print('FRAME: {}'.format(frame_itt))
pic_name = 'output/{:05d}.png'.format(frame_itt)
img1 = img2
img2 = cam.read()[1]
tt_reading.append(time_tressing(t0, '\t\treading'))
# for skiping video
if frame_itt < 1360:
print('\tpass')
continue
T_v = frame_itt / 25 # - 120
Ta.append(T_v)
sa.append(speed_g[frame_itt + delta_fr])
angles_objects = []
boxes_main = []
time_start_itt = time.time()
boxes_out = nn.get_features(img1)
tt_nn.append(time_tressing(time_start_itt, '\t\tneural network'))
t0 = time.time()
if debug:
out = img1.copy()
for box in boxes_out:
cl_name = box[0]
pt1 = box[1]
pt2 = box[2]
if cl_name in vehicles_classes:
out = draw.draw_bounding_box(out, cl_name, draw.red, pt1, pt2,
3)
else:
out = draw.draw_bounding_box(out, cl_name, draw.blue, pt1, pt2,
3)
tt_draw_rec.append(time_tressing(t0, '\t\tbounding box drawing'))
import matplotlib.pyplot as plt
import draw
import neural_network as nn
import openpose_to_Json as op
#itt = 1
times = []
times_yolo = []
times_openpose = []
for itt in range(1, 15 + 1):
print('start itt =', itt)
T = time.time()
image = cv2.imread('input/img/i/cars&humans/{}.jpg'.format(itt))
frameWidth = image.shape[1]
frameHeight = image.shape[0]
t_y = time.time()
output_yolo = nn.get_features(image)
times_yolo.append(time.time() - t_y)
t_op = time.time()
output_opnps = op.getPose(image, True)
pp, ll = op.getPose(image)
times_openpose.append(time.time() - t_op)
yolo_result = image.copy()
for box in output_yolo:
name = box[0]
pt1 = box[1]
pt2 = box[2]
color = box[3]
yolo_result = draw.draw_bounding_box(yolo_result, name, color, pt1,
pt2)
yolo_result = op.drawPose(yolo_result, pp, ll, 3, 1)
cv2.imwrite('output/cars&humans/{}_yolo.png'.format(itt), yolo_result)