def ftm_pyramid(input_file, template_file, max_level = 5):
img = iu.get_image(input_file)
tpl = iu.get_image(template_file)
image = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
template = cv.cvtColor(tpl, cv.COLOR_BGR2GRAY)
tm_results = temp_match(image, template, max_level)
c = 0
flag = False
while flag is False and c < np.size(tm_results):
current = tm_results[c]
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(current)
if max_val > 0.9:
cv.rectangle(img,
max_loc,
(max_loc[0] + template.shape[1],
max_loc[1] + template.shape[0]),
(0,0,255), 2)
else:
flag = True
c = c+1
cv.imshow("Result", img)
cv.waitKey()
return 0
def delete_lines(input_file,
lines,
probabilistic=False,
line_length=1000,
width=5,
color=(255, 255, 255)):
"""
>>> lines = detect_lines(test_image)
>>> isinstance(delete_lines(test_image, lines), np.ndarray)
True
>>> lines = detect_lines(test_image, probabilistic=True)
>>> isinstance(delete_lines(test_image, lines, probabilistic=True), np.ndarray)
True
>>> delete_lines(None, lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> delete_lines("", lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> delete_lines("fakeRoute", lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> delete_lines(test_image, None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Lines can't be None.
>>> delete_lines(test_image, lines, line_length=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Line_length value must be greater than 0.
>>> delete_lines(test_image, lines, width=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Width value must be greater than 0.
>>> delete_lines(test_image, lines, 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
check_lines(lines)
check_line_length(line_length)
check_width(width)
iu.check_color(color)
image = iu.get_image(input_file)
return draw_lines(image, lines, probabilistic, line_length, width, color)
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 draw_corners(input_file,
corners,
radius=5,
color=(0, 0, 255),
thickness=-1):
"""
>>> corners = detect_corners(test_image)
>>> isinstance(draw_corners(test_image, corners), np.ndarray)
True
>>> draw_corners(None, corners)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> draw_corners("", corners)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> draw_corners("fakeRoute", corners)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> draw_corners(test_image, None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Corners can't be None.
>>> draw_corners(test_image, [])
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Corners can't be void.
>>> draw_corners(test_image, corners, 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).
>>> draw_corners(test_image, corners, radius=-5)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Radius value must be greater than 0.
"""
# Checking arguments
check_corners(corners)
check_radius(radius)
iu.check_color(color)
img = iu.get_image(input_file)
for i in corners:
x, y = i.ravel()
cv.circle(img, (x, y), radius, color, thickness)
return img
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 in_contour(input_file, point, squares=True, thresh_val=255, k_size=5, iterations=30):
"""
>>> isinstance(in_contour(test_image, (200, 200)), np.ndarray)
True
>>> in_contour(test_image, (0, 0))
-1
>>> in_contour(None, (0,0))
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> in_contour("", (0,0))
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> in_contour("fakeRoute", (0,0))
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> in_contour(test_image, (0,0), thresh_val=-200)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> in_contour(test_image, (0,0), k_size=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Kernel size value must be greater than 0.
>>> in_contour(test_image, (0,0), iterations=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Iterations value must be greater than 0.
"""
image = iu.get_image(input_file)
check_threshold(thresh_val)
check_kernel(k_size)
check_iterations(iterations)
contours = detect_contours(image, thresh_val, k_size, iterations)
if squares == True:
contours = get_squares(contours)
contours = join_contours(contours)
cnt = -1
for c in contours:
if cv.pointPolygonTest(c, point, measureDist=False) >= 0:
cnt = c
return cnt
def in_contour(input_file, point, squares=True, thresh_val=255, k_size=5, iterations=30):
"""
>>> isinstance(in_contour(test_image, (200, 200)), np.ndarray)
True
>>> in_contour(test_image, (0, 0))
-1
>>> in_contour(None, (0,0))
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> in_contour("", (0,0))
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> in_contour("fakeRoute", (0,0))
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> in_contour(test_image, (0,0), thresh_val=-200)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> in_contour(test_image, (0,0), k_size=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Kernel size value must be greater than 0.
>>> in_contour(test_image, (0,0), iterations=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Iterations value must be greater than 0.
