Fixed previous commit

Author: davidpagnon

Sports2D/Utilities/common.py

@@ -695,6 +695,73 @@ def write_calibration(calib_params, toml_path):
         meta = '[metadata]\nadjusted = false\nerror = 0.0\n'
         cal_f.write(meta)
 
+    
+def pad_shape(arr, target_len, fill_value=np.nan):
+    '''
+    Pads an array to the target length with specified fill values
+    
+    INPUTS:
+    - arr: Input array to be padded.
+    - target_len: The target length of the first dimension after padding.
+    - fill_value: The value to use for padding (default: np.nan).
+    
+    OUTPUTS:
+    - Padded array with shape (target_len, ...) matching the input dimensions.
+    '''
+
+    if len(arr) < target_len:
+        pad_shape = (target_len - len(arr),) + arr.shape[1:]
+        padding = np.full(pad_shape, fill_value)
+        return np.concatenate((arr, padding))
+    
+    return arr
+
+
+def min_with_single_indices(L, T):
+    '''
+    Let L be a list (size s) with T associated tuple indices (size s).
+    Select the smallest values of L, considering that 
+    the next smallest value cannot have the same numbers 
+    in the associated tuple as any of the previous ones.
+
+    Example:
+    L = [  20,   27,  51,    33,   43,   23,   37,   24,   4,   68,   84,    3  ]
+    T = list(it.product(range(2),range(3)))
+      = [(0,0),(0,1),(0,2),(0,3),(1,0),(1,1),(1,2),(1,3),(2,0),(2,1),(2,2),(2,3)]
+
+    - 1st smallest value: 3 with tuple (2,3), index 11
+    - 2nd smallest value when excluding indices (2,.) and (.,3), i.e. [(0,0),(0,1),(0,2),X,(1,0),(1,1),(1,2),X,X,X,X,X]:
+    20 with tuple (0,0), index 0
+    - 3rd smallest value when excluding [X,X,X,X,X,(1,1),(1,2),X,X,X,X,X]:
+    23 with tuple (1,1), index 5
+    
+    INPUTS:
+    - L: list (size s)
+    - T: T associated tuple indices (size s)
+
+    OUTPUTS: 
+    - minL: list of smallest values of L, considering constraints on tuple indices
+    - argminL: list of indices of smallest values of L (indices of best combinations)
+    - T_minL: list of tuples associated with smallest values of L
+    '''
+
+    minL = [np.nanmin(L)]
+    argminL = [np.nanargmin(L)]
+    T_minL = [T[argminL[0]]]
+    
+    mask_tokeep = np.array([True for t in T])
+    i=0
+    while mask_tokeep.any()==True:
+        mask_tokeep = mask_tokeep & np.array([t[0]!=T_minL[i][0] and t[1]!=T_minL[i][1] for t in T])
+        if mask_tokeep.any()==True:
+            indicesL_tokeep = np.where(mask_tokeep)[0]
+            minL += [np.nanmin(np.array(L)[indicesL_tokeep]) if not np.isnan(np.array(L)[indicesL_tokeep]).all() else np.nan]
+            argminL += [indicesL_tokeep[np.nanargmin(np.array(L)[indicesL_tokeep])] if not np.isnan(minL[-1]) else indicesL_tokeep[0]]
+            T_minL += (T[argminL[i+1]],)
+            i+=1
+    
+    return np.array(minL), np.array(argminL), np.array(T_minL)
+    
 
 def sort_people_sports2d(keyptpre, keypt, scores=None):
     '''

Sports2D/process.py

@@ -338,73 +338,6 @@ def compute_angle(ang_name, person_X_flipped, person_Y, angle_dict, keypoints_id
     return ang
 
 
-def min_with_single_indices(L, T):
-    '''
-    Let L be a list (size s) with T associated tuple indices (size s).
-    Select the smallest values of L, considering that 
-    the next smallest value cannot have the same numbers 
-    in the associated tuple as any of the previous ones.
-
-    Example:
-    L = [  20,   27,  51,    33,   43,   23,   37,   24,   4,   68,   84,    3  ]
-    T = list(it.product(range(2),range(3)))
-      = [(0,0),(0,1),(0,2),(0,3),(1,0),(1,1),(1,2),(1,3),(2,0),(2,1),(2,2),(2,3)]
-
-    - 1st smallest value: 3 with tuple (2,3), index 11
-    - 2nd smallest value when excluding indices (2,.) and (.,3), i.e. [(0,0),(0,1),(0,2),X,(1,0),(1,1),(1,2),X,X,X,X,X]:
-    20 with tuple (0,0), index 0
-    - 3rd smallest value when excluding [X,X,X,X,X,(1,1),(1,2),X,X,X,X,X]:
-    23 with tuple (1,1), index 5
-    
-    INPUTS:
-    - L: list (size s)
-    - T: T associated tuple indices (size s)
-
-    OUTPUTS: 
-    - minL: list of smallest values of L, considering constraints on tuple indices
-    - argminL: list of indices of smallest values of L (indices of best combinations)
-    - T_minL: list of tuples associated with smallest values of L
-    '''
-
-    minL = [np.nanmin(L)]
-    argminL = [np.nanargmin(L)]
-    T_minL = [T[argminL[0]]]
-    
-    mask_tokeep = np.array([True for t in T])
-    i=0
-    while mask_tokeep.any()==True:
-        mask_tokeep = mask_tokeep & np.array([t[0]!=T_minL[i][0] and t[1]!=T_minL[i][1] for t in T])
-        if mask_tokeep.any()==True:
-            indicesL_tokeep = np.where(mask_tokeep)[0]
-            minL += [np.nanmin(np.array(L)[indicesL_tokeep]) if not np.isnan(np.array(L)[indicesL_tokeep]).all() else np.nan]
-            argminL += [indicesL_tokeep[np.nanargmin(np.array(L)[indicesL_tokeep])] if not np.isnan(minL[-1]) else indicesL_tokeep[0]]
-            T_minL += (T[argminL[i+1]],)
-            i+=1
-    
-    return np.array(minL), np.array(argminL), np.array(T_minL)
-    
-    
-def pad_shape(arr, target_len, fill_value=np.nan):
-    '''
-    Pads an array to the target length with specified fill values
-    
-    INPUTS:
-    - arr: Input array to be padded.
-    - target_len: The target length of the first dimension after padding.
-    - fill_value: The value to use for padding (default: np.nan).
-    
-    OUTPUTS:
-    - Padded array with shape (target_len, ...) matching the input dimensions.
-    '''
-
-    if len(arr) < target_len:
-        pad_shape = (target_len - len(arr),) + arr.shape[1:]
-        padding = np.full(pad_shape, fill_value)
-        return np.concatenate((arr, padding))
-    
-    return arr
-
-
 def draw_dotted_line(img, start, direction, length, color=(0, 255, 0), gap=7, dot_length=3, thickness=thickness):
     '''
     Draw a dotted line with on a cv2 image