from os import listdir from os path import isfile join from collection

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from os import listdir
from os.path import isfile, join
from collections import deque
import math
import argparse
import numpy as np
import cv2
#определяем границы для красного и зеленого цвета
#красный цвет представляет из себя две области в пространстве HSV
lower_red = np.array([0, 70, 80], dtype = "uint8")
upper_red = np.array([19, 255, 255], dtype = "uint8")
lower_violet = np.array([160, 85, 110], dtype = "uint8")
upper_violet = np.array([180, 255, 255], dtype = "uint8")
#с зеленым все проще - он в центре диапазона
lower_green = np.array([53, 90, 90], dtype = "uint8")
upper_green = np.array([91, 255, 255], dtype = "uint8")
#путь по умолчанию к видеофайлам
mypath = 'C:\\Users\\dns\\Desktop\\tr\\'
f1 = open(mypath + 'res.txt', 'w')
f1.close()
#своя функция для рассчета дистанции - "велосипед", но дает выигрыш в скорости
def fastest_calc_dist(p1,p2):
return math.sqrt((p2[0] - p1[0]) ** 2 +
(p2[1] - p1[1]) ** 2 )
def find_traffic_light(video_file):
cap = cv2.VideoCapture(video_file)
frame_len = 7
semaphors_array = deque(maxlen=frame_len)
true_semaphors = []
offset = 10
x_mult_offset = 30
frame_num = 0
red_frame_array = deque(maxlen=2*frame_len)
green_frame_array = deque(maxlen=2*frame_len)
prev_frame = deque(maxlen=2*frame_len)
while(cap.isOpened()):
ret, frame = cap.read()
f1 = open(mypath + 'res.txt', 'a')
if frame is None:
if len(true_semaphors) == 0:
print(file_name, -1)
f1.write('%s %d\n' % (file_name, -1))
break
sem_frame = frame_num
frame_num = frame_num + 1
frame_h,frame_w, _ = frame.shape
crop_h, crop_w = int(0.7 * frame_h), int(0.8 * frame_w)
frame_crop = frame[0:crop_h,int((frame_w-crop_w)/2):int((frame_w+crop_w)/2)]
blurred = cv2.GaussianBlur(frame_crop, (7, 7), 0.5)
converted = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
red_mask = cv2.inRange(converted, lower_red, upper_red) + cv2.inRange(converted, lower_violet, upper_violet)
green_mask = cv2.inRange(converted, lower_green, upper_green)
if len(prev_frame) < 1:
prev_frame.appendleft(blurred)
frameDelta = cv2.absdiff(prev_frame[len(prev_frame)-1], blurred)
prev_frame.appendleft(blurred)
diff_red_mask = np.zeros(crop_h*crop_w, dtype = "uint8").reshape(crop_h,crop_w)
if len(red_frame_array) > 1 :
diff_red_mask = red_frame_array[len(red_frame_array) - 1] - red_mask
red_frame_array.appendleft(red_mask)
diff_green_mask = np.zeros(crop_h*crop_w, dtype = "uint8").reshape(crop_h,crop_w)
if len(green_frame_array) > 1 :
diff_green_mask = green_mask - green_frame_array[len(green_frame_array) - 1]
green_frame_array.appendleft(green_mask)
diff_red_mask = cv2.erode(diff_red_mask, None, iterations=1)
diff_red_mask = cv2.dilate(diff_red_mask, None, iterations=3)
ret, diff_red_mask = cv2.threshold(diff_red_mask,127,250,cv2.THRESH_BINARY)
diff_green_mask = cv2.erode(diff_green_mask, None, iterations=1)
diff_green_mask = cv2.dilate(diff_green_mask, None, iterations=3)
ret, diff_green_mask = cv2.threshold(diff_green_mask,127,250,cv2.THRESH_BINARY)
cnts_red = cv2.findContours(diff_red_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
cnts_green = cv2.findContours(diff_green_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
area = 0
red_circle_array = []
for red_contour in cnts_red:
red_circle = cv2.minEnclosingCircle(red_contour)
((red_contour_x, red_contour_y), red_contour_radius) = red_circle
min_radius = 2 + (red_contour_x/crop_h) * 3
max_radius = 35 + (red_contour_x/crop_h) * 30
if red_contour_radius > min_radius and red_contour_radius < max_radius:
new_circle = (red_circle, area)
red_circle_array.append(new_circle)
green_circle_array = []
for green_contour in cnts_green:
