House Detection¶
M. Fawcett - 10/16/2021
This Python program uses YOLO and bounding boxes to detect houses in images downladed from Google Street View.
Main Cite: https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/
Image Annotation Software: https://towardsdatascience.com/annotate-your-image-using-online-annotation-tool-52d0a742daff
How to Download Specific Open Image Classes: https://github.com/EscVM/OIDv4_ToolKit
Additional Images: https://storage.googleapis.com/openimages/web/download_v5.html
Also see:
Cite: https://towardsdatascience.com/object-detection-using-google-ai-open-images-4c908cad4a54
Cite: https://towardsdatascience.com/yolo-v5-object-detection-tutorial-2e607b9013ef
Cite: https://www.analyticsvidhya.com/blog/2021/08/train-your-own-yolov5-object-detection-model/
Cite: https://analyticsindiamag.com/hands-on-guide-to-object-detection-using-yolo/
Cite: https://github.com/OlafenwaMoses/ImageAI
In [ ]:
!pip install wget
import wget
In [ ]:
# cite: https://stackoverflow.com/questions/45293933/could-not-find-a-version-that-satisfies-the-requirement-opencv-python
# !pip install opencv-python
import cv2
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!pip install --upgrade pip
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!pip3 install https://github.com/OlafenwaMoses/ImageAI/releases/download/2.0.2/imageai-2.0.2-py3-none-any.whl
In [ ]:
wget.download("https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/yolo.h5", "yolo.h5")
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import tensorflow as tf
print(tf.__version__)
import keras as keras
In [ ]:
!pip install imageAI
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#Library
from imageai.Detection import ObjectDetection
import os
import time
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LABELS = ['Shirt', 'Trousers', 'Swimwear', 'Tie', 'Bus', 'Truck', 'Train', 'Motorcycle', 'Helmet', 'Shorts', 'Airplane',
'Sunglasses', 'Jacket', 'Dress', 'Human eye', 'Suit', 'Footwear', 'Woman', 'Human face', 'Man', 'Human arm',
'Human head','Human hand', 'Human leg', 'Human nose', 'Human mouth', 'Human ear', 'Human beard', 'Human foot', 'Car',
'Wheel', 'Boat', 'House', 'Bird', 'Guitar', 'Fast food', 'Hat', 'Dog', 'Laptop', 'Beer', 'Cat', 'Lantern', 'Fountain']
In [ ]:
labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck",
"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana",
"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator",
"book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
#
In [ ]:
LABELS = ['Shirt', 'Trousers', 'Swimwear', 'Tie', 'Bus', 'Truck', 'Train', 'Motorcycle', 'Helmet', 'Shorts', 'Airplane',
'Sunglasses', 'Jacket', 'Dress', 'Human eye', 'Suit', 'Footwear', 'Woman', 'Human face', 'Man', 'Human arm',
'Human head','Human hand', 'Human leg', 'Human nose', 'Human mouth', 'Human ear', 'Human beard', 'Human foot', 'Car',
'Wheel', 'Boat', 'House', 'Bird', 'Guitar', 'Fast food', 'Hat', 'Dog', 'Laptop', 'Beer', 'Cat', 'Lantern', 'Fountain']
In [ ]:
# define the model
model = make_yolov3_model()
In [1]:
# Cite: Jason Brownlee
# create a YOLOv3 Keras model and save it to file
# based on https://github.com/experiencor/keras-yolo3
import struct
import numpy as np
from keras.layers import Conv2D
from keras.layers import Input
from keras.layers import BatchNormalization
from keras.layers import LeakyReLU
from keras.layers import ZeroPadding2D
from keras.layers import UpSampling2D
from keras.layers.merge import add, concatenate
from keras.models import Model
def _conv_block(inp, convs, skip=True):
x = inp
count = 0
for conv in convs:
if count == (len(convs) - 2) and skip:
skip_connection = x
count += 1
if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet prefer left and top
x = Conv2D(conv['filter'],
conv['kernel'],
strides=conv['stride'],
padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet prefer left and top
name='conv_' + str(conv['layer_idx']),
use_bias=False if conv['bnorm'] else True)(x)
if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)
if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)
return add([skip_connection, x]) if skip else x
def make_yolov3_model():
input_image = Input(shape=(None, None, 3))
# Layer 0 => 4
x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},
{'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 1},
{'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 2},
{'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])
# Layer 5 => 8
x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},
{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 6},
{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])
# Layer 9 => 11
x = _conv_block(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},
{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])
# Layer 12 => 15
x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},
{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 13},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])
# Layer 16 => 36
for i in range(7):
x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])
skip_36 = x
# Layer 37 => 40
x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},
{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 38},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])
# Layer 41 => 61
for i in range(7):
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])
skip_61 = x
# Layer 62 => 65
x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 63},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])
# Layer 66 => 74
for i in range(3):
x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])
# Layer 75 => 79
x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 76},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 77},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}], skip=False)
# Layer 80 => 82
yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 80},
{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}], skip=False)
