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 get_analysis(input_file, min_dim=1000):
original = iu.get_image(input_file)
original = iu.pyramid_clean(original)
cleaned = cc.delete_border_noise(original)
contours = cc.detect_contours(cleaned)
contours = cc.delete_small_contours(contours, min_dim)
contours = cc.get_squares(contours)
contours = cc.join_contours(contours, min_dist=10)
blank = np.zeros(original.shape, np.uint8)
blank[:] = 255
blank2 = np.zeros(original.shape, np.uint8)
blank2[:] = 255
contours_lines = cc.draw_contours(blank, contours, color=(255, 0, 0),
thickness=10)
filled = cc.draw_contours(blank2, contours, color=(0, 0, 0), thickness=-1)
if len(contours) > 0:
dimension = 0
area = 0
squares = len(contours)
for c in contours:
dimension = dimension + cc.get_contour_dimension(c)
area = area + cc.get_contour_area(c)
else:
dimension = -1
area = -1
squares = 0
model = aa.get_model(filled)
corners = cc.detect_corners(contours_lines)
num_corners = len(corners)
results = [input_file,
dimension,
area,
squares,
model,
num_corners
]
return results
def get_analysis(input_file, min_dim=1000):
original = iu.get_image(input_file)
original = iu.pyramid_clean(original)
cleaned = cc.delete_border_noise(original)
contours = cc.detect_contours(cleaned)
contours = cc.delete_small_contours(contours, min_dim)
contours = cc.get_squares(contours)
contours = cc.join_contours(contours, min_dist=10)
blank = np.zeros(original.shape, np.uint8)
blank[:] = 255
blank2 = np.zeros(original.shape, np.uint8)
blank2[:] = 255
contours_lines = cc.draw_contours(blank,
contours,
color=(255, 0, 0),
thickness=10)
filled = cc.draw_contours(blank2, contours, color=(0, 0, 0), thickness=-1)
if len(contours) > 0:
dimension = 0
area = 0
squares = len(contours)
for c in contours:
dimension = dimension + cc.get_contour_dimension(c)
area = area + cc.get_contour_area(c)
else:
dimension = -1
area = -1
squares = 0
model = aa.get_model(filled)
corners = cc.detect_corners(contours_lines)
num_corners = len(corners)
results = [input_file, dimension, area, squares, model, num_corners]
return results
def delete_border_noise(input_file, width=20, color=(255, 255, 255)):
"""
>>> isinstance(delete_border_noise(test_image), np.ndarray)
True
>>> delete_border_noise(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> delete_border_noise("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> delete_border_noise("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> delete_border_noise(test_image, width=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Width value must be greater than 0.
>>> delete_border_noise(test_image, color=(-10, 0, 0))
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Color value must be: (0-255, 0-255, 0-255).
"""
# Checking arguments
iu.check_color(color)
check_width(width)
image = iu.get_image(input_file)
image = iu.pyramid_clean(image)
image[0:width, :] = color
image[:, 0:width] = color
image[:, -width:] = color
image[-width:, :] = color
return image
def delete_border_noise(input_file, width=20, color=(255, 255, 255)):
"""
>>> isinstance(delete_border_noise(test_image), np.ndarray)
True
>>> delete_border_noise(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> delete_border_noise("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> delete_border_noise("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> delete_border_noise(test_image, width=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Width value must be greater than 0.
>>> delete_border_noise(test_image, color=(-10, 0, 0))
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Color value must be: (0-255, 0-255, 0-255).
"""
# Checking arguments
iu.check_color(color)
check_width(width)
image = iu.get_image(input_file)
image = iu.pyramid_clean(image)
image[0:width, :] = color
image[:, 0:width] = color
image[:, -width:] = color
image[-width:, :] = color
return image