Comments (1)
dmerinodel
commented on July 21, 2024
Hi,
I kept studying that error and after some 'debugging' in the raw2temp module, I discovered that my thermal_np_array had some 0 values. I'm assuming that these are some hardware bad detections and I'm replacing those 0s with some non zero value from the same row (e.g, the first non zero value). I'm including this piece of code
for row in thermal_np:
if 0 in row:
non_zeros = row[row != 0]
row[row == 0] = non_zeros[0]Update 18-06-21. That method led to bad coloured images. I ended up adding the following module.
def zeros_filter(matrix, row, col):
"""
Input: bidimensional np.array, row and column where the 0 lies.
Output: average of the surrounding values.
"""
shape = np.shape(matrix)
rows = shape[0]
cols = shape[1]
# Surroundings depends on the position of the element (corners, edges or inner
# points). Each 'if/elif' is for each possible position in the matrix. Surrounding
# non-zero elements are selected for the interpolation and if there isn't any, a
# non-zero value from the row is taken.
if (row not in {0,rows-1}) and (col not in {0,cols-1}): # inner points
surr = np.array([matrix[row-1][col-1],matrix[row-1][col],matrix[row][col+1],
matrix[row][col-1],matrix[row][col+1],
matrix[row+1][col-1],matrix[row+1][col],matrix[row+1][col+1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
elif (row == 0): # top row
if (col == 0):
surr = np.array([matrix[row][col+1],matrix[row+1][col],
matrix[row+1][col+1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
elif (col == cols-1):
surr = np.array([matrix[row][col-1],matrix[row+1][col],
matrix[row+1][col-1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
else:
surr = np.array([matrix[row][col-1],matrix[row][col+1],
matrix[row+1][col-1],matrix[row+1][col],matrix[row+1][col+1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
elif (row == rows-1): # bottom row
if (col == 0):
surr = np.array([matrix[row][col+1],matrix[row-1][col],
matrix[row][col+1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
elif (col == cols-1):
surr = np.array([matrix[row][col-1],matrix[row-1][col],
matrix[row-1][col-1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
else:
surr = np.array([matrix[row-1][col-1],matrix[row-1][col],matrix[row][col+1],
matrix[row][col-1],matrix[row][col+1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
elif (col == 0): # first column
surr = np.array([matrix[row-1][col],matrix[row][col+1],
matrix[row][col+1],
matrix[row+1][col],matrix[row+1][col+1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
elif (col == cols-1): # last column
surr = np.array([matrix[row-1][col],matrix[row][col-1],
matrix[row][col-1],
matrix[row+1][col],matrix[row+1][col-1]])
surr = surr[surr != 0]
if len(surr) == 0:
new_value = matrix[row][matrix[row] != 0][0]
else:
mean = np.mean(surr)
new_value = mean
return new_valueI think variable names and comments are clear enough. It is some kind of "convolution". If someone knows how to do it in a easier way I'd appreciate it :)
from read_thermal.py.
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from read_thermal.py.