# Layer 83 => 86
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}], skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_61])
# Layer 87 => 91
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 88},
{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 89},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 90},
{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 91}], skip=False)
# Layer 92 => 94
yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 92},
{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}], skip=False)
# Layer 95 => 98
x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 96}], skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_36])
# Layer 99 => 106
yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 99},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 100},
{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 101},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 102},
{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 103},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 104},
{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 105}], skip=False)
model = Model(input_image, [yolo_82, yolo_94, yolo_106])
return model
class WeightReader:
def __init__(self, weight_file):
with open(weight_file, 'rb') as w_f:
major, = struct.unpack('i', w_f.read(4))
minor, = struct.unpack('i', w_f.read(4))
revision, = struct.unpack('i', w_f.read(4))
if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:
w_f.read(8)
else:
w_f.read(4)
transpose = (major > 1000) or (minor > 1000)
binary = w_f.read()
self.offset = 0
self.all_weights = np.frombuffer(binary, dtype='float32')
def read_bytes(self, size):
self.offset = self.offset + size
return self.all_weights[self.offset-size:self.offset]
def load_weights(self, model):
for i in range(106):
try:
conv_layer = model.get_layer('conv_' + str(i))
print("loading weights of convolution #" + str(i))
if i not in [81, 93, 105]:
norm_layer = model.get_layer('bnorm_' + str(i))
size = np.prod(norm_layer.get_weights()[0].shape)
beta = self.read_bytes(size) # bias
gamma = self.read_bytes(size) # scale
mean = self.read_bytes(size) # mean
var = self.read_bytes(size) # variance
weights = norm_layer.set_weights([gamma, beta, mean, var])
if len(conv_layer.get_weights()) > 1:
bias = self.read_bytes(np.prod(conv_layer.get_weights()[1].shape))
kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2,3,1,0])
conv_layer.set_weights([kernel, bias])
else:
kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2,3,1,0])
conv_layer.set_weights([kernel])
except ValueError:
print("no convolution #" + str(i))
def reset(self):
self.offset = 0
# define the model
model = make_yolov3_model()
# load the model weights
weight_reader = WeightReader('yolov3.weights')
# set the model weights into the model
weight_reader.load_weights(model)
# save the model to file
model.save('model.h5')
loading weights of convolution #0 loading weights of convolution #1 loading weights of convolution #2 loading weights of convolution #3 no convolution #4 loading weights of convolution #5 loading weights of convolution #6 loading weights of convolution #7 no convolution #8 loading weights of convolution #9 loading weights of convolution #10 no convolution #11 loading weights of convolution #12 loading weights of convolution #13 loading weights of convolution #14 no convolution #15 loading weights of convolution #16 loading weights of convolution #17 no convolution #18 loading weights of convolution #19 loading weights of convolution #20 no convolution #21 loading weights of convolution #22 loading weights of convolution #23 no convolution #24 loading weights of convolution #25 loading weights of convolution #26 no convolution #27 loading weights of convolution #28 loading weights of convolution #29 no convolution #30 loading weights of convolution #31 loading weights of convolution #32 no convolution #33 loading weights of convolution #34 loading weights of convolution #35 no convolution #36 loading weights of convolution #37 loading weights of convolution #38 loading weights of convolution #39 no convolution #40 loading weights of convolution #41 loading weights of convolution #42 no convolution #43 loading weights of convolution #44 loading weights of convolution #45 no convolution #46 loading weights of convolution #47 loading weights of convolution #48 no convolution #49 loading weights of convolution #50 loading weights of convolution #51 no convolution #52 loading weights of convolution #53 loading weights of convolution #54 no convolution #55 loading weights of convolution #56 loading weights of convolution #57 no convolution #58 loading weights of convolution #59 loading weights of convolution #60 no convolution #61 loading weights of convolution #62 loading weights of convolution #63 loading weights of convolution #64 no convolution #65 loading weights of convolution #66 loading weights of convolution #67 no convolution #68 loading weights of convolution #69 loading weights of convolution #70 no convolution #71 loading weights of convolution #72 loading weights of convolution #73 no convolution #74 loading weights of convolution #75 loading weights of convolution #76 loading weights of convolution #77 loading weights of convolution #78 loading weights of convolution #79 loading weights of convolution #80 loading weights of convolution #81 no convolution #82 no convolution #83 loading weights of convolution #84 no convolution #85 no convolution #86 loading weights of convolution #87 loading weights of convolution #88 loading weights of convolution #89 loading weights of convolution #90 loading weights of convolution #91 loading weights of convolution #92 loading weights of convolution #93 no convolution #94 no convolution #95 loading weights of convolution #96 no convolution #97 no convolution #98 loading weights of convolution #99 loading weights of convolution #100 loading weights of convolution #101 loading weights of convolution #102 loading weights of convolution #103 loading weights of convolution #104 loading weights of convolution #105 WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