"""
image = iu.get_image(input_file)
check_threshold(thresh_val)
check_kernel(k_size)
check_iterations(iterations)
contours = detect_contours(image, thresh_val, k_size, iterations)
if squares == True:
contours = get_squares(contours)
contours = join_contours(contours)
cnt = -1
for c in contours:
if cv.pointPolygonTest(c, point, measureDist=False) >= 0:
cnt = c
return cnt
def delete_lines(input_file, lines, width=5, color=(255, 255, 255)):
image = iu.get_image(input_file)
check_lines(lines)
check_width(width)
check_color(color)
return draw_lines(image, lines, width, color)
def get_gray_intensity_analysis(input_file, split_x=3, split_y=3):
"""
>>> isinstance(get_gray_intensity_analysis(test_image), list)
True
>>> get_gray_intensity_analysis(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> get_gray_intensity_analysis("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> get_gray_intensity_analysis("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> get_gray_intensity_analysis(test_image, split_x=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: The split value must be greater than 0.
>>> get_gray_intensity_analysis(test_image, split_y=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: The split value must be greater than 0.
"""
# Checking arguments
check_split(split_x)
check_split(split_y)
# Loading the image
image = iu.get_image(input_file, 0)
results = []
# Calculating split coords
coordinates = iu.split_image(image, split_x, split_y)
# For each split, get it's gray intensity value.
for i, coord in enumerate(coordinates):
x1, x2 = coord[1]
y1, y2 = coord[0]
crop = image[x1:x2, y1:y2]
if crop is None or crop == []:
raise Exception("Unexpected error while cropping the image.")
intensity = get_gray_intensity(crop)
results.append(intensity)
return results
def delete_lines(input_file, lines, probabilistic=False, line_length=1000,
width=5, color=(255, 255, 255)):
"""
>>> lines = detect_lines(test_image)
>>> isinstance(delete_lines(test_image, lines), np.ndarray)
True
>>> lines = detect_lines(test_image, probabilistic=True)
>>> isinstance(delete_lines(test_image, lines, probabilistic=True), np.ndarray)
True
>>> delete_lines(None, lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> delete_lines("", lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> delete_lines("fakeRoute", lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> delete_lines(test_image, None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Lines can't be None.
>>> delete_lines(test_image, lines, line_length=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Line_length value must be greater than 0.
>>> delete_lines(test_image, lines, width=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Width value must be greater than 0.
>>> delete_lines(test_image, lines, 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
check_lines(lines)
check_line_length(line_length)
check_width(width)
iu.check_color(color)
image = iu.get_image(input_file)
return draw_lines(image, lines, probabilistic, line_length, width, color)
def get_gray_intensity_analysis(input_file, split_x=3, split_y=3):
"""
>>> isinstance(get_gray_intensity_analysis(test_image), list)
True
>>> get_gray_intensity_analysis(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> get_gray_intensity_analysis("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> get_gray_intensity_analysis("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> get_gray_intensity_analysis(test_image, split_x=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: The split value must be greater than 0.
>>> get_gray_intensity_analysis(test_image, split_y=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: The split value must be greater than 0.
"""
# Checking arguments
check_split(split_x)
check_split(split_y)
# Loading the image
image = iu.get_image(input_file, 0)
results = []
# Calculating split coords
coordinates = iu.split_image(image, split_x, split_y)
# For each split, get it's gray intensity value.
for i, coord in enumerate(coordinates):
x1, x2 = coord[1]
y1, y2 = coord[0]
crop = image[x1:x2, y1:y2]
if crop is None or crop == []:
raise Exception("Unexpected error while cropping the image.")
intensity = get_gray_intensity(crop)
results.append(intensity)
return results
def ftm_pyramid(input_file, template_file, max_level=5):
image = iu.get_image(input_file, 0)
template = iu.get_image(template_file, 0)
tm_results = temp_match(image, template, max_level)
print len(tm_results)
for r in tm_results:
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(r)
if max_val > 0.99:
cv.rectangle(image, max_loc, (max_loc[0] + template.shape[1],
max_loc[1] + template.shape[0]),
(0, 0, 255), 1)
cv.imshow("Result", image)
cv.waitKey()
return 0
def temp_match(input, template, max_level):
results = []
input = iu.get_image(input)
source_pyr = buildPyramid(input, max_level)
template_pyr = buildPyramid(template, max_level)
for lvl in range(0, int(max_level), 1):
curr_image = source_pyr[lvl]
curr_template = template_pyr[lvl]
dX = curr_image.shape[1] + 1 - curr_template.shape[1]
dY = curr_image.shape[0] + 1 - curr_template.shape[0]
result = np.zeros([dX, dY])
#On the first level performs regular template matching.
if lvl == 0:
result = cv.matchTemplate(curr_image, curr_template,
cv.TM_CCORR_NORMED)
#On every other level, perform pyramid transformation and template
#matching on the predefined ROI areas, obtained using the result of the
#previous level.
