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cam_label_image1.py
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cam_label_image1.py
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import tensorflow as tf, sys
import cv2
import time
import urllib.request
import numpy as np
stream = urllib.request.urlopen('http://192.168.43.139:8090/test.mjpg')
print(type(stream))
bytes = bytes()
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line
in tf.gfile.GFile("retrained_labels.txt")]
# Unpersists graph from file
with tf.gfile.FastGFile("retrained_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
while True:
start = time.time()
urllib.request.urlcleanup()
bytes += stream.read(1024)
a = bytes.find(b'\xff\xd8')
b = bytes.find(b'\xff\xd9')
if a != -1 and b != -1:
jpg = bytes[a:b+2]
bytes = bytes[b+2:]
i = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8), cv2.IMREAD_COLOR)
frame = i
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg:0': frame})
#print predictions
#print predictions.shape
#print type(predictions)
#cv2.imwrite("conv_1.jpg",predictions)
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))
print((time.time()-start))
cv2.imshow('i', i)
if cv2.waitKey(1) == 27:
exit(0)