/Users/mitchellfawcett/anaconda3/lib/python3.7/site-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument. category=CustomMaskWarning)
In [10]:
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
conv_0 (Conv2D) (None, None, None, 3 864 input_1[0][0]
__________________________________________________________________________________________________
bnorm_0 (BatchNormalization) (None, None, None, 3 128 conv_0[0][0]
__________________________________________________________________________________________________
leaky_0 (LeakyReLU) (None, None, None, 3 0 bnorm_0[0][0]
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D) (None, None, None, 3 0 leaky_0[0][0]
__________________________________________________________________________________________________
conv_1 (Conv2D) (None, None, None, 6 18432 zero_padding2d[0][0]
__________________________________________________________________________________________________
bnorm_1 (BatchNormalization) (None, None, None, 6 256 conv_1[0][0]
__________________________________________________________________________________________________
leaky_1 (LeakyReLU) (None, None, None, 6 0 bnorm_1[0][0]
__________________________________________________________________________________________________
conv_2 (Conv2D) (None, None, None, 3 2048 leaky_1[0][0]
__________________________________________________________________________________________________
bnorm_2 (BatchNormalization) (None, None, None, 3 128 conv_2[0][0]
__________________________________________________________________________________________________
leaky_2 (LeakyReLU) (None, None, None, 3 0 bnorm_2[0][0]
__________________________________________________________________________________________________
conv_3 (Conv2D) (None, None, None, 6 18432 leaky_2[0][0]
__________________________________________________________________________________________________
bnorm_3 (BatchNormalization) (None, None, None, 6 256 conv_3[0][0]
__________________________________________________________________________________________________
leaky_3 (LeakyReLU) (None, None, None, 6 0 bnorm_3[0][0]
__________________________________________________________________________________________________
add (Add) (None, None, None, 6 0 leaky_1[0][0]
leaky_3[0][0]
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, None, None, 6 0 add[0][0]
__________________________________________________________________________________________________
conv_5 (Conv2D) (None, None, None, 1 73728 zero_padding2d_1[0][0]
__________________________________________________________________________________________________
bnorm_5 (BatchNormalization) (None, None, None, 1 512 conv_5[0][0]
__________________________________________________________________________________________________
leaky_5 (LeakyReLU) (None, None, None, 1 0 bnorm_5[0][0]
__________________________________________________________________________________________________
conv_6 (Conv2D) (None, None, None, 6 8192 leaky_5[0][0]
__________________________________________________________________________________________________
bnorm_6 (BatchNormalization) (None, None, None, 6 256 conv_6[0][0]
__________________________________________________________________________________________________
leaky_6 (LeakyReLU) (None, None, None, 6 0 bnorm_6[0][0]
__________________________________________________________________________________________________
conv_7 (Conv2D) (None, None, None, 1 73728 leaky_6[0][0]
__________________________________________________________________________________________________
bnorm_7 (BatchNormalization) (None, None, None, 1 512 conv_7[0][0]
__________________________________________________________________________________________________
leaky_7 (LeakyReLU) (None, None, None, 1 0 bnorm_7[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, None, None, 1 0 leaky_5[0][0]
leaky_7[0][0]
__________________________________________________________________________________________________
conv_9 (Conv2D) (None, None, None, 6 8192 add_1[0][0]
__________________________________________________________________________________________________
bnorm_9 (BatchNormalization) (None, None, None, 6 256 conv_9[0][0]
__________________________________________________________________________________________________
leaky_9 (LeakyReLU) (None, None, None, 6 0 bnorm_9[0][0]
__________________________________________________________________________________________________
conv_10 (Conv2D) (None, None, None, 1 73728 leaky_9[0][0]
__________________________________________________________________________________________________
bnorm_10 (BatchNormalization) (None, None, None, 1 512 conv_10[0][0]
__________________________________________________________________________________________________
leaky_10 (LeakyReLU) (None, None, None, 1 0 bnorm_10[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, None, None, 1 0 add_1[0][0]
leaky_10[0][0]
__________________________________________________________________________________________________
zero_padding2d_2 (ZeroPadding2D (None, None, None, 1 0 add_2[0][0]
__________________________________________________________________________________________________
conv_12 (Conv2D) (None, None, None, 2 294912 zero_padding2d_2[0][0]
__________________________________________________________________________________________________
bnorm_12 (BatchNormalization) (None, None, None, 2 1024 conv_12[0][0]
__________________________________________________________________________________________________
leaky_12 (LeakyReLU) (None, None, None, 2 0 bnorm_12[0][0]
__________________________________________________________________________________________________
conv_13 (Conv2D) (None, None, None, 1 32768 leaky_12[0][0]
__________________________________________________________________________________________________
bnorm_13 (BatchNormalization) (None, None, None, 1 512 conv_13[0][0]
