我正在开发一个手语识别系统,它可以从网络摄像头中获取实时视频。我使用了两个系统来协调整个系统。第一个系统给出手语使用者手的关键点和骨架,第二个系统获取关键点和骨架数据,并对其进行分析,给出手语使用者想要说的话。所以我的问题是将关键点和骨架视频文件传递给分析部分。我不知道我的问题有多严重,但我急需帮助
我已经尝试了在互联网上找到的几种方法,但这些方法似乎都不管用
cv2.imshow(“输出骨架”,帧) #imwrite(“video_output/{:03d}.jpg.”格式(k),帧)
key = cv2.waitKey(1)
if key == 27:
break
print("total = {}".format(time.time() - t))
vid_writer.write(frame)
def predict(image_data):
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg/contents:0': image_data})
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
max_score = 0.0
res = ''
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
if score > max_score:
max_score = score
res = human_string
return res, max_score
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line in tf.gfile.GFile("logs/trained_labels.txt")]
# Unpersists graph from file
with tf.gfile.FastGFile("logs/trained_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:
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
c = 0
input_source = cv2.imshow('frame',frame1)
cap = cv2.VideoCapture(input_source)
res, score = '', 0.0
i = 0
mem = ''
consecutive = 0
sequence = ''
目前没有回答
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