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