__________________________________________________________________________________________________
leaky_13 (LeakyReLU) (None, None, None, 1 0 bnorm_13[0][0]
__________________________________________________________________________________________________
conv_14 (Conv2D) (None, None, None, 2 294912 leaky_13[0][0]
__________________________________________________________________________________________________
bnorm_14 (BatchNormalization) (None, None, None, 2 1024 conv_14[0][0]
__________________________________________________________________________________________________
leaky_14 (LeakyReLU) (None, None, None, 2 0 bnorm_14[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, None, None, 2 0 leaky_12[0][0]
leaky_14[0][0]
__________________________________________________________________________________________________
conv_16 (Conv2D) (None, None, None, 1 32768 add_3[0][0]
__________________________________________________________________________________________________
bnorm_16 (BatchNormalization) (None, None, None, 1 512 conv_16[0][0]
__________________________________________________________________________________________________
leaky_16 (LeakyReLU) (None, None, None, 1 0 bnorm_16[0][0]
__________________________________________________________________________________________________
conv_17 (Conv2D) (None, None, None, 2 294912 leaky_16[0][0]
__________________________________________________________________________________________________
bnorm_17 (BatchNormalization) (None, None, None, 2 1024 conv_17[0][0]
__________________________________________________________________________________________________
leaky_17 (LeakyReLU) (None, None, None, 2 0 bnorm_17[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, None, None, 2 0 add_3[0][0]
leaky_17[0][0]
__________________________________________________________________________________________________
conv_19 (Conv2D) (None, None, None, 1 32768 add_4[0][0]
__________________________________________________________________________________________________
bnorm_19 (BatchNormalization) (None, None, None, 1 512 conv_19[0][0]
__________________________________________________________________________________________________
leaky_19 (LeakyReLU) (None, None, None, 1 0 bnorm_19[0][0]
__________________________________________________________________________________________________
conv_20 (Conv2D) (None, None, None, 2 294912 leaky_19[0][0]
__________________________________________________________________________________________________
bnorm_20 (BatchNormalization) (None, None, None, 2 1024 conv_20[0][0]
__________________________________________________________________________________________________
leaky_20 (LeakyReLU) (None, None, None, 2 0 bnorm_20[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, None, None, 2 0 add_4[0][0]
leaky_20[0][0]
__________________________________________________________________________________________________
conv_22 (Conv2D) (None, None, None, 1 32768 add_5[0][0]
__________________________________________________________________________________________________
bnorm_22 (BatchNormalization) (None, None, None, 1 512 conv_22[0][0]
__________________________________________________________________________________________________
leaky_22 (LeakyReLU) (None, None, None, 1 0 bnorm_22[0][0]
__________________________________________________________________________________________________
conv_23 (Conv2D) (None, None, None, 2 294912 leaky_22[0][0]
__________________________________________________________________________________________________
bnorm_23 (BatchNormalization) (None, None, None, 2 1024 conv_23[0][0]
__________________________________________________________________________________________________
leaky_23 (LeakyReLU) (None, None, None, 2 0 bnorm_23[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, None, None, 2 0 add_5[0][0]
leaky_23[0][0]
__________________________________________________________________________________________________
conv_25 (Conv2D) (None, None, None, 1 32768 add_6[0][0]
__________________________________________________________________________________________________
bnorm_25 (BatchNormalization) (None, None, None, 1 512 conv_25[0][0]
__________________________________________________________________________________________________
leaky_25 (LeakyReLU) (None, None, None, 1 0 bnorm_25[0][0]
__________________________________________________________________________________________________
conv_26 (Conv2D) (None, None, None, 2 294912 leaky_25[0][0]
__________________________________________________________________________________________________
bnorm_26 (BatchNormalization) (None, None, None, 2 1024 conv_26[0][0]
__________________________________________________________________________________________________
leaky_26 (LeakyReLU) (None, None, None, 2 0 bnorm_26[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, None, None, 2 0 add_6[0][0]
leaky_26[0][0]
__________________________________________________________________________________________________
conv_28 (Conv2D) (None, None, None, 1 32768 add_7[0][0]
__________________________________________________________________________________________________
bnorm_28 (BatchNormalization) (None, None, None, 1 512 conv_28[0][0]
__________________________________________________________________________________________________
leaky_28 (LeakyReLU) (None, None, None, 1 0 bnorm_28[0][0]
__________________________________________________________________________________________________
conv_29 (Conv2D) (None, None, None, 2 294912 leaky_28[0][0]
__________________________________________________________________________________________________
bnorm_29 (BatchNormalization) (None, None, None, 2 1024 conv_29[0][0]
__________________________________________________________________________________________________
leaky_29 (LeakyReLU) (None, None, None, 2 0 bnorm_29[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, None, None, 2 0 add_7[0][0]
leaky_29[0][0]
