def get_filling_ratio(rectangle: Rectangle, mask: np.array):
# compute the area of bboxes
bbox_area = rectangle.get_area()
whites = count_whites(rectangle, mask)
# return the filling ratio
return whites / bbox_area
def get_mask(mask: np.array) -> (np.array, List[Rectangle]):
regions = []
side = SIDE
for _ in range(INTERMEDIATE_STEPS):
kernel = np.empty((side, side))
kernel.fill(1 / 255)
conv_res = cv2.filter2D(mask,
cv2.CV_32F,
kernel,
anchor=(0, 0),
borderType=cv2.BORDER_CONSTANT)
positions = conv_iter(conv_res, side)
for pos in positions:
rec = Rectangle(top_left=(pos[0], pos[1]), width=side, height=side)
regions.append(rec)
side = int(side / SHRINK_MULTIPLIER)
if side % 2 == 0:
side += 1
regions = combine_overlapped_regions(regions)
mask = clear_non_region_mask(mask, regions)
return mask, regions
def get_mask(self, mask: np.array):
a, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
min_point = np.full(2, np.iinfo(np.int).max)
max_point = np.zeros(2).astype(int)
for point in contour:
min_point[0] = min(min_point[0], int(point[0][1]))
min_point[1] = min(min_point[1], int(point[0][0]))
max_point[0] = max(max_point[0], int(point[0][1]))
max_point[1] = max(max_point[1], int(point[0][0]))
rectangle = Rectangle()
rectangle.top_left = min_point.astype(int).tolist()
rectangle.height = int(max_point[0] - min_point[0])
rectangle.width = int(max_point[1] - min_point[1])
fr = get_filling_ratio(rectangle, mask)
ff = rectangle.get_form_factor()
if (rectangle.get_area() < self.min_area or
fr < self.min_fill_ratio or
fr > self.max_fill_ratio or
ff < self.min_form_factor or
ff > self.max_form_factor):
cv2.rectangle(mask, (min_point[1], min_point[0]), (max_point[1], max_point[0]), 0, thickness=cv2.FILLED)
return mask
def main():
composite1 = GraphicComposite('Composite-1')
composite1.add_graphic(Rectangle('Rectangle-1'))
composite1.add_graphic(Rectangle('Rectangle-2'))
composite1.add_graphic(Circle('Circle-1'))
composite2 = GraphicComposite('Composite-2')
composite2.add_graphic(Rectangle('Rectangle-3'))
composite2.add_graphic(Circle('Circle-2'))
composite = GraphicComposite('Overall Composite')
composite.add_graphic(composite1)
composite.add_graphic(composite2)
composite.draw()
print()
composite1.translate(x=10, y=5)
print()
composite2.resize(times=1.4)
def get_cc_regions(mask: np.array) -> List[Rectangle]:
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
regions = []
for contour in contours:
min_point = np.full(2, np.iinfo(np.int).max)
max_point = np.zeros(2).astype(int)
for point in contour:
min_point[0] = min(min_point[0], int(point[0][1]))
min_point[1] = min(min_point[1], int(point[0][0]))
max_point[0] = max(max_point[0], int(point[0][1]))
max_point[1] = max(max_point[1], int(point[0][0]))
rectangle = Rectangle()
rectangle.top_left = min_point.astype(int).tolist()
rectangle.width = int(max_point[0] - min_point[0]) + 1
rectangle.height = int(max_point[1] - min_point[1]) + 1
if rectangle.height < mask.shape[0] and \
rectangle.width < mask.shape[1]:
regions.append(rectangle)
return regions
def get_sizes(self, data: List[Data]) -> (Dict[str, 'Template'], int):
sign_type = {}
total = 0
for sample in data:
for gt in sample.gt:
if gt.type not in sign_type.keys():
sign_type[gt.type] = Template()
sign_type[gt.type].signs.append(
Rectangle(top_left=gt.top_left,
width=gt.width,
height=gt.height))
total += 1
return sign_type, total
def __init__(self, directory: str, name: str):
self.name = name
self.gt = []
self.img_path = '{}/{}.jpg'.format(directory, name)
self.mask_path = '{}/mask/mask.{}.png'.format(directory, name)
with open('{}/gt/gt.{}.txt'.format(directory, name)) as f:
for line in f.readlines():
parts = line.strip().split(' ')
gt = GroundTruth()
gt.type = parts[4]
gt.rectangle = Rectangle()
gt.rectangle.top_left = (float(parts[0]), float(parts[1]))
gt.rectangle.width = float(parts[3]) - float(parts[1]) + 1
gt.rectangle.height = float(parts[2]) - float(parts[0]) + 1
self.gt.append(gt)
def sliding_window(mask: np.array) -> (np.array, List[Rectangle]):
regions = []
side = SIDE
for _ in range(INTERMEDIATE_STEPS):
positions = window_iter(mask, side)
for pos in positions:
regions.append(
Rectangle(top_left=(pos[0], pos[1]), width=side, height=side))
side = int(side / SHRINK_MULTIPLIER)
if side % 2 == 0:
side += 1
regions = combine_overlapped_regions(regions)
mask = clear_non_region_mask(mask, regions)
return mask, regions
