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Update image_processing.py #495

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Update image_processing.py #495

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95f4c71
Update image_processing.py
whoiskatrin Jan 5, 2023
9862a6c
Update image_processing.py
whoiskatrin Jan 5, 2023
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41 changes: 8 additions & 33 deletions camelot/image_processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@

def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2):
"""Thresholds an image using OpenCV's adaptiveThreshold.

Parameters
----------
imagename : string
Expand All @@ -18,41 +17,29 @@ def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2):
blocksize : int, optional (default: 15)
Size of a pixel neighborhood that is used to calculate a
threshold value for the pixel: 3, 5, 7, and so on.

For more information, refer `OpenCV's adaptiveThreshold <https://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold>`_.
c : int, optional (default: -2)
Constant subtracted from the mean or weighted mean.
Normally, it is positive but may be zero or negative as well.

For more information, refer `OpenCV's adaptiveThreshold <https://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold>`_.

Returns
-------
img : object
numpy.ndarray representing the original image.
threshold : object
numpy.ndarray representing the thresholded image.

"""
img = cv2.imread(imagename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

if process_background:
threshold = cv2.adaptiveThreshold(
gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, blocksize, c
)
else:
threshold = cv2.adaptiveThreshold(
np.invert(gray),
255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,
blocksize,
c,
)
if not process_background:
gray = np.invert(gray)
threshold = cv2.adaptiveThreshold(
gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, blocksize, c
)
return img, threshold



def find_lines(
threshold, regions=None, direction="horizontal", line_scale=15, iterations=0
):
Expand Down Expand Up @@ -177,7 +164,6 @@ def find_contours(vertical, horizontal):

def find_joints(contours, vertical, horizontal):
"""Finds joints/intersections present inside each table boundary.

Parameters
----------
contours : list
Expand All @@ -188,30 +174,19 @@ def find_joints(contours, vertical, horizontal):
numpy.ndarray representing pixels where vertical lines lie.
horizontal : object
numpy.ndarray representing pixels where horizontal lines lie.

Returns
-------
tables : dict
Dict with table boundaries as keys and list of intersections
in that boundary as their value.
Keys are of the form (x1, y1, x2, y2) where (x1, y1) -> lb
and (x2, y2) -> rt in image coordinate space.

"""
joints = np.multiply(vertical, horizontal)
tables = {}
for c in contours:
x, y, w, h = c
roi = joints[y : y + h, x : x + w]
try:
__, jc, __ = cv2.findContours(
roi.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE
)
except ValueError:
# for opencv backward compatibility
jc, __ = cv2.findContours(
roi.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE
)
roi = joints[y:y+h, x:x+w]
jc = cv2.findContours(roi.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)[1]
if len(jc) <= 4: # remove contours with less than 4 joints
continue
joint_coords = []
Expand Down