__________________________________________________________________________________________________
conv_31 (Conv2D) (None, None, None, 1 32768 add_8[0][0]
__________________________________________________________________________________________________
bnorm_31 (BatchNormalization) (None, None, None, 1 512 conv_31[0][0]
__________________________________________________________________________________________________
leaky_31 (LeakyReLU) (None, None, None, 1 0 bnorm_31[0][0]
__________________________________________________________________________________________________
conv_32 (Conv2D) (None, None, None, 2 294912 leaky_31[0][0]
__________________________________________________________________________________________________
bnorm_32 (BatchNormalization) (None, None, None, 2 1024 conv_32[0][0]
__________________________________________________________________________________________________
leaky_32 (LeakyReLU) (None, None, None, 2 0 bnorm_32[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, None, None, 2 0 add_8[0][0]
leaky_32[0][0]
__________________________________________________________________________________________________
conv_34 (Conv2D) (None, None, None, 1 32768 add_9[0][0]
__________________________________________________________________________________________________
bnorm_34 (BatchNormalization) (None, None, None, 1 512 conv_34[0][0]
__________________________________________________________________________________________________
leaky_34 (LeakyReLU) (None, None, None, 1 0 bnorm_34[0][0]
__________________________________________________________________________________________________
conv_35 (Conv2D) (None, None, None, 2 294912 leaky_34[0][0]
__________________________________________________________________________________________________
bnorm_35 (BatchNormalization) (None, None, None, 2 1024 conv_35[0][0]
__________________________________________________________________________________________________
leaky_35 (LeakyReLU) (None, None, None, 2 0 bnorm_35[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, None, None, 2 0 add_9[0][0]
leaky_35[0][0]
__________________________________________________________________________________________________
zero_padding2d_3 (ZeroPadding2D (None, None, None, 2 0 add_10[0][0]
__________________________________________________________________________________________________
conv_37 (Conv2D) (None, None, None, 5 1179648 zero_padding2d_3[0][0]
__________________________________________________________________________________________________
bnorm_37 (BatchNormalization) (None, None, None, 5 2048 conv_37[0][0]
__________________________________________________________________________________________________
leaky_37 (LeakyReLU) (None, None, None, 5 0 bnorm_37[0][0]
__________________________________________________________________________________________________
conv_38 (Conv2D) (None, None, None, 2 131072 leaky_37[0][0]
__________________________________________________________________________________________________
bnorm_38 (BatchNormalization) (None, None, None, 2 1024 conv_38[0][0]
__________________________________________________________________________________________________
leaky_38 (LeakyReLU) (None, None, None, 2 0 bnorm_38[0][0]
__________________________________________________________________________________________________
conv_39 (Conv2D) (None, None, None, 5 1179648 leaky_38[0][0]
__________________________________________________________________________________________________
bnorm_39 (BatchNormalization) (None, None, None, 5 2048 conv_39[0][0]
__________________________________________________________________________________________________
leaky_39 (LeakyReLU) (None, None, None, 5 0 bnorm_39[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, None, None, 5 0 leaky_37[0][0]
leaky_39[0][0]
__________________________________________________________________________________________________
conv_41 (Conv2D) (None, None, None, 2 131072 add_11[0][0]
__________________________________________________________________________________________________
bnorm_41 (BatchNormalization) (None, None, None, 2 1024 conv_41[0][0]
__________________________________________________________________________________________________
leaky_41 (LeakyReLU) (None, None, None, 2 0 bnorm_41[0][0]
__________________________________________________________________________________________________
conv_42 (Conv2D) (None, None, None, 5 1179648 leaky_41[0][0]
__________________________________________________________________________________________________
bnorm_42 (BatchNormalization) (None, None, None, 5 2048 conv_42[0][0]
__________________________________________________________________________________________________
leaky_42 (LeakyReLU) (None, None, None, 5 0 bnorm_42[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, None, None, 5 0 add_11[0][0]
leaky_42[0][0]
__________________________________________________________________________________________________
conv_44 (Conv2D) (None, None, None, 2 131072 add_12[0][0]
__________________________________________________________________________________________________
bnorm_44 (BatchNormalization) (None, None, None, 2 1024 conv_44[0][0]
__________________________________________________________________________________________________
leaky_44 (LeakyReLU) (None, None, None, 2 0 bnorm_44[0][0]
__________________________________________________________________________________________________
conv_45 (Conv2D) (None, None, None, 5 1179648 leaky_44[0][0]
__________________________________________________________________________________________________
bnorm_45 (BatchNormalization) (None, None, None, 5 2048 conv_45[0][0]
__________________________________________________________________________________________________
leaky_45 (LeakyReLU) (None, None, None, 5 0 bnorm_45[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, None, None, 5 0 add_12[0][0]
leaky_45[0][0]
