def find_signs(image_path):
image = cv2.imread(image_path)
signs = analyze_signs_from_image(
image, method=DETECT_BY_CONTOUR) + analyze_signs_from_image(
image, method=DETECT_BY_SEGMENTS)
signs = remove_fully_contained_signs(signs)
rectangle_signs = []
for coordinates in signs:
wrapped = straighten_signs.four_point_transform(image, coordinates)
rectangle_signs.append(cv2.cvtColor(wrapped, cv2.COLOR_BGR2RGB))
signs_with_text = []
for sign in rectangle_signs:
if not get_text_from_image(sign) == "":
signs_with_text.append(sign)
imshow(sign, "Sign")
return signs_with_text
def find_contour(thresh):
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(contours)
print("Found " + str(len(contours)) + " contours.") if DEBUG else None
large_contours = []
for c in contours:
area = cv2.contourArea(c)
image_area = thresh.shape[0] * thresh.shape[1]
if MIN_SQUARE_AREA_RATIO * image_area < area < MAX_SQUARE_AREA_RATIO * image_area:
large_contours.append(c)
if DEBUG:
copy = cv2.cvtColor(thresh, cv2.COLOR_GRAY2RGB)
cv2.drawContours(copy, large_contours, -1, (0, 0, 255), 4)
imshow(copy, "contour")
print(str(len(large_contours)) + " of them are large enough.")
return large_contours
def analyze_signs_from_image(image, method):
processed = preprocess_image(image)
imshow(processed, "Processed")
masks_dict = colors.get_masks(processed)
squares = []
for color in COLORS:
current_channel = masks_dict[color]
processed = process_mask(current_channel, color=color)
if method == DETECT_BY_LINES:
signs = edge_detection.detect_squares_by_lines(processed)
elif method == DETECT_BY_SEGMENTS:
signs = edge_detection.detect_squares_by_segments(processed)
elif method == DETECT_BY_CONTOUR:
contour = contour_detection.find_contour(processed)
signs = contour_detection.get_squares(contour)
else:
print("Does not recognize method")
raise AttributeError
if not len(signs) == 0:
squares.append(signs)
if len(squares) == 0:
return []
return [item for sublist in squares for item in sublist]
def process_mask(image, color='Image'):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
imshow(gray, color)
open_width = int(np.ceil(image.shape[0] * OPEN_RATIO) // 2 * 2 + 1)
open_height = int(np.ceil(image.shape[1] * OPEN_RATIO) // 2 * 2 + 1)
open_kernel = np.ones((open_width, open_height), np.uint8)
img_open = cv2.morphologyEx(gray, cv2.MORPH_OPEN, open_kernel)
imshow(img_open, color + ' opening')
close_width = int(np.ceil(image.shape[0] * CLOSING_RATIO) // 2 * 2 + 1)
close_height = int(np.ceil(image.shape[1] * CLOSING_RATIO) // 2 * 2 + 1)
closing_kernel = np.ones((close_width, close_height), np.uint8)
img_no_line_gaps = cv2.morphologyEx(img_open, cv2.MORPH_CLOSE,
closing_kernel)
imshow(img_no_line_gaps, color + " closing")
return img_no_line_gaps
def analyze_image(image):
imshow(image, "Original")
signs_list = analyze_signs_from_image(image, method=DETECT_BY_SEGMENTS)
image = visualize.show_squares(image, signs_list)
imshow(image, "Final")
return image