add trained model and predict alg

RNN/label.txt

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+001 002 043 044 045 046 054 159 160 161 

RNN/predict.py

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+import os
+import numpy as np
+import tensorflow as tf
+import argparse
+
+def load_data(dirname):
+    if dirname[-1] != '/':
+        dirname=dirname+'/'
+    listfile=os.listdir(dirname)
+    X = []
+    Y = []
+    for text in listfile:
+        textname = dirname + text
+        numbers=[]
+        with open(textname, mode = 'r') as t:
+            numbers = [float(num) for num in t.read().split()]
+            for i in range(len(numbers),25200):
+                numbers.extend([0.000])
+        landmark_frame=[]
+        row=0
+        for i in range(0,70):
+            landmark_frame.extend(numbers[row:row+84])
+            row += 84
+        landmark_frame=np.array(landmark_frame)
+        landmark_frame=landmark_frame.reshape(-1,84)
+        X.append(np.array(landmark_frame))
+        Y.append(text)
+    X = np.array(X)
+    Y = np.array(Y)
+    # print(Y)
+    x_train = X
+    x_train=np.array(x_train)
+    return x_train,Y
+
+def load_label():
+    listfile=[]
+    with open("label.txt",mode='r') as l:
+        listfile=[i for i in l.read().split()]
+    label = {}
+    count = 1
+    for l in listfile:
+        if "_" in l:
+            continue
+        label[l] = count
+        count += 1
+    return label
+    
+def main(input_data_path, output_data_path):
+    comp = 'bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 \
+    mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu'
+
+    cmd = 'GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_cpu \
+    --calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt'
+
+    output_dir = output_data_path
+    x_test, Y = load_data(output_dir)
+    new_model = tf.keras.models.load_model('model.h5')
+    # new_model.summary()
+
+    labels=load_label()
+    xhat = x_test
+    print(xhat)
+
+    yhat = new_model.predict(xhat)
+
+    print(yhat)
+    predictions = np.array([np.argmax(pred) for pred in yhat])
+    rev_labels = dict(zip(list(labels.values()), list(labels.keys())))
+    s=0
+    filel = np.array(Y)
+    txtpath=output_data_path+"result.txt" 
+    with open(txtpath, "w") as f:
+        for i in predictions:
+            f.write(Y[s])
+            f.write(" ")
+            f.write(rev_labels[i])
+            f.write("\n")
+            s+=1
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Predict Sign language with Mediapipe')
+    parser.add_argument("--input_data_path",help=" ")
+    parser.add_argument("--output_data_path",help=" ")
+    args = parser.parse_args()
+    input_data_path = args.input_data_path
+    output_data_path = args.output_data_path
+    main(input_data_path,output_data_path)