def draw_bounding_box_on_image(current_frame_number,
image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=4,
display_str_list=(),
use_normalized_coordinates=True):
"""Adds a bounding box to an image.
Each string in display_str_list is displayed on a separate line above the
bounding box in black text on a rectangle filled with the input 'color'.
If the top of the bounding box extends to the edge of the image, the strings
are displayed below the bounding box.
Args:
image: a PIL.Image object.
ymin: ymin of bounding box.
xmin: xmin of bounding box.
ymax: ymax of bounding box.
xmax: xmax of bounding box.
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list: list of strings to display in box
(each to be shown on its own line).
use_normalized_coordinates: If True (default), treat coordinates
ymin, xmin, ymax, xmax as relative to the image. Otherwise treat
coordinates as absolute.
"""
csv_line = "" # to create new csv line consists of vehicle type, predicted_speed, color and predicted_direction
update_csv = False # update csv for a new vehicle that are passed from ROI - just one new line for each vehicles
is_vehicle_detected = [0]
draw = ImageDraw.Draw(image)
im_width, im_height = image.size
if use_normalized_coordinates:
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
else:
(left, right, top, bottom) = (xmin, xmax, ymin, ymax)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top),
(left, top)],
width=thickness,
fill=color)
predicted_speed = "n.a." # means not available, it is just initialization
predicted_direction = "n.a." # means not available, it is just initialization
image_temp = numpy.array(image)
detected_vehicle_image = image_temp[int(top):int(bottom),
int(left):int(right)]
if (
bottom > ROI_POSITION
): # if the vehicle get in ROI area, vehicle predicted_speed predicted_color algorithms are called - 200 is an arbitrary value, for my case it looks very well to set position of ROI line at y pixel 200
predicted_direction, predicted_speed, is_vehicle_detected, update_csv = speed_prediction.predict_speed(
top, bottom, right, left, current_frame_number,
detected_vehicle_image, ROI_POSITION)
predicted_color = color_recognition_api.color_recognition(
detected_vehicle_image)
try:
font = ImageFont.truetype('arial.ttf', 16)
except IOError:
font = ImageFont.load_default()
# If the total height of the display strings added to the top of the bounding
# box exceeds the top of the image, stack the strings below the bounding box
# instead of above.
display_str_list[0] = predicted_color + " " + display_str_list[0]
csv_line = predicted_color + "," + str(predicted_direction) + "," + str(
predicted_speed) # csv line created
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
# Each display_str has a top and bottom margin of 0.05x.
total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)
if top > total_display_str_height:
text_bottom = top
else:
text_bottom = bottom + total_display_str_height
# Reverse list and print from bottom to top.
for display_str in display_str_list[::-1]:
text_width, text_height = font.getsize(display_str)
margin = np.ceil(0.05 * text_height)
draw.rectangle([(left, text_bottom - text_height - 2 * margin),
(left + text_width, text_bottom)],
fill=color)
draw.text((left + margin, text_bottom - text_height - margin),
display_str,
fill='black',
font=font)
text_bottom -= text_height - 2 * margin
return is_vehicle_detected, csv_line, update_csv
i.setDone()
if i.timedOut():
index=carsVelocity.index(i)
carsVelocity.pop(index)
del i
if new==True: #If nothing is detected,create new
p=vehicles.Car(pid,cx,cy,max_p_age)
carsVelocity.append(p)
pid+=1
"""
#cv2.circle(frame,(cx,cy),5,(0,0,255),-1)
#img=cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
if (fline.is_passing(cy, cx)):
crop_vehicle = frame[y:y + h, x:x + w]
color = color_recognition_api.color_recognition(
crop_vehicle)
cv2.imwrite("firstPass.jpg", crop_vehicle)
#cv2.imshow("cropped_first", crop_vehicle)
if (sline.is_passing(cy, cx)):
crop_vehicle = frame[y + int(h / 2):y + h, x:x + w]
#color = color_recognition_api.color_recognition(crop_vehicle)
#cv2.imshow("cropped_second", crop_vehicle)
cv2.imwrite("secondPass.jpg", crop_vehicle)
"""licPlate = Main.main(frame2)
if(licPlate != ""):
cv2.imwrite("detected.jpg", crop_vehicle)
file = open("LicensePlates.txt", "a")
file.write("{}\n".format(licPlate))
file.close()
print("Detected license plate: " + str(licPlate) + "\n")"""
####Tracking######
m = cv2.moments(cnt)
cx = int(m['m10'] / m['m00'])
cy = int(m['m01'] / m['m00'])
x, y, w, h = cv2.boundingRect(cnt)
crop_vehicle = frame[y:y + h, x:x + w]
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
result, plateCoordinates = Main.main(crop_vehicle)
if result != "":
print("License Plate: " + str(result) + "\n")
color = color_recognition_api.color_recognition(
frame[int(plateCoordinates[2][1] -
150):int(plateCoordinates[0][1] - 100),
int(plateCoordinates[1][0]
):int(plateCoordinates[3][0])])
print("Color: " + str(color) + "\n")
print(
"-----------------------------------------------------------------\n"
)
if previous != result:
print("Send...\n")
r = requests.post(
"https://fathomless-plains-27484.herokuapp.com/api/v1/s3M5aCMtypyas8fs1VPHhw/passage",
data={
'car_num': result,
'color': color,
'camera_id': 1
def draw_bounding_box_on_image(current_frame_number,
image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=4,
display_str_list=(),
use_normalized_coordinates=True):
csv_line = ""
update_csv = False # update csv for a new vehicle that are passed from ROI - just one new line for each vehicles
is_vehicle_detected = [0]
draw = ImageDraw.Draw(image)
im_width, im_height = image.size
if use_normalized_coordinates:
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
else:
(left, right, top, bottom) = (xmin, xmax, ymin, ymax)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top),
(left, top)],
width=thickness,
fill=color)
image_temp = numpy.array(image)
detected_vehicle_image = image_temp[int(top):int(bottom),
int(left):int(right)]
if (bottom > ROI_POSITION_TOP and bottom < ROI_POSITION_BOTTOM and right <
(ROI_POSITION_RIGHT + ERROR_FOCTOR_RL) and left >
(ROI_POSITION_LEFT - ERROR_FOCTOR_RL)):
is_vehicle_detected = counting.predict_count(
top, bottom, right, left, current_frame_number,
detected_vehicle_image, ROI_POSITION_TOP, ROI_POSITION_BOTTOM,
ROI_POSITION_RIGHT, ROI_POSITION_LEFT, ERROR_FOCTOR_BT)
predicted_color = color_recognition_api.color_recognition(
detected_vehicle_image)
try:
font = ImageFont.truetype('arial.ttf', 16)
except IOError:
font = ImageFont.load_default()
# If the total height of the display strings added to the top of the bounding
# box exceeds the top of the image, stack the strings below the bounding box
# instead of above.
display_str_list[0] = predicted_color + " " + display_str_list[0]
csv_line = predicted_color
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
# Each display_str has a top and bottom margin of 0.05x.
total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)
if top > total_display_str_height:
text_bottom = top
else:
text_bottom = bottom + total_display_str_height
# Reverse list and print from bottom to top.
for display_str in display_str_list[::-1]:
text_width, text_height = font.getsize(display_str)
margin = np.ceil(0.05 * text_height)
draw.rectangle([(left, text_bottom - text_height - 2 * margin),
(left + text_width, text_bottom)],
fill=color)
draw.text((left + margin, text_bottom - text_height - margin),
display_str,
fill='black',
font=font)
text_bottom -= text_height - 2 * margin
return is_vehicle_detected