def get_mask(mask: np.array) -> (np.array, List[Rectangle]):
integral = cv2.integral(mask / 255)
positions = int_iter(integral)
regions = []
for pos in positions:
regions.append(
Rectangle(
top_left=(pos[0], pos[1]),
width=pos[2],
height=pos[2]
)
)
regions = combine_overlapped_regions(regions)
mask = clear_non_region_mask(mask, regions)
return mask, regions
def train(self, images: List[Picture]) -> List[Rectangle]:
self.compare_histograms.train(images)
return [Rectangle() for _ in images]
def get_cropped(rectangle: Rectangle, img):
img_cropped = img[
int(rectangle.top_left[0]):int(rectangle.get_bottom_right()[0]) + 1,
int(rectangle.top_left[1]):int(rectangle.get_bottom_right()[1]) + 1]
return img_cropped
def detect_text_gray(img: np.ndarray) -> (np.ndarray, Rectangle):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
kernel = np.ones((3, 3))
Ib = cv2.GaussianBlur(gray, (3, 3), 5)
gradient = cv2.morphologyEx(Ib, cv2.MORPH_GRADIENT, kernel)
re, th1 = cv2.threshold(gradient, 127, 255, cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(gradient, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2)
th3 = cv2.bitwise_and(th1, th2)
edges = cv2.dilate(th3, kernel)
edges1 = cv2.erode(edges, kernel)
cnts = cv2.findContours(edges1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# ret, labels = cv2.connectedComponents(cnts)
text = []
corner_left = gray.shape
corner_right = (0, 0)
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if (5 <= w <= 150) and (5 <= h <= 150):
text.append(c)
if cv2.norm((x, y)) < cv2.norm(corner_left):
corner_left = (x, y)
if cv2.norm((x + w, y + h)) > cv2.norm(corner_right):
corner_right = (x + w, y + h)
# cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
padding = int(0.07 * (cv2.norm(np.subtract(corner_right, corner_left))))
sub = np.subtract(corner_right, corner_left)
width = sub[0]
height = sub[1]
bounding = Rectangle(corner_left, width, height)
mask = np.ones(img.shape[:2], dtype=np.uint8) * 255
# cv2.rectangle(im, (corner_left[0] - padding, corner_left[1] - padding),
# (bounding.get_bottom_right()[0] + padding, bounding.get_bottom_right()[1] + padding), (0, 0, 0), -1)
corner_left = (corner_left[0] - padding, corner_left[1] - padding)
corner_right = (bounding.get_bottom_right()[0] + padding,
bounding.get_bottom_right()[1] + padding)
mask = cv2.rectangle(mask, corner_left, corner_right, (0, 0, 0), -1)
sub = np.subtract(corner_right, corner_left)
width = sub[0]
height = sub[1]
bounding = Rectangle(corner_left, width, height)
# cv2.imshow('mask',mask)
# plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
# plt.show()
return mask, bounding
def detect_text(img: np.ndarray) -> (np.ndarray, Rectangle):
im = img.copy()
im_yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV)
Y, U, V = cv2.split(im_yuv)
kernel = np.ones((3, 3))
Ib = cv2.GaussianBlur(Y, (3, 3), 5)
gradient = cv2.morphologyEx(Ib, cv2.MORPH_GRADIENT, kernel)
re, th1 = cv2.threshold(gradient, 126, 255, cv2.THRESH_BINARY)
edges = cv2.dilate(th1, kernel)
edges1 = cv2.erode(edges, kernel)
cnts = cv2.findContours(edges1, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# ret, labels = cv2.connectedComponents(cnts)
text = []
corner_left = Y.shape
corner_right = (0, 0)
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if (5 <= w <= 300) and (6 <= h <= 300):
text.append(c)
if cv2.norm((x, y)) < cv2.norm(corner_left):
corner_left = (x, y)
if cv2.norm((x + w, y + h)) > cv2.norm(corner_right):
corner_right = (x + w, y + h)
# cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
padding = int(0.07 * (cv2.norm(np.subtract(corner_right, corner_left))))
sub = np.subtract(corner_right, corner_left)
width = sub[0]
height = sub[1]
bounding = Rectangle(corner_left, width, height)
mask = np.ones(img.shape[:2], dtype=np.uint8) * 255
# cv2.rectangle(im, (corner_left[0] - padding, corner_left[1] - padding),
# (bounding.get_bottom_right()[0] + padding, bounding.get_bottom_right()[1] + padding), (0, 0, 0), -1)
corner_left = (corner_left[0] - padding, corner_left[1] - padding)
corner_right = (bounding.get_bottom_right()[0] + padding,
bounding.get_bottom_right()[1] + padding)
mask = cv2.rectangle(mask, corner_left, corner_right, (0, 0, 0), -1)
sub = np.subtract(corner_right, corner_left)
width = sub[0]
height = sub[1]
bounding = Rectangle(corner_left, width, height)
# cv2.imshow('mask',mask)
# plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
# plt.show()
return mask, bounding