np.array(frames)),].sum(axis=1) camx = df_speed[cam1_nb-1].loc[range(np.array(frames)),].sum(axis=1)
camy = df_speed[cam2_nb-1].loc[range(np.array(frames)),].sum(axis=1) camy = df_speed[cam2_nb-1].loc[range(np.array(frames)),].sum(axis=1)
elif len(id_kpt)1 and len(id_kpt)==len(weights_kpt) # ex id_kpt1=9 set to 10, id_kpt2=10 to 15 elif len(id_kpt)==1 and len(id_kpt)==len(weights_kpt): # ex id_kpt1=9 set to 10, id_kpt2=10 to 15
# ajouter frames # ajouter frames
dict_id_weights = {iw for i, w in zip(id_kpt, weights_kpt)} dict_id_weights = {i:w for i, w in zip(id_kpt, weights_kpt)}
camx = df_speed[cam1_nb-1].dot(pd.Series(dict_id_weights).reindex(df_speed[cam1_nb-1].columns, fill_value=0)) camx = df_speed[cam1_nb-1].dot(pd.Series(dict_id_weights).reindex(df_speed[cam1_nb-1].columns, fill_value=0))
camy = df_speed[cam2_nb-1].dot(pd.Series(dict_id_weights).reindex(df_speed[cam2_nb-1].columns, fill_value=0)) camy = df_speed[cam2_nb-1].dot(pd.Series(dict_id_weights).reindex(df_speed[cam2_nb-1].columns, fill_value=0))
camx = camx.loc[range(np.array(frames))] camx = camx.loc[range(np.array(frames))]
camy = camy.loc[range(np.array(frames))] camy = camy.loc[range(np.array(frames))]
else else:
raise ValueError('wrong values for id_kpt or weights_kpt') raise ValueError('wrong values for id_kpt or weights_kpt')
@ -220,9 +220,9 @@ plt.show()
# et on relance tout le code # et on relance tout le code
if offset 0 if offset == 0:
json_dir_to_offset = json_dirs[cam2_nb-1] json_dir_to_offset = json_dirs[cam2_nb-1]
else else:
json_dir_to_offset = json_dirs[cam1_nb-1] json_dir_to_offset = json_dirs[cam1_nb-1]
offset = -offset offset = -offset