__________________________________________________________________________________________________
conv_47 (Conv2D) (None, None, None, 2 131072 add_13[0][0]
__________________________________________________________________________________________________
bnorm_47 (BatchNormalization) (None, None, None, 2 1024 conv_47[0][0]
__________________________________________________________________________________________________
leaky_47 (LeakyReLU) (None, None, None, 2 0 bnorm_47[0][0]
__________________________________________________________________________________________________
conv_48 (Conv2D) (None, None, None, 5 1179648 leaky_47[0][0]
__________________________________________________________________________________________________
bnorm_48 (BatchNormalization) (None, None, None, 5 2048 conv_48[0][0]
__________________________________________________________________________________________________
leaky_48 (LeakyReLU) (None, None, None, 5 0 bnorm_48[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, None, None, 5 0 add_13[0][0]
leaky_48[0][0]
__________________________________________________________________________________________________
conv_50 (Conv2D) (None, None, None, 2 131072 add_14[0][0]
__________________________________________________________________________________________________
bnorm_50 (BatchNormalization) (None, None, None, 2 1024 conv_50[0][0]
__________________________________________________________________________________________________
leaky_50 (LeakyReLU) (None, None, None, 2 0 bnorm_50[0][0]
__________________________________________________________________________________________________
conv_51 (Conv2D) (None, None, None, 5 1179648 leaky_50[0][0]
__________________________________________________________________________________________________
bnorm_51 (BatchNormalization) (None, None, None, 5 2048 conv_51[0][0]
__________________________________________________________________________________________________
leaky_51 (LeakyReLU) (None, None, None, 5 0 bnorm_51[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, None, None, 5 0 add_14[0][0]
leaky_51[0][0]
__________________________________________________________________________________________________
conv_53 (Conv2D) (None, None, None, 2 131072 add_15[0][0]
__________________________________________________________________________________________________
bnorm_53 (BatchNormalization) (None, None, None, 2 1024 conv_53[0][0]
__________________________________________________________________________________________________
leaky_53 (LeakyReLU) (None, None, None, 2 0 bnorm_53[0][0]
__________________________________________________________________________________________________
conv_54 (Conv2D) (None, None, None, 5 1179648 leaky_53[0][0]
__________________________________________________________________________________________________
bnorm_54 (BatchNormalization) (None, None, None, 5 2048 conv_54[0][0]
__________________________________________________________________________________________________
leaky_54 (LeakyReLU) (None, None, None, 5 0 bnorm_54[0][0]
__________________________________________________________________________________________________
add_16 (Add) (None, None, None, 5 0 add_15[0][0]
leaky_54[0][0]
__________________________________________________________________________________________________
conv_56 (Conv2D) (None, None, None, 2 131072 add_16[0][0]
__________________________________________________________________________________________________
bnorm_56 (BatchNormalization) (None, None, None, 2 1024 conv_56[0][0]
__________________________________________________________________________________________________
leaky_56 (LeakyReLU) (None, None, None, 2 0 bnorm_56[0][0]
__________________________________________________________________________________________________
conv_57 (Conv2D) (None, None, None, 5 1179648 leaky_56[0][0]
__________________________________________________________________________________________________
bnorm_57 (BatchNormalization) (None, None, None, 5 2048 conv_57[0][0]
__________________________________________________________________________________________________
leaky_57 (LeakyReLU) (None, None, None, 5 0 bnorm_57[0][0]
__________________________________________________________________________________________________
add_17 (Add) (None, None, None, 5 0 add_16[0][0]
leaky_57[0][0]
__________________________________________________________________________________________________
conv_59 (Conv2D) (None, None, None, 2 131072 add_17[0][0]
__________________________________________________________________________________________________
bnorm_59 (BatchNormalization) (None, None, None, 2 1024 conv_59[0][0]
__________________________________________________________________________________________________
leaky_59 (LeakyReLU) (None, None, None, 2 0 bnorm_59[0][0]
__________________________________________________________________________________________________
conv_60 (Conv2D) (None, None, None, 5 1179648 leaky_59[0][0]
__________________________________________________________________________________________________
bnorm_60 (BatchNormalization) (None, None, None, 5 2048 conv_60[0][0]
__________________________________________________________________________________________________
leaky_60 (LeakyReLU) (None, None, None, 5 0 bnorm_60[0][0]
__________________________________________________________________________________________________
add_18 (Add) (None, None, None, 5 0 add_17[0][0]
leaky_60[0][0]
__________________________________________________________________________________________________
zero_padding2d_4 (ZeroPadding2D (None, None, None, 5 0 add_18[0][0]
__________________________________________________________________________________________________
conv_62 (Conv2D) (None, None, None, 1 4718592 zero_padding2d_4[0][0]
__________________________________________________________________________________________________
bnorm_62 (BatchNormalization) (None, None, None, 1 4096 conv_62[0][0]