else:
mask = cv.pyrUp(r)
mask8u = cv.inRange(mask, 0, 255)
contours = cv.findContours(mask8u, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_NONE)
#Uses contours to define the region of interest and perform TM on
#the areas.
for i in range(0, np.size(contours)-1):
x, y, w, h = cv.boundingRect(contours[i][0])
tpl_X = curr_template.shape[1]
tpl_Y = curr_template.shape[0]
#result = cv.matchTemplate(curr_image, curr_template,
# cv.TM_CCORR_NORMED)
result[y:y+h, x:x+w] = cv.matchTemplate(
curr_image[y:y+h+tpl_Y, x:x+w+tpl_X],
curr_template, cv.TM_CCORR_NORMED)
T, r = cv.threshold(result, 0.94, 1., cv.THRESH_TOZERO)
cv.imshow("test", r)
cv.waitKey()
results.append(r)
return results
def detect_corners(input_file, max_corners=10, min_dist=50, trust_val=0.5):
"""
>>> isinstance(detect_corners(test_image), np.ndarray)
True
>>> detect_corners(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> detect_corners("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> detect_corners("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> detect_corners(test_image, max_corners=-5)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Max_corners value must be greater than 0.
>>> detect_corners(test_image, min_dist=-100)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Min_dist value must be greater than 0.
>>> detect_corners(test_image, trust_val=5)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Trust_val value must be between 0 and 1.
"""
# Checking arguments
check_max_corners(max_corners)
check_min_dist(min_dist)
check_trust_val(trust_val)
image = iu.get_image(input_file)
contours = detect_contours(image)
blank = image
blank[:] = 255
img = draw_contours(blank, contours, 10, color=(0, 0, 0))
img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
corners = cv.goodFeaturesToTrack(img, max_corners, trust_val, min_dist)
return corners
def detect_corners(input_file, max_corners=10, min_dist=50, trust_val=0.5):
"""
>>> isinstance(detect_corners(test_image), np.ndarray)
True
>>> detect_corners(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> detect_corners("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> detect_corners("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> detect_corners(test_image, max_corners=-5)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Max_corners value must be greater than 0.
>>> detect_corners(test_image, min_dist=-100)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Min_dist value must be greater than 0.
>>> detect_corners(test_image, trust_val=5)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Trust_val value must be between 0 and 1.
"""
# Checking arguments
check_max_corners(max_corners)
check_min_dist(min_dist)
check_trust_val(trust_val)
image = iu.get_image(input_file)
contours = detect_contours(image)
blank = image
blank[:] = 255
img = draw_contours(blank, contours, 10, color=(0, 0, 0))
img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
corners = cv.goodFeaturesToTrack(img, max_corners, trust_val, min_dist)
return corners
def draw_corners(input_file, corners, radius=5, color=(0, 0, 255), thickness=-1):
"""
>>> corners = detect_corners(test_image)
>>> isinstance(draw_corners(test_image, corners), np.ndarray)
True
>>> draw_corners(None, corners)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> draw_corners("", corners)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> draw_corners("fakeRoute", corners)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> draw_corners(test_image, None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Corners can't be None.
>>> draw_corners(test_image, [])
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Corners can't be void.
>>> draw_corners(test_image, corners, 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).
>>> draw_corners(test_image, corners, radius=-5)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Radius value must be greater than 0.
"""
# Checking arguments
check_corners(corners)
check_radius(radius)
iu.check_color(color)
img = iu.get_image(input_file)
for i in corners:
x, y = i.ravel()
cv.circle(img, (x, y), radius, color, thickness)
return img
def build_pyramid(input_file, max_level):
image = iu.get_image(input_file)
results = [image]
aux = image.copy()
for i in range(0, max_level):
aux = cv.pyrDown(aux, (aux.shape[1]/2, aux.shape[0]/2))
results = [aux] + results
return results
def ftm_pyramid(input_file, template_file, max_level=5):
image = iu.get_image(input_file, 0)
template = iu.get_image(template_file, 0)
tm_results = temp_match(image, template, max_level)
print len(tm_results)
for r in tm_results:
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(r)
if max_val > 0.99:
cv.rectangle(image,
max_loc,
(max_loc[0] + template.shape[1],
max_loc[1] + template.shape[0]),
(0, 0, 255), 1)
cv.imshow("Result", image)
cv.waitKey()
return 0
def buildPyramid(input_file, max_level):
image = iu.get_image(input_file)
results = [image]
aux = image
for i in range(0,max_level):
aux = cv.pyrDown(aux)
results = [aux] + results
return results
def build_pyramid(input_file, max_level):
image = iu.get_image(input_file)
results = [image]
aux = image.copy()
for i in range(0, max_level):
aux = cv.pyrDown(aux, (aux.shape[1] / 2, aux.shape[0] / 2))
results = [aux] + results
return results
def get_model(input_file, split_x=3, split_y=3):
"""
>>> isinstance(get_model(test_image), list)
True
>>> get_model(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> get_model("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> get_model("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> get_model(test_image, split_x=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: The split value must be greater than 0.
