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demo.py
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demo.py
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import cv2
import time
import tensorflow as tf
from model.pspunet import pspunet
from data_loader.display import create_mask
import numpy as np
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
tf.config.experimental.set_virtual_device_configuration(
gpus[0],
[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=500)])
except RuntimeError as e:
print(e)
cap= cv2.VideoCapture(YOUR_VIDEO_PATH)
IMG_WIDTH = 480
IMG_HEIGHT = 272
n_classes = 7
model = pspunet((IMG_HEIGHT, IMG_WIDTH ,3), n_classes)
model.load_weights("pspunet_weight.h5")
while True:
start= time.time()
try:
_,frame = cap.read()
frame = cv2.resize(frame, (IMG_WIDTH, IMG_HEIGHT))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = frame[tf.newaxis, ...]
frame = frame/255
except:
cv2.destroyAllWindows()
cap.release()
break
pre = model.predict(frame)
pre = create_mask(pre).numpy()
frame2 = frame/2
frame2[0][(pre==1).all(axis=2)] += [0, 0, 0] #""bike_lane_normal", "sidewalk_asphalt", "sidewalk_urethane""
frame2[0][(pre==2).all(axis=2)] += [0.5, 0.5,0] # "caution_zone_stairs", "caution_zone_manhole", "caution_zone_tree_zone", "caution_zone_grating", "caution_zone_repair_zone"]
frame2[0][(pre==3).all(axis=2)] += [0.2, 0.7, 0.5] #"alley_crosswalk","roadway_crosswalk"
frame2[0][(pre==4).all(axis=2)] += [0, 0.5, 0.5] #"braille_guide_blocks_normal", "braille_guide_blocks_damaged"
frame2[0][(pre==5).all(axis=2)] += [0, 0, 0.5] #"roadway_normal","alley_normal","alley_speed_bump", "alley_damaged""
frame2[0][(pre==6).all(axis=2)] += [0.5, 0, 0] #"sidewalk_blocks","sidewalk_cement" , "sidewalk_soil_stone", "sidewalk_damaged","sidewalk_other"
video = np.uint8(frame2)
print(1/(time.time()-start))
cv2.waitKey(1)