def get_masked_images(img, contours):
mask = np.zeros(img.shape[:2], dtype="uint8")
filtered_contours = []
for c in contours:
ps = math.pow(cv2.arcLength(c, True), 2)
circularity = 0
area = 0
if ps > 0:
area = cv2.contourArea(c)
circularity = (4 * math.pi * area) / ps
if circularity > 0.08 and area > 30:
filtered_contours.append(c)
masks = []
united_contours = features_util.combine_contours(filtered_contours, 20)
for c in united_contours:
sub_mask = mask.copy()
cv2.drawContours(sub_mask, c, -1, (255, 255, 255), -1)
x1, y1, w, h = features_util.largest_bounding_rect(c)
cropped = sub_mask[y1:y1 + h, x1:x1 + w]
masks.append(cropped)
return masks
def get_masked_images(img, x_dist):
contours = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[1]
# Sort contours by x value.
contours.sort(key=lambda c: features_util.mid_bbox(cv2.boundingRect(c))[0])
# Combine contours that are less than 'x_dist' apart from each on the x-axis (or 30 on the y).
contours = features_util.combine_contours(contours, x_dist, 40)
#contours = features_util.combine_contours_better(filtered_contours, x_dist, 30)
masks = []
for c in contours:
mask = np.zeros(img.shape, "uint8")
cv2.drawContours(mask, c, -1, (255, 255, 255), -1)
min_y, max_y, min_x, max_x = features_util.crop_to_content(mask,
padding=2)
mask = mask[min_y:max_y, min_x:max_x]
masks.append(mask)
# Split masks into two, if they are over '24' pixels in width.
# This is because some letters are too close to each other for the contours
# to be seperated.
filtered_masks = []
for mask in masks:
if mask.shape[1] > 24:
mask1 = mask[:, 0:12]
mask2 = mask[:, 12:]
filtered_masks.append(mask1)
filtered_masks.append(mask2)
elif mask.shape[0] > 12 or mask.shape[1] > 12:
filtered_masks.append(mask)
return filtered_masks
def segment_image(img):
contours = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0]
contours.sort(key=lambda c: features_util.mid_bbox(cv2.boundingRect(c))[0])
contours = features_util.combine_contours(contours, 3, 20)
for c in contours:
if 15 < cv2.contourArea(c[0]) < 35:
return True
return False
def get_contour_positions(img):
contours = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0]
contours = features_util.combine_contours(contours, 3)
bboxes = [features_util.largest_bounding_rect(c) for c in contours]
bboxes.sort(key=lambda x: x[2],
reverse=True) # Sort by width of contour bbox.
bboxes = [features_util.mid_bbox(bbox) for bbox in bboxes]
return (bboxes[0], bboxes[1])
def segment_image(img):
contours = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0]
filtered_contours = []
for c in contours:
area = cv2.contourArea(c)
if area < 1000:
filtered_contours.append(c)
contours = filtered_contours
contours.sort(key=lambda c: features_util.mid_bbox(cv2.boundingRect(c))[0])
contours = features_util.combine_contours(contours, 5, 30)
masks = []
for c in contours:
mask = np.zeros(img.shape[:2], dtype="uint8")
cv2.drawContours(mask, c, -1, (255, 255, 255), -1)
masks.append(mask)
return masks