>>> get_model(test_image, split_y=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: The split value must be greater than 0.
"""
# Checking arguments
check_split(split_x)
check_split(split_y)
# Loading the image
image = iu.get_image(input_file, 0)
mean = math.ceil(np.mean(image))
results = get_gray_intensity_analysis(input_file, split_x, split_y)
for i, r in enumerate(results):
if r <= mean:
results[i] = 1
else:
results[i] = 0
return results
def get_model(input_file, split_x=3, split_y=3):
"""
>>> isinstance(get_model(test_image), list)
True
>>> get_model(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> get_model("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> get_model("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> get_model(test_image, split_x=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: The split value must be greater than 0.
>>> get_model(test_image, split_y=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: The split value must be greater than 0.
"""
# Checking arguments
check_split(split_x)
check_split(split_y)
# Loading the image
image = iu.get_image(input_file, 0)
mean = math.ceil(np.mean(image))
results = get_gray_intensity_analysis(input_file, split_x, split_y)
for i, r in enumerate(results):
if r <= mean:
results[i] = 1
else:
results[i] = 0
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 draw_lines(input_file, lines, width=5, color=(0, 0, 255)):
image = iu.get_image(input_file)
check_lines(lines)
check_width(width)
check_color(color)
if np.size(lines[0]) == 1:
x1, y1, x2, y2 = lines
cv.line(image, (x1, y1), (x2, y2), color, width)
else:
for x1, y1, x2, y2 in lines[0]:
cv.line(image, (x1, y1), (x2, y2), color, width)
return image
def draw_contours(input_file, contours, thickness=0, color=(0, 0, 255)):
"""
>>> contours = detect_contours(test_image)
>>> isinstance(draw_contours(test_image, contours), np.ndarray)
True
>>> draw_contours(None, contours)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> draw_contours("", contours)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> draw_contours("fakeRoute", contours)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> draw_contours(test_image, None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Contours can't be None.
>>> draw_contours(test_image, [])
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Contours can't be void.
>>> draw_contours(test_image, contours, 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
check_contours(contours)
iu.check_color(color)
image = iu.get_image(input_file)
for cnt in contours:
cv.drawContours(image, [cnt], 0, color, thickness)
return image
def draw_contours(input_file, contours, thickness=0, color=(0, 0, 255)):
"""
>>> contours = detect_contours(test_image)
>>> isinstance(draw_contours(test_image, contours), np.ndarray)
True
>>> draw_contours(None, contours)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> draw_contours("", contours)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> draw_contours("fakeRoute", contours)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> draw_contours(test_image, None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Contours can't be None.
>>> draw_contours(test_image, [])
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Contours can't be void.
>>> draw_contours(test_image, contours, 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
check_contours(contours)
iu.check_color(color)
image = iu.get_image(input_file)
for cnt in contours:
cv.drawContours(image, [cnt], 0, color, thickness)
return image
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
def apply(input_file, thresh_values=[250, 245, 240, 230, 225, 220]):
