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tpb1908 / GitTesting

A repository for testing Git features and the Git API
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Multiple code blocks in line #49

Open tpb1908 opened 7 years ago

tpb1908 commented 7 years ago 
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"
# Convert to RGB
print "Converting frame to RGB"
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Align face
print "Aligning face"
alignedFace = self.aligner.align(96, frame, skipMulti=True) # skip image if more than one face is detected

# Extract features
if alignedFace is not None:
    print "Initializing neural net"
    with openface.TorchNeuralNet(model="./data/nn4.small2.v1.t7") as net:
        print "Starting forward pass"
        features = net.forward(alignedFace)

        print features
else:
    print "No face found"