def search_sequence_cv2(arr, seq):
""" Find sequence in an array using cv2.
Copied from Source: https://stackoverflow.com/a/36535397
Inputs
------
* arr (list or np.array): The array to search the sequence in.
* seq (list or np.array): The sequence to be searched.
Returns
-------
* (list): Index(s) where the sequence match was found.
Returns empty list if not found any.
"""
# Run a template match with input sequence as the template across
# the entire length of the input array and get scores.
arr = np.array(arr)
seq = np.array(seq)
S = cv2m(arr.astype('uint8'),seq.astype('uint8'),cv2.TM_SQDIFF)
# Now, with floating point array cases, the matching scores might not be
# exactly zeros, but would be very small numbers as compared to others.
# So, for that use a very small to be used to threshold the scorees
# against and decide for matches.
thresh = 1e-5 # Would depend on elements in seq. So, be careful setting this.
# Find the matching indices
idx = np.where(S.ravel() < thresh)[0]
if len(idx) > 0:
return idx
else:
return []
def get_legal_actions(self, joint_states):
assert len(joint_states) == 2
board = joint_states[0] + joint_states[1]
legal_moves = []
for k in range(board.shape[1]):
match = cv2m(self.legal_move_pattern.astype(self.datatype),
board[:, k].astype(self.datatype), cv2.TM_SQDIFF)
i, j = np.where(match == 0)
if len(i) != 0:
legal_moves.append(np.max(i) * board.shape[1] + k)
return legal_moves
def search_sequence_cv2(arr,seq):
""" Find sequence in an array using cv2.
"""
# Run a template match with input sequence as the template across
# the entire length of the input array and get scores.
S = cv2m(arr.astype('uint8'),seq.astype('uint8'),cv2.TM_SQDIFF)
# Now, with floating point array cases, the matching scores might not be
# exactly zeros, but would be very small numbers as compared to others.
# So, for that use a very small to be used to threshold the scorees
# against and decide for matches.
thresh = 1e-5 # Would depend on elements in seq. So, be careful setting this.
# Find the matching indices
idx = np.where(S.ravel() < thresh)[0]
# Get the range of those indices as final output
if len(idx)>0:
return np.unique((idx[:,None] + np.arange(seq.size)).ravel())
else:
return [] # No match found
def cv2_based(field_array, match_array):
M = cv2m(field_array, match_array, cv2.TM_SQDIFF_NORMED)
return np.where(1e-6 >= M)
def findSuper8Sprocket3(image, filename):
matches = cv2m(image, arrToFind, cv2.TM_SQDIFF)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(matches)
return minLoc