# If thresh is not a list of values transform it.
if not isinstance(thresh_values, list):
thresh_values = [thresh_values]
# Checking arguments and raising expected exceptions
check_arguments(thresh_values)
image = iu.get_image(input_file)
results = []
for i in thresh_values:
th, img_thresh = cv.threshold(image, float(i), 255, cv.THRESH_BINARY)
results = results + [img_thresh]
return results
def clean(input_file, thresh_val = [225, 220, 215, 210, 205, 200],
window_size = 3):
# Ensures that window_size parameter is integer
window_size = int(window_size)
# Checking arguments and raising expected exceptions
check_arguments(window_size)
# Loading the image
image = iu.get_image(input_file)
# Gray-scale and Gaussian Blur
image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
image = cv.GaussianBlur(image, (window_size, window_size), 0)
# Applying threshold list
results = th.apply(image, thresh_val)
return results
def delete_all_lines(input_file,
width=5, color=(255, 255, 255),
min_val=50, max_val=200, aperture_size=3,
rho=1, theta=np.pi/180, threshold=200,
min_line_length=30, max_line_gap=20):
image = iu.get_image(input_file)
check_width(width)
check_color(color)
check_canny_args(min_val, max_val, aperture_size)
check_houghlines_p_args(rho, theta, threshold, min_line_length, max_line_gap)
lines = detect_lines(image, min_val, max_val, aperture_size, rho, theta,
threshold, min_line_length, max_line_gap)
while lines is not None:
image = delete_lines(image, lines, width, color)
lines = detect_lines(image, rho, theta, threshold,
min_line_length, max_line_gap)
return image
def detect_lines(input_file,
min_val=50, max_val=200, aperture_size=3,
rho=1, theta=np.pi/180, threshold=200,
min_line_length=30, max_line_gap=20):
if min_val < max_val:
image = iu.get_image(input_file)
check_canny_args(min_val, max_val, aperture_size)
check_houghlines_p_args(rho, theta, threshold,
min_line_length, max_line_gap)
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
edges = cv.Canny(gray, min_val, max_val, aperture_size)
lines = cv.HoughLinesP(edges, rho, theta, threshold,
min_line_length, max_line_gap)
else:
lines = None
return lines
def clean(input_file, thresh_val = [250, 245, 240, 230, 225, 220],
window_size = 5, kernel_size = 5):
# Ensures that both quality and window_size parameters are integers
window_size = int(window_size)
kernel_size = int(kernel_size)
#Checking arguments and raising expected exceptions
check_arguments(window_size, kernel_size)
# Loading the image
image = iu.get_image(input_file)
# Applying Grayscale, Gaussian and median blur and erode
image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
image = cv.GaussianBlur(image, (window_size, window_size), 0)
image = cv.medianBlur(image, window_size)
image = cv.erode(image, (kernel_size, kernel_size))
# Applying threshold list
results = th.apply(image, thresh_val)
return results
def get_gray_intensity(input_file):
"""
>>> isinstance(get_gray_intensity(test_image), float)
True
>>> get_gray_intensity(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> get_gray_intensity("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> get_gray_intensity("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
"""
image = iu.get_image(input_file, 0)
return math.floor(np.mean(image))
def get_gray_intensity(input_file):
"""
>>> isinstance(get_gray_intensity(test_image), float)
True
>>> get_gray_intensity(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> get_gray_intensity("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> get_gray_intensity("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
"""
image = iu.get_image(input_file, 0)
return math.floor(np.mean(image))
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 delete_all_lines(input_file,
probabilistic=False,
min_val=50,
max_val=200,
aperture_size=3,
rho=1,
theta=np.pi / 180,
threshold=200,
min_line_length=30,
max_line_gap=20,
line_length=1000,
width=5,
color=(255, 255, 255)):
"""
>>> isinstance(delete_all_lines(test_image), np.ndarray)
True
>>> isinstance(delete_all_lines(test_image, probabilistic=True), np.ndarray)
True
>>> delete_all_lines(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> delete_all_lines("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> delete_all_lines("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> delete_all_lines(test_image, min_val=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Min_val value must be between 0 and 255.
>>> delete_all_lines(test_image, max_val=300)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Max_val value must be between 0 and 255.
>>> delete_all_lines(test_image, min_val=100, max_val=30)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Min_val value must be lesser than max_val.
>>> delete_all_lines(test_image, aperture_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Aperture_size value must be greater than 0.
>>> delete_all_lines(test_image, rho=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Rho value must be greater than 0.
>>> delete_all_lines(test_image, theta=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Theta value must be greater than 0.
>>> delete_all_lines(test_image, threshold=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold value must be greater than 0.
>>> delete_all_lines(test_image, line_length=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Line_length value must be greater than 0.
>>> delete_all_lines(test_image, width=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Width value must be greater than 0.
>>> delete_all_lines(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
if probabilistic is False:
check_line_length(line_length)
check_canny_args(min_val, max_val, aperture_size)
check_houghlines_args(rho, theta, threshold, min_line_length, max_line_gap,
probabilistic)
check_width(width)
iu.check_color(color)
image = iu.get_image(input_file)
lines = detect_lines(image, probabilistic, min_val, max_val, aperture_size,
rho, theta, threshold, min_line_length, max_line_gap)
while lines is not None:
image = delete_lines(image, lines, probabilistic, line_length, width,
color)
lines = detect_lines(image, probabilistic, rho, theta, threshold,
min_line_length, max_line_gap)
return image
def adaptive_threshold(input_file, max_val=255, thresh_type=0, block_size=11,
c=5, mode=0):
"""
>>> isinstance(adaptive_threshold(test_image), np.ndarray)
True
>>> isinstance(adaptive_threshold(test_image, mode=1), np.ndarray)
True
>>> adaptive_threshold(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> adaptive_threshold("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> adaptive_threshold("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> adaptive_threshold(test_image, max_val=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> adaptive_threshold(test_image, max_val=260)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> adaptive_threshold(test_image, thresh_type=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold_type value must be between 0 and 4.
