分割网络
from openvino.inference_engine import IECore
import numpy as np
import time
import cv2 as cv
def ssd_video_demo():
ie = IECore()
for device in ie.available_devices:
print(device)
model_xml = "/home/bhc/BHC/model/intel/semantic-segmentation-adas-0001/FP16/semantic-segmentation-adas-0001.xml"
model_bin = "/home/bhc/BHC/model/intel/semantic-segmentation-adas-0001/FP16/semantic-segmentation-adas-0001.bin"
net = ie.read_network(model=model_xml, weights=model_bin)
input_blob = next(iter(net.input_info))
out_blob = next(iter(net.outputs))
n, c, h, w = net.input_info[input_blob].input_data.shape
print(n, c, h, w)
cap = cv.VideoCapture("2.mp4")
exec_net = ie.load_network(network=net, device_name="CPU")
while True:
ret, frame = cap.read()
if ret is not True:
break
image = cv.resize(frame, (w, h))
image = image.transpose(2, 0, 1)
inf_start = time.time()
res = exec_net.infer(inputs={input_blob:[image]})
inf_end = time.time() - inf_start
res = res[out_blob] #(1, 1, 1024, 2048) 1:每一个像素的类别的标签
res = np.squeeze(res, 0)
res = res.transpose(1, 2, 0)
# res = np.squeeze(res, 2)
hh, ww, c = res.shape
res = res.reshape((hh, ww))
mask = np.zeros((hh, ww, 3), dtype=np.uint8)
for i in range(20): #20个类别,每个对象进行颜色变更
mask[np.where(res > i)] = (np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255))
mask = cv.resize(mask, (frame.shape[1], frame.shape[0]))
result = cv.addWeighted(frame, 0.5, mask, 0.5, 0) #进行融合
cv.putText(result, "infer time(ms): %.3f, FPS: %.2f"%(inf_end*1000, 1/(inf_end+0.0001)), (10, 50),
cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 255), 2, 8)
cv.imshow("semantic segmentation Demo", result)
c = cv.waitKey(1)
if c == 27:
break
cv.waitKey(0)
cv.destroyAllWindows()
if __name__ == "__main__":
ssd_video_demo()
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