def process_image_v6(original_image):
processed_image1 = lap(original_image)
processed_image2 = find_traffic_light(original_image)
processed_image = cv2.add(processed_image1, processed_image2)
processed_image = cv2.normalize(processed_image,
None,
alpha=0,
beta=1,
norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_32F)
return processed_image
def process_image_2range(original_image):
original_image = cv2.normalize(original_image,
None,
alpha=0,
beta=1,
norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_32F)
processed_image1 = find_lines_inrange(original_image, 0, 0, 77, 255, 87,
255)
processed_image2 = find_traffic_light(original_image)
processed_image3 = find_lines_inrange(original_image, 98, 90, 123, 176,
255, 255)
processed_image = cv2.add(processed_image1, processed_image2)
processed_image = cv2.add(processed_image, processed_image3)
return processed_image
def process_image_v3(original_image):
processed_image1 = transform2(original_image)
processed_image2 = find_traffic_light(original_image)
processed_image = cv2.add(processed_image1, processed_image2)
return processed_image
def process_image_v2(original_image):
processed_image1 = proc_screen(original_image)
processed_image2 = find_traffic_light(original_image)
processed_image = cv2.add(processed_image1, processed_image2)
return processed_image
def process_image_v1(original_image):
Y = original_image * 1
minimap = Y[130:180, 224:295, :]
vertices_roi_minimap_warped = np.array([[300, 190], [228, 190], [300, 140],
[300, 190]])
pts1_mm = np.float32([[0, 0], [71, 0], [0, 50], [71, 50]]) # minimap
pts2_mm = np.float32([[229, 150], [300, 150], [229, 200], [300, 200]])
pts1 = np.float32([[140, 120], [160, 120], [90, 200], [210, 200]]) # road
pts2 = np.float32([[100, 0], [200, 0], [100, 200], [200, 200]])
M = cv2.getPerspectiveTransform(pts1, pts2)
M_m = cv2.getPerspectiveTransform(pts1_mm, pts2_mm)
warped_mm = cv2.warpPerspective(minimap, M_m, (300, 200))
warped = cv2.warpPerspective(original_image, M, (300, 200))
mask = np.full_like(warped, 255)
cv2.fillPoly(mask, [vertices_roi_minimap_warped], (0, 0, 0))
masked = cv2.bitwise_and(warped, mask)
# CANNY
canny_warped = cv2.GaussianBlur(masked, (3, 3), 0)
canny_warped = cv2.cvtColor(canny_warped, cv2.COLOR_RGB2GRAY)
canny_warped = cv2.Canny(canny_warped,
threshold1=80,
threshold2=100,
apertureSize=3)
# #FINDING LINES
minLineLength = 20
maxLineGap = 20
left_coordinate = []
right_coordinate = []
try:
lines = cv2.HoughLinesP(image=canny_warped,
rho=cv2.HOUGH_PROBABILISTIC,
theta=np.pi / 180,
threshold=25,
minLineLength=minLineLength,
maxLineGap=maxLineGap)
canny_warped = cv2.cvtColor(canny_warped, cv2.COLOR_GRAY2RGB)
if lines is not None:
for line in lines:
x1, y1, x2, y2 = line[0]
if y2 - y1 == 0:
continue
slope = (x2 - x1) / (y2 - y1)
angle = np.rad2deg(np.arctan(slope))
if abs(angle) <= 60 and slope < 0:
left_coordinate.append([x1, y1, x2, y2])
cv2.line(canny_warped, (x1, y1), (x2, y2), (0, 255, 0), 2)
elif abs(angle) <= 60 and slope > 0:
right_coordinate.append([x1, y1, x2, y2])
cv2.line(canny_warped, (x1, y1), (x2, y2), (255, 0, 0), 2)
except:
pass
canny_warped = cv2.fillPoly(canny_warped,
np.array([[[229, 150], [300, 150], [300, 200],
[229, 200]]]),
0) # закрашиваем место под цветную миникарту
# processed_image1 = cv2.add(canny_warped, warped_mm)
processed_image2 = find_traffic_light(original_image)
processed_image = cv2.add(canny_warped, processed_image2)
return processed_image
def process_image_lap(original_image):
processed_image1 = lap(original_image)
processed_image2 = find_traffic_light(original_image)
processed_image = cv2.add(processed_image1, processed_image2)
return processed_image