>>> adaptive_threshold(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.
>>> adaptive_threshold(test_image, block_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Block size value must be greater than 0.
>>> adaptive_threshold(test_image, block_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Block size value must be odd.
>>> adaptive_threshold(test_image, c='a')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Constraint must be integer.
>>> adaptive_threshold(test_image, c='a')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Constraint must be integer.
>>> adaptive_threshold(test_image, mode=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Mode values are: 0 (Gaussian) and 1 (Mean).
"""
# Checking arguments
check_threshold(max_val)
check_thresh_type(thresh_type)
check_block_size(block_size)
check_c(c)
if mode == 0:
mode = cv.ADAPTIVE_THRESH_GAUSSIAN_C
elif mode == 1:
mode = cv.ADAPTIVE_THRESH_MEAN_C
else:
raise ValueError("Mode values are: 0 (Gaussian) and 1 (Mean).")
# Loading image
image = iu.get_image(input_file, 0)
return cv.adaptiveThreshold(image, maxValue=max_val, thresholdType=thresh_type,
blockSize=block_size, C=c, adaptiveMethod=mode)
def threshold(input_file, thresh_val=200, new_value=255, thresh_type=0):
"""
>>> isinstance(threshold(test_image), np.ndarray)
True
>>> threshold(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> threshold("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> threshold("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> threshold(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.
>>> threshold(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.
>>> threshold(test_image, new_value=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> threshold(test_image, new_value=260)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> threshold(test_image, thresh_type=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold_type value must be between 0 and 4.
>>> threshold(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.
"""
# Checking arguments
check_threshold(thresh_val)
check_threshold(new_value)
check_thresh_type(thresh_type)
# Loading image
image = iu.get_image(input_file, 0)
# Thresholding
th, img_thresh = cv.threshold(image, thresh_val, new_value, thresh_type)
return img_thresh
path, 1024, 1024, args.resolution, args.resolution)
elif 'celeba' in args.dataset.lower():
if args.resolution == None:
args.resolution = 128
output_path = './stats/celeba_{}x{}_fid_stats.npz'.format(
args.resolution, args.resolution)
image_list = sorted(glob.glob(args.data_path))[:n_samples]
image_loader = lambda path: get_celeba_image(
path, 256, 256, args.resolution, args.resolution)
elif 'lsun' in args.dataset.lower():
if args.resolution == None:
args.resolution = 256
output_path = './stats/lsun_{}x{}_fid_stats.npz'.format(
args.resolution, args.resolution)
image_list = sorted(glob.glob(args.data_path))[:n_samples]
image_loader = lambda path: get_image(path, 256, 256, args.resolution,
args.resolution)
else:
raise NotImplementedError()
assert n_samples <= len(image_list)
print("%d images found and loaded" % len(image_list))
print("create inception graph..", end=" ", flush=True)
fid.create_inception_graph(
inception_path) # load the graph into the current TF graph
print("ok")
print("calculte FID stats..", end=" ", flush=True)
feature_map = np.zeros((n_samples, 2048))
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
def draw_lines(input_file, lines, probabilistic=False,
line_length=1000, width=5, color=(0, 0, 255)):
"""
>>> lines = detect_lines(test_image)
>>> isinstance(draw_lines(test_image, lines), np.ndarray)
True
>>> linesp = detect_lines(test_image, probabilistic=True)
>>> isinstance(draw_lines(test_image, linesp, probabilistic=True), np.ndarray)
True
>>> draw_lines(None, lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> draw_lines("", lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> draw_lines("fakeRoute", lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> draw_lines(test_image, None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Lines can't be None.
>>> draw_lines(test_image, lines, line_length=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Line_length value must be greater than 0.
>>> draw_lines(test_image, lines, width=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Width value must be greater than 0.
