def detect_plates_in_scene(img_original_scene):
list_of_possible_plates = [] # this will be the return value
height, width, num_of_channels = img_original_scene.shape
img_contours = np.zeros((height, width, 3), np.uint8)
cv2.destroyAllWindows()
img_grayscale_scene, img_thresh_scene = Preprocess.preprocess(
img_original_scene)
list_of_possible_chars_in_scene = find_possible_chars_in_scene(
img_thresh_scene)
list_of_lists_of_matching_chars = DetectChars.find_list_of_lists_of_matching_chars(
list_of_possible_chars_in_scene)
for list_of_matching_chars in list_of_lists_of_matching_chars:
random_blue = random.randint(0, 255)
random_green = random.randint(0, 255)
random_red = random.randint(0, 255)
contours = []
for matching_char in list_of_matching_chars:
contours.append(matching_char.contour)
cv2.drawContours(img_contours, contours, -1,
(random_blue, random_green, random_red))
for list_of_matching_chars in list_of_lists_of_matching_chars:
possible_plate = extract_plate(img_original_scene,
list_of_matching_chars)
if possible_plate.imgPlate is not None: # if plate was found
list_of_possible_plates.append(possible_plate)
# end if
# end for
print("\n" + str(len(list_of_possible_plates)) + " possible plates found")
for i in range(0, len(list_of_possible_plates)):
rect_points = cv2.boxPoints(
list_of_possible_plates[i].rrLocationOfPlateInScene)
cv2.line(img_contours, tuple(rect_points[0]), tuple(rect_points[1]),
PlateRecognition.SCALAR_RED, 2)
cv2.line(img_contours, tuple(rect_points[1]), tuple(rect_points[2]),
PlateRecognition.SCALAR_RED, 2)
cv2.line(img_contours, tuple(rect_points[2]), tuple(rect_points[3]),
PlateRecognition.SCALAR_RED, 2)
cv2.line(img_contours, tuple(rect_points[3]), tuple(rect_points[0]),
PlateRecognition.SCALAR_RED, 2)
cv2.imshow("4b", list_of_possible_plates[i].imgPlate)
cv2.waitKey(0)
return list_of_possible_plates
def detect_plates_in_scene(img_original_scene) -> [PossiblePlate]:
if img_original_scene is None:
print("\n### Error: image not found ### \n\n")
os.system("pause")
return
list_of_possible_plates: [PossiblePlate] = []
height, width, num_channels = img_original_scene.shape
# Creo las matrices vacias del tamaño de la imagen
img_grayscale_scene = np.zeros((height, width, 1), np.uint8)
img_thresh_scene = np.zeros((height, width, 1), np.uint8)
img_contours = np.zeros((height, width, 3), np.uint8)
# Proceso el INPUT y obtengo un grayScale y un Threshold.
img_grayscale_scene, img_thresh_scene = Preprocess.preprocess(img_original_scene)
# Guardo como jpg el grayScale y Threshold.
if SAVE_IMAGE:
cv2.imwrite("./output/showImage/grayscale.jpg", img_grayscale_scene)
cv2.imwrite("./output/showImage/thresh.jpg", img_thresh_scene)
# Muestro el grayScale y Threshold.
if SHOW_IMAGE:
cv2.imshow("GrayScaleImage", img_grayscale_scene)
cv2.imshow("AdaptativeThresholdImage", img_thresh_scene)
# Busco todos los posibles chars
# Primero buscamos todos los contornos, y despues los filtramos por los que pueden ser chars
# Sin comparar contra otros
if SHOW_TIME:
start_time_2 = time.time()
list_of_possible_chars_in_scene = find_possible_chars_in_scene(img_thresh_scene)
finish_time_2 = time.time() - start_time_2
print("--- Find possible chars: %s seconds ---" % finish_time_2)
else:
list_of_possible_chars_in_scene: [PossibleChar] = find_possible_chars_in_scene(img_thresh_scene)
# Busco grupos de posibles chars
list_of_lists_of_matching_chars_in_scene = DetectChars.find_list_of_lists_of_matching_chars(
list_of_possible_chars_in_scene)
# Dibujo los contornos a los posibles chars
if not SUPER_SPEED_MODE:
for list_of_matching_chars in list_of_lists_of_matching_chars_in_scene:
int_random_blue = random.randint(0, 255)
int_random_green = random.randint(0, 255)
int_random_red = random.randint(0, 255)
contours = []
for matching_char in list_of_matching_chars:
contours.append(matching_char.contour)
cv2.drawContours(img_contours, contours, -1, (int_random_blue, int_random_green, int_random_red))
if SHOW_IMAGE:
cv2.imshow("5- Posibles contornos", img_contours)
for list_of_matching_chars in list_of_lists_of_matching_chars_in_scene:
possible_plate = extract_plate(img_original_scene, list_of_matching_chars)
if possible_plate.imgPlate is not None:
list_of_possible_plates.append(possible_plate)
if NO_ERROR_PRINT_ENABLED:
print("\n" + str(len(list_of_possible_plates)) + " possible plates found" + "\n")
for i in range(0, len(list_of_possible_plates)):
p2f_rect_points = cv2.boxPoints(list_of_possible_plates[i].rrLocationOfPlateInScene)
cv2.line(img_contours, tuple(p2f_rect_points[0]), tuple(p2f_rect_points[1]), SCALAR_RED, 2)
cv2.line(img_contours, tuple(p2f_rect_points[1]), tuple(p2f_rect_points[2]), SCALAR_RED, 2)
cv2.line(img_contours, tuple(p2f_rect_points[2]), tuple(p2f_rect_points[3]), SCALAR_RED, 2)
cv2.line(img_contours, tuple(p2f_rect_points[3]), tuple(p2f_rect_points[0]), SCALAR_RED, 2)
if SHOW_IMAGE:
cv2.imshow("Posibles patentes " + str(i), img_contours)
if SAVE_IMAGE:
cv2.imwrite("./output/showImage/PosiblesPatentes.jpg", img_contours)
if NO_ERROR_PRINT_ENABLED:
print("possible plate " + str(i))
print("\n### Plate detection complete ###\n")
return list_of_possible_plates