green_circle = cv2.minEnclosingCircle(green_contour)
((green_contour_x, green_contour_y), green_contour_radius) = green_circle
min_radius = 2 + (green_contour_x/crop_h) * 3
max_radius = 35 + (green_contour_y/crop_h) * 30
if green_contour_radius > min_radius and green_contour_radius < max_radius:
new_circle = (green_circle, area)
green_circle_array.append(new_circle)
semaphors = []
for red_circle in red_circle_array:
((red_x, red_y), red_contour_radius) = red_circle[0]
red_x = int(red_x)
red_y = int(red_y)
top = 0
left = 0
right = frameDelta.shape[1]
bottom = frameDelta.shape[0]
circle_offset = offset + red_contour_radius
if top < int(red_y - circle_offset):
top = int(red_y - circle_offset)
if left < int(red_x - (x_mult_offset*circle_offset)):
left = int(red_x - (x_mult_offset*circle_offset))
if bottom > int(red_y + circle_offset):
bottom = int(red_y + circle_offset)
if right > int(red_x + (x_mult_offset*circle_offset)):
right = int(red_x + (x_mult_offset*circle_offset))
red_crop = frameDelta[top:bottom,left:right,:]
total_move_green_red = np.sum(red_crop) - (380*red_contour_radius*red_contour_radius)
total_move = total_move_green_red / (red_crop.shape[0]*red_crop.shape[1])
for green_circle in green_circle_array:
dif_x = red_circle[0][0][0] - green_circle[0][0][0]
dif_y = red_circle[0][0][1] - green_circle[0][0][1]
dif_r = red_circle[0][1]/green_circle[0][1]
radius_red = red_circle[0][1]
max_dist_y = -5*(radius_red+green_circle[0][1])/2
if max_dist_y < -170:
max_dist_y = -170
if dif_r > 0.4 and dif_r < 2.5:
if dif_x < (radius_red/2) and dif_x > (-radius_red/2):
if dif_y > max_dist_y and dif_y < (-(radius_red+green_circle[0][1])/4) and dif_y < -7:
if total_move < 33:
new_semaphor = (red_circle[0], green_circle[0],total_move)
semaphors.append(new_semaphor)
for semaphor in semaphors:
true_semaphor = 0
frame_delta = 1
for last_semaphors in semaphors_array:
frame_delta = frame_delta + 1
for last_semaphor in last_semaphors:
distance_red = fastest_calc_dist(semaphor[0][0],last_semaphor[0][0])
distance_green = fastest_calc_dist(semaphor[1][0],last_semaphor[1][0])
if distance_red < (semaphor[0][1]*0.5) and distance_green < (semaphor[1][1]*0.5):
true_semaphor = true_semaphor + 1
if (frame_num - frame_delta) < sem_frame:
sem_frame = frame_num - frame_delta
break
if true_semaphor > 1:
true_semaphors.append(semaphor)
break
semaphors_array.appendleft(semaphors)
for semaphor in semaphors:
cv2.circle(frame_crop, (int(semaphor[0][0][0]), int(semaphor[0][0][1])), int(20),
(255, 0, 255), 2)
for red_circle in red_circle_array:
cv2.circle(frame_crop, (int(red_circle[0][0][0]), int(red_circle[0][0][1])), int(red_circle[0][1]),
(0, 0, 255), 2)
for green_circle in green_circle_array:
cv2.circle(frame_crop, (int(green_circle[0][0][0]), int(green_circle[0][0][1])), int(green_circle[0][1]),
(0, 255, 0), 2)
for semaphor in true_semaphors:
cv2.circle(frame_crop, (int(semaphor[0][0][0]), int(semaphor[0][0][1])), int(semaphor[0][1]),
(255, 0, 0), 2)
cv2.circle(frame_crop, (int(semaphor[1][0][0]), int(semaphor[1][0][1])), int(semaphor[1][1]),
(255, 255, 0), 2)
cv2.imshow("images", frame_crop)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if len(true_semaphors) > 0:
print(file_name, sem_frame)
f1.write('%s %05d\n' % (file_name, sem_frame))
break
cap.release()
f1.close()
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--folder", required=False, help="path to videos directory")
args = vars(ap.parse_args())
if args['folder'] is not None:
mypath = args['folder']
print(mypath)
#берем все файлы с разрешением .avi и начинаем работу с ними
file_list = [f for f in listdir(mypath) if isfile(join(mypath, f)) and f.split('.')[-1] == 'avi']
for file_name in file_list:
find_traffic_light(mypath + file_name)