>>> draw_lines(test_image, lines, 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
check_lines(lines)
check_line_length(line_length)
check_width(width)
iu.check_color(color)
# Loading the image
image = iu.get_image(input_file)
if np.size(lines[0]) == 1:
l_array = [lines]
else:
l_array = lines[0]
for l in l_array:
x1, y1, x2, y2 = get_line_coordinates(l, probabilistic, line_length)
p1 = (x1, y1)
p2 = (x2, y2)
cv.line(image, p1, p2, color, width)
return image
def detect_lines(input_file,
probabilistic=False,
min_val=50,
max_val=200,
aperture_size=3,
rho=1,
theta=np.pi / 180,
threshold=200,
min_line_length=30,
max_line_gap=20):
"""
>>> isinstance(detect_lines(test_image), np.ndarray)
True
>>> isinstance(detect_lines(test_image, probabilistic=True), np.ndarray)
True
>>> detect_lines(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> detect_lines("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> detect_lines("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> detect_lines(test_image, min_val=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Min_val value must be between 0 and 255.
>>> detect_lines(test_image, max_val=300)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Max_val value must be between 0 and 255.
>>> detect_lines(test_image, aperture_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Aperture_size value must be greater than 0.
>>> detect_lines(test_image, rho=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Rho value must be greater than 0.
>>> detect_lines(test_image, theta=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Theta value must be greater than 0.
>>> detect_lines(test_image, threshold=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold value must be greater than 0.
"""
if min_val < max_val:
# Checking arguments
check_canny_args(min_val, max_val, aperture_size)
check_houghlines_args(rho, theta, threshold, min_line_length,
max_line_gap, probabilistic)
image = iu.get_image(input_file)
if probabilistic is False:
edges = cv.Canny(image, min_val, max_val, aperture_size)
lines = cv.HoughLines(edges, rho, theta, threshold)
else:
image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
edges = cv.Canny(image, min_val, max_val, aperture_size)
lines = cv.HoughLinesP(edges, rho, theta, threshold,
min_line_length, max_line_gap)
else:
lines = None
return lines
def draw_lines(input_file,
lines,
probabilistic=False,
line_length=1000,
width=5,
color=(0, 0, 255)):
"""
>>> lines = detect_lines(test_image)
>>> isinstance(draw_lines(test_image, lines), np.ndarray)
True
>>> linesp = detect_lines(test_image, probabilistic=True)
>>> isinstance(draw_lines(test_image, linesp, probabilistic=True), np.ndarray)
True
>>> draw_lines(None, lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> draw_lines("", lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> draw_lines("fakeRoute", lines)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> draw_lines(test_image, None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Lines can't be None.
>>> draw_lines(test_image, lines, line_length=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Line_length value must be greater than 0.
>>> draw_lines(test_image, lines, width=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Width value must be greater than 0.
>>> draw_lines(test_image, lines, 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
check_lines(lines)
check_line_length(line_length)
check_width(width)
iu.check_color(color)
# Loading the image
image = iu.get_image(input_file)
if np.size(lines[0]) == 1:
l_array = [lines]
else:
l_array = lines[0]
for l in l_array:
x1, y1, x2, y2 = get_line_coordinates(l, probabilistic, line_length)
p1 = (x1, y1)
p2 = (x2, y2)
cv.line(image, p1, p2, color, width)
return image
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
def delete_all_lines(input_file, probabilistic=False,
min_val=50, max_val=200, aperture_size=3,
rho=1, theta=np.pi/180, threshold=200,
min_line_length=30, max_line_gap=20,
line_length=1000, width=5, color=(255, 255, 255)):
"""
>>> isinstance(delete_all_lines(test_image), np.ndarray)
True
>>> isinstance(delete_all_lines(test_image, probabilistic=True), np.ndarray)
True
>>> delete_all_lines(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> delete_all_lines("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> delete_all_lines("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> delete_all_lines(test_image, min_val=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Min_val value must be between 0 and 255.
>>> delete_all_lines(test_image, max_val=300)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Max_val value must be between 0 and 255.
>>> delete_all_lines(test_image, min_val=100, max_val=30)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Min_val value must be lesser than max_val.
>>> delete_all_lines(test_image, aperture_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Aperture_size value must be greater than 0.
>>> delete_all_lines(test_image, rho=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Rho value must be greater than 0.
>>> delete_all_lines(test_image, theta=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Theta value must be greater than 0.
>>> delete_all_lines(test_image, threshold=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold value must be greater than 0.
>>> delete_all_lines(test_image, line_length=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Line_length value must be greater than 0.
>>> delete_all_lines(test_image, width=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Width value must be greater than 0.