__________________________________________________________________________________________________
leaky_62 (LeakyReLU) (None, None, None, 1 0 bnorm_62[0][0]
__________________________________________________________________________________________________
conv_63 (Conv2D) (None, None, None, 5 524288 leaky_62[0][0]
__________________________________________________________________________________________________
bnorm_63 (BatchNormalization) (None, None, None, 5 2048 conv_63[0][0]
__________________________________________________________________________________________________
leaky_63 (LeakyReLU) (None, None, None, 5 0 bnorm_63[0][0]
__________________________________________________________________________________________________
conv_64 (Conv2D) (None, None, None, 1 4718592 leaky_63[0][0]
__________________________________________________________________________________________________
bnorm_64 (BatchNormalization) (None, None, None, 1 4096 conv_64[0][0]
__________________________________________________________________________________________________
leaky_64 (LeakyReLU) (None, None, None, 1 0 bnorm_64[0][0]
__________________________________________________________________________________________________
add_19 (Add) (None, None, None, 1 0 leaky_62[0][0]
leaky_64[0][0]
__________________________________________________________________________________________________
conv_66 (Conv2D) (None, None, None, 5 524288 add_19[0][0]
__________________________________________________________________________________________________
bnorm_66 (BatchNormalization) (None, None, None, 5 2048 conv_66[0][0]
__________________________________________________________________________________________________
leaky_66 (LeakyReLU) (None, None, None, 5 0 bnorm_66[0][0]
__________________________________________________________________________________________________
conv_67 (Conv2D) (None, None, None, 1 4718592 leaky_66[0][0]
__________________________________________________________________________________________________
bnorm_67 (BatchNormalization) (None, None, None, 1 4096 conv_67[0][0]
__________________________________________________________________________________________________
leaky_67 (LeakyReLU) (None, None, None, 1 0 bnorm_67[0][0]
__________________________________________________________________________________________________
add_20 (Add) (None, None, None, 1 0 add_19[0][0]
leaky_67[0][0]
__________________________________________________________________________________________________
conv_69 (Conv2D) (None, None, None, 5 524288 add_20[0][0]
__________________________________________________________________________________________________
bnorm_69 (BatchNormalization) (None, None, None, 5 2048 conv_69[0][0]
__________________________________________________________________________________________________
leaky_69 (LeakyReLU) (None, None, None, 5 0 bnorm_69[0][0]
__________________________________________________________________________________________________
conv_70 (Conv2D) (None, None, None, 1 4718592 leaky_69[0][0]
__________________________________________________________________________________________________
bnorm_70 (BatchNormalization) (None, None, None, 1 4096 conv_70[0][0]
__________________________________________________________________________________________________
leaky_70 (LeakyReLU) (None, None, None, 1 0 bnorm_70[0][0]
__________________________________________________________________________________________________
add_21 (Add) (None, None, None, 1 0 add_20[0][0]
leaky_70[0][0]
__________________________________________________________________________________________________
conv_72 (Conv2D) (None, None, None, 5 524288 add_21[0][0]
__________________________________________________________________________________________________
bnorm_72 (BatchNormalization) (None, None, None, 5 2048 conv_72[0][0]
__________________________________________________________________________________________________
leaky_72 (LeakyReLU) (None, None, None, 5 0 bnorm_72[0][0]
__________________________________________________________________________________________________
conv_73 (Conv2D) (None, None, None, 1 4718592 leaky_72[0][0]
__________________________________________________________________________________________________
bnorm_73 (BatchNormalization) (None, None, None, 1 4096 conv_73[0][0]
__________________________________________________________________________________________________
leaky_73 (LeakyReLU) (None, None, None, 1 0 bnorm_73[0][0]
__________________________________________________________________________________________________
add_22 (Add) (None, None, None, 1 0 add_21[0][0]
leaky_73[0][0]
__________________________________________________________________________________________________
conv_75 (Conv2D) (None, None, None, 5 524288 add_22[0][0]
__________________________________________________________________________________________________
bnorm_75 (BatchNormalization) (None, None, None, 5 2048 conv_75[0][0]
__________________________________________________________________________________________________
leaky_75 (LeakyReLU) (None, None, None, 5 0 bnorm_75[0][0]
__________________________________________________________________________________________________
conv_76 (Conv2D) (None, None, None, 1 4718592 leaky_75[0][0]
__________________________________________________________________________________________________
bnorm_76 (BatchNormalization) (None, None, None, 1 4096 conv_76[0][0]
__________________________________________________________________________________________________
leaky_76 (LeakyReLU) (None, None, None, 1 0 bnorm_76[0][0]
__________________________________________________________________________________________________
conv_77 (Conv2D) (None, None, None, 5 524288 leaky_76[0][0]
__________________________________________________________________________________________________
bnorm_77 (BatchNormalization) (None, None, None, 5 2048 conv_77[0][0]
