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1 changed files with 78 additions and 24 deletions
102
__dev/test.py
102
__dev/test.py
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@ -250,25 +250,25 @@ def umeyama(src, dst, estimate_scale):
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return T
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mean_face_x = np.array([
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0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
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0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
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0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
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0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
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0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
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0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
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0.553364, 0.490127, 0.42689 ])
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mean_face_y = np.array([
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0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
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0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
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0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
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0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
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0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
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0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
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0.784792, 0.824182, 0.831803, 0.824182 ])
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landmarks_2D = np.stack( [ mean_face_x, mean_face_y ], axis=1 )
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#mean_face_x = np.array([
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#0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
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#0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
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#0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
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#0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
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#0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
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#0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
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#0.553364, 0.490127, 0.42689 ])
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#
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#mean_face_y = np.array([
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#0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
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#0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
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#0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
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#0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
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#0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
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#0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
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#0.784792, 0.824182, 0.831803, 0.824182 ])
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#
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#landmarks_2D = np.stack( [ mean_face_x, mean_face_y ], axis=1 )
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def get_transform_mat (image_landmarks, output_size, scale=1.0):
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if not isinstance(image_landmarks, np.ndarray):
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@ -306,11 +306,7 @@ def get_transform_mat (image_landmarks, output_size, scale=1.0):
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import mathlib
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def main():
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def f ( *args, asd=True, **kwargs ):
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import code
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code.interact(local=dict(globals(), **locals()))
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f( 1, asd=True, bg=0)
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from nnlib import nnlib
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exec( nnlib.import_all( device_config=nnlib.device.Config() ), locals(), globals() )
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@ -1357,6 +1353,64 @@ O[i0, i1, i2, i3: (1 + 1 - 1)/1, (64 + 1 - 1)/1, (64 + 2 - 1)/2, (1 + 1 - 1)/1]
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if __name__ == "__main__":
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#import os
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#os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
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#os.environ["PLAIDML_DEVICE_IDS"] = "opencl_nvidia_geforce_gtx_1060_6gb.0"
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#import keras
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#import numpy as np
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#import cv2
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#import time
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#K = keras.backend
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#
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#
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#
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#PNet_Input = keras.layers.Input ( (None, None,3) )
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#x = PNet_Input
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#x = keras.layers.Conv2D (10, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
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#x = keras.layers.PReLU (shared_axes=[1,2], name="PReLU1" )(x)
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#x = keras.layers.MaxPooling2D( pool_size=(2,2), strides=(2,2), padding='same' ) (x)
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#x = keras.layers.Conv2D (16, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
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#x = keras.layers.PReLU (shared_axes=[1,2], name="PReLU2" )(x)
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#x = keras.layers.Conv2D (32, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv3")(x)
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#x = keras.layers.PReLU (shared_axes=[1,2], name="PReLU3" )(x)
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#prob = keras.layers.Conv2D (2, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv41")(x)
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#x = keras.layers.Conv2D (4, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv42")(x)
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#
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#pnet = K.function ([PNet_Input], [x,prob] )
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#
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#img = np.random.uniform ( size=(1920,1920,3) )
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#minsize=80
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#factor=0.95
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#factor_count=0
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#h=img.shape[0]
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#w=img.shape[1]
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#
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#minl=np.amin([h, w])
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#m=12.0/minsize
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#minl=minl*m
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## create scale pyramid
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#scales=[]
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#while minl>=12:
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# scales += [m*np.power(factor, factor_count)]
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# minl = minl*factor
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# factor_count += 1
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# # first stage
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# for scale in scales:
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# hs=int(np.ceil(h*scale))
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# ws=int(np.ceil(w*scale))
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# im_data = cv2.resize(img, (ws, hs), interpolation=cv2.INTER_LINEAR)
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# im_data = (im_data-127.5)*0.0078125
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# img_x = np.expand_dims(im_data, 0)
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# img_x = np.transpose(img_x, (0,2,1,3))
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# t = time.time()
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# out = pnet([img_x])
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# t = time.time() - t
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# print (img_x.shape, t)
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#
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#import code
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#code.interact(local=dict(globals(), **locals()))
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#os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
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#os.environ["PLAIDML_DEVICE_IDS"] = "opencl_nvidia_geforce_gtx_1060_6gb.0"
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#import keras
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