def detect_contours(input_file, thresh_val=255, k_size=5, iterations=30):
"""
>>> contours = detect_contours(test_image)
>>> isinstance(detect_contours(test_image), list)
True
>>> detect_contours(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> detect_contours("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> detect_contours("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> detect_contours(test_image, thresh_val=270)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> detect_contours(test_image, k_size=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Kernel size value must be greater than 0.
>>> detect_contours(test_image, iterations=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Iterations value must be greater than 0.
"""
gray = iu.get_image(input_file, 0)
# Checking arguments
check_threshold(thresh_val)
check_kernel(k_size)
check_iterations(iterations)
gray = iu.pyramid_clean(gray)
th2 = th.adaptive_threshold(gray, max_val=thresh_val,
mode=cv.ADAPTIVE_THRESH_MEAN_C)
th2 = cv.erode(th2, kernel=(k_size, k_size), iterations=iterations)
th2 = cv.bitwise_not(th2)
contours, h = cv.findContours(th2, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
return contours
def detect_contours(input_file, thresh_val=255, k_size=5, iterations=30):
"""
>>> contours = detect_contours(test_image)
>>> isinstance(detect_contours(test_image), list)
True
>>> detect_contours(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> detect_contours("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> detect_contours("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> detect_contours(test_image, thresh_val=270)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> detect_contours(test_image, k_size=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Kernel size value must be greater than 0.
>>> detect_contours(test_image, iterations=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Iterations value must be greater than 0.
"""
gray = iu.get_image(input_file, 0)
# Checking arguments
check_threshold(thresh_val)
check_kernel(k_size)
check_iterations(iterations)
gray = iu.pyramid_clean(gray)
th2 = th.adaptive_threshold(gray, max_val=thresh_val,
mode=cv.ADAPTIVE_THRESH_MEAN_C)
th2 = cv.erode(th2, kernel=(k_size, k_size), iterations=iterations)
th2 = cv.bitwise_not(th2)
contours, h = cv.findContours(th2, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
return contours
def remove_bg(input_file, thresh_val=0, window_size=3, block_size=11, c=5,
mode=0, thresh_type=0):
"""
>>> isinstance(remove_bg(test_image), np.ndarray)
True
>>> isinstance(remove_bg(test_image, mode=1), np.ndarray)
True
>>> isinstance(remove_bg(test_image, mode=2), np.ndarray)
True
>>> remove_bg(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> remove_bg("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> remove_bg("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> remove_bg(test_image, window_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Window size value must be greater than 0.
>>> remove_bg(test_image, window_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Window size value must be odd.
>>> remove_bg(test_image, block_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Block size value must be greater than 0.
>>> remove_bg(test_image, block_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Block size value must be odd.
>>> remove_bg(test_image, c='a')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Constraint must be integer.
>>> remove_bg(test_image, thresh_type=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold_type value must be between 0 and 4.
>>> remove_bg(test_image, thresh_type=6)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold_type value must be between 0 and 4.
>>> remove_bg(test_image, mode=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Thres_type value must be between 0 and 2 (0-Adapt-Gauss, 1-Adapt-Mean, 2-Simple).
>>> remove_bg(test_image, mode=3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Thres_type value must be between 0 and 2 (0-Adapt-Gauss, 1-Adapt-Mean, 2-Simple).
"""
# Checking arguments
check_threshold(thresh_val)
check_window_size(window_size)
check_block_size(block_size)
check_c(c)
check_mode(mode)
# Loading image
image = iu.get_image(input_file)
# Removing noise by blurring and thresholding
image = cv.GaussianBlur(image, (window_size, window_size), 0)
result = []
if mode == 2:
result = th.threshold(image, thresh_val, thresh_type=thresh_type)
else:
result = th.adaptive_threshold(image, block_size=block_size, c=c,
mode=mode, thresh_type=thresh_type)
return result
def clean(input_file, thresh_val=200, window_size=5, kernel_size=5):
"""
>>> isinstance(clean(test_image), np.ndarray)
True
>>> clean(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> clean("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> clean("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> clean(test_image, thresh_val=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> clean(test_image, thresh_val=260)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> clean(test_image, window_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Window size value must be greater than 0.
>>> clean(test_image, window_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Window size value must be odd.
>>> clean(test_image, kernel_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Kernel size value must be greater than 0.
>>> clean(test_image, kernel_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Kernel size value must be odd.
"""
# Checking arguments
check_kernel_size(kernel_size)
check_threshold(thresh_val)
check_window_size(window_size)
# Loading the image
image = iu.get_image(input_file, 0)
# Applying Gaussian and median blur and erode
image = cv.GaussianBlur(image, (window_size, window_size), 0)
image = cv.medianBlur(image, window_size)
image = cv.erode(image, (kernel_size, kernel_size))
return th.adaptive_threshold(image, thresh_val)
def clean(input_file, thresh_val=200, window_size=5, kernel_size=5):
"""
>>> isinstance(clean(test_image), np.ndarray)
True
>>> clean(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> clean("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> clean("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> clean(test_image, thresh_val=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> clean(test_image, thresh_val=260)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> clean(test_image, window_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Window size value must be greater than 0.
>>> clean(test_image, window_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Window size value must be odd.
>>> clean(test_image, kernel_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Kernel size value must be greater than 0.
>>> clean(test_image, kernel_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Kernel size value must be odd.
"""
# Checking arguments
check_kernel_size(kernel_size)
check_threshold(thresh_val)
check_window_size(window_size)
# Loading the image
image = iu.get_image(input_file, 0)
# Applying Gaussian and median blur and erode
image = cv.GaussianBlur(image, (window_size, window_size), 0)
image = cv.medianBlur(image, window_size)
image = cv.erode(image, (kernel_size, kernel_size))
return th.adaptive_threshold(image, thresh_val)
def remove_bg(input_file,
thresh_val=0,
window_size=3,
block_size=11,
c=5,
mode=0,
thresh_type=0):
"""
>>> isinstance(remove_bg(test_image), np.ndarray)
True
>>> isinstance(remove_bg(test_image, mode=1), np.ndarray)
True
>>> isinstance(remove_bg(test_image, mode=2), np.ndarray)
True
>>> remove_bg(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> remove_bg("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> remove_bg("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> remove_bg(test_image, window_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Window size value must be greater than 0.
>>> remove_bg(test_image, window_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Window size value must be odd.
>>> remove_bg(test_image, block_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Block size value must be greater than 0.
>>> remove_bg(test_image, block_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Block size value must be odd.
>>> remove_bg(test_image, c='a')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Constraint must be integer.
>>> remove_bg(test_image, thresh_type=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold_type value must be between 0 and 4.
>>> remove_bg(test_image, thresh_type=6)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold_type value must be between 0 and 4.
>>> remove_bg(test_image, mode=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Thres_type value must be between 0 and 2 (0-Adapt-Gauss, 1-Adapt-Mean, 2-Simple).
>>> remove_bg(test_image, mode=3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Thres_type value must be between 0 and 2 (0-Adapt-Gauss, 1-Adapt-Mean, 2-Simple).
"""
# Checking arguments
check_threshold(thresh_val)
check_window_size(window_size)
check_block_size(block_size)
check_c(c)
check_mode(mode)
# Loading image
image = iu.get_image(input_file)
# Removing noise by blurring and thresholding
image = cv.GaussianBlur(image, (window_size, window_size), 0)
result = []
if mode == 2:
result = th.threshold(image, thresh_val, thresh_type=thresh_type)
else:
result = th.adaptive_threshold(image,
block_size=block_size,
c=c,
mode=mode,
thresh_type=thresh_type)
return result