>>> delete_all_lines(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
if probabilistic is False:
check_line_length(line_length)
check_canny_args(min_val, max_val, aperture_size)
check_houghlines_args(rho, theta, threshold, min_line_length, max_line_gap,
probabilistic)
check_width(width)
iu.check_color(color)
image = iu.get_image(input_file)
lines = detect_lines(image, probabilistic, min_val, max_val, aperture_size,
rho, theta, threshold, min_line_length, max_line_gap)
while lines is not None:
image = delete_lines(image, lines, probabilistic, line_length, width,
color)
lines = detect_lines(image, probabilistic, rho, theta, threshold,
min_line_length, max_line_gap)
return image
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 threshold(input_file, thresh_val=200, new_value=255, thresh_type=0):
"""
>>> isinstance(threshold(test_image), np.ndarray)
True
>>> threshold(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> threshold("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> threshold("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> threshold(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.
>>> threshold(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.
>>> threshold(test_image, new_value=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> threshold(test_image, new_value=260)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> threshold(test_image, thresh_type=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold_type value must be between 0 and 4.
>>> threshold(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.
"""
# Checking arguments
check_threshold(thresh_val)
check_threshold(new_value)
check_thresh_type(thresh_type)
# Loading image
image = iu.get_image(input_file, 0)
# Thresholding
th, img_thresh = cv.threshold(image, thresh_val, new_value, thresh_type)
return img_thresh
def adaptive_threshold(input_file,
max_val=255,
thresh_type=0,
block_size=11,
c=5,
mode=0):
"""
>>> isinstance(adaptive_threshold(test_image), np.ndarray)
True
>>> isinstance(adaptive_threshold(test_image, mode=1), np.ndarray)
True
>>> adaptive_threshold(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> adaptive_threshold("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> adaptive_threshold("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> adaptive_threshold(test_image, max_val=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> adaptive_threshold(test_image, max_val=260)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: All threshold values must be between 0 and 255.
>>> adaptive_threshold(test_image, thresh_type=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold_type value must be between 0 and 4.
>>> adaptive_threshold(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.
>>> adaptive_threshold(test_image, block_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Block size value must be greater than 0.
>>> adaptive_threshold(test_image, block_size=2)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Block size value must be odd.
>>> adaptive_threshold(test_image, c='a')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Constraint must be integer.
>>> adaptive_threshold(test_image, c='a')
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Constraint must be integer.
>>> adaptive_threshold(test_image, mode=-1)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Mode values are: 0 (Gaussian) and 1 (Mean).
"""
# Checking arguments
check_threshold(max_val)
check_thresh_type(thresh_type)
check_block_size(block_size)
check_c(c)
if mode == 0:
mode = cv.ADAPTIVE_THRESH_GAUSSIAN_C
elif mode == 1:
mode = cv.ADAPTIVE_THRESH_MEAN_C
else:
raise ValueError("Mode values are: 0 (Gaussian) and 1 (Mean).")
# Loading image
image = iu.get_image(input_file, 0)
return cv.adaptiveThreshold(image,
maxValue=max_val,
thresholdType=thresh_type,
blockSize=block_size,
C=c,
adaptiveMethod=mode)
def detect_lines(input_file, probabilistic=False,
min_val=50, max_val=200, aperture_size=3,
rho=1, theta=np.pi/180, threshold=200,
min_line_length=30, max_line_gap=20):
"""
>>> isinstance(detect_lines(test_image), np.ndarray)
True
>>> isinstance(detect_lines(test_image, probabilistic=True), np.ndarray)
True
>>> detect_lines(None)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be a None object
>>> detect_lines("")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: The input file can't be ''.
>>> detect_lines("fakeRoute")
Traceback (most recent call last):
File "<stdin>", line 1, in ?
IOError: Input file not found.
>>> detect_lines(test_image, min_val=-10)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Min_val value must be between 0 and 255.
>>> detect_lines(test_image, max_val=300)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Max_val value must be between 0 and 255.
>>> detect_lines(test_image, aperture_size=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Aperture_size value must be greater than 0.
>>> detect_lines(test_image, rho=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Rho value must be greater than 0.
>>> detect_lines(test_image, theta=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Theta value must be greater than 0.
>>> detect_lines(test_image, threshold=-3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: Threshold value must be greater than 0.
"""
if min_val < max_val:
# Checking arguments
check_canny_args(min_val, max_val, aperture_size)
check_houghlines_args(rho, theta, threshold, min_line_length,
max_line_gap, probabilistic)
image = iu.get_image(input_file)
if probabilistic is False:
edges = cv.Canny(image, min_val, max_val, aperture_size)
lines = cv.HoughLines(edges, rho, theta, threshold)
else:
image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
edges = cv.Canny(image, min_val, max_val, aperture_size)
lines = cv.HoughLinesP(edges, rho, theta, threshold, min_line_length, max_line_gap)
else:
lines = None
return lines