__________________________________________________________________________________________________
leaky_77 (LeakyReLU) (None, None, None, 5 0 bnorm_77[0][0]
__________________________________________________________________________________________________
conv_78 (Conv2D) (None, None, None, 1 4718592 leaky_77[0][0]
__________________________________________________________________________________________________
bnorm_78 (BatchNormalization) (None, None, None, 1 4096 conv_78[0][0]
__________________________________________________________________________________________________
leaky_78 (LeakyReLU) (None, None, None, 1 0 bnorm_78[0][0]
__________________________________________________________________________________________________
conv_79 (Conv2D) (None, None, None, 5 524288 leaky_78[0][0]
__________________________________________________________________________________________________
bnorm_79 (BatchNormalization) (None, None, None, 5 2048 conv_79[0][0]
__________________________________________________________________________________________________
leaky_79 (LeakyReLU) (None, None, None, 5 0 bnorm_79[0][0]
__________________________________________________________________________________________________
conv_84 (Conv2D) (None, None, None, 2 131072 leaky_79[0][0]
__________________________________________________________________________________________________
bnorm_84 (BatchNormalization) (None, None, None, 2 1024 conv_84[0][0]
__________________________________________________________________________________________________
leaky_84 (LeakyReLU) (None, None, None, 2 0 bnorm_84[0][0]
__________________________________________________________________________________________________
up_sampling2d (UpSampling2D) (None, None, None, 2 0 leaky_84[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, None, None, 7 0 up_sampling2d[0][0]
add_18[0][0]
__________________________________________________________________________________________________
conv_87 (Conv2D) (None, None, None, 2 196608 concatenate[0][0]
__________________________________________________________________________________________________
bnorm_87 (BatchNormalization) (None, None, None, 2 1024 conv_87[0][0]
__________________________________________________________________________________________________
leaky_87 (LeakyReLU) (None, None, None, 2 0 bnorm_87[0][0]
__________________________________________________________________________________________________
conv_88 (Conv2D) (None, None, None, 5 1179648 leaky_87[0][0]
__________________________________________________________________________________________________
bnorm_88 (BatchNormalization) (None, None, None, 5 2048 conv_88[0][0]
__________________________________________________________________________________________________
leaky_88 (LeakyReLU) (None, None, None, 5 0 bnorm_88[0][0]
__________________________________________________________________________________________________
conv_89 (Conv2D) (None, None, None, 2 131072 leaky_88[0][0]
__________________________________________________________________________________________________
bnorm_89 (BatchNormalization) (None, None, None, 2 1024 conv_89[0][0]
__________________________________________________________________________________________________
leaky_89 (LeakyReLU) (None, None, None, 2 0 bnorm_89[0][0]
__________________________________________________________________________________________________
conv_90 (Conv2D) (None, None, None, 5 1179648 leaky_89[0][0]
__________________________________________________________________________________________________
bnorm_90 (BatchNormalization) (None, None, None, 5 2048 conv_90[0][0]
__________________________________________________________________________________________________
leaky_90 (LeakyReLU) (None, None, None, 5 0 bnorm_90[0][0]
__________________________________________________________________________________________________
conv_91 (Conv2D) (None, None, None, 2 131072 leaky_90[0][0]
__________________________________________________________________________________________________
bnorm_91 (BatchNormalization) (None, None, None, 2 1024 conv_91[0][0]
__________________________________________________________________________________________________
leaky_91 (LeakyReLU) (None, None, None, 2 0 bnorm_91[0][0]
__________________________________________________________________________________________________
conv_96 (Conv2D) (None, None, None, 1 32768 leaky_91[0][0]
__________________________________________________________________________________________________
bnorm_96 (BatchNormalization) (None, None, None, 1 512 conv_96[0][0]
__________________________________________________________________________________________________
leaky_96 (LeakyReLU) (None, None, None, 1 0 bnorm_96[0][0]
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, None, None, 1 0 leaky_96[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, None, None, 3 0 up_sampling2d_1[0][0]
add_10[0][0]
__________________________________________________________________________________________________
conv_99 (Conv2D) (None, None, None, 1 49152 concatenate_1[0][0]
__________________________________________________________________________________________________
bnorm_99 (BatchNormalization) (None, None, None, 1 512 conv_99[0][0]
__________________________________________________________________________________________________
leaky_99 (LeakyReLU) (None, None, None, 1 0 bnorm_99[0][0]
__________________________________________________________________________________________________
conv_100 (Conv2D) (None, None, None, 2 294912 leaky_99[0][0]
__________________________________________________________________________________________________
bnorm_100 (BatchNormalization) (None, None, None, 2 1024 conv_100[0][0]
__________________________________________________________________________________________________
leaky_100 (LeakyReLU) (None, None, None, 2 0 bnorm_100[0][0]
__________________________________________________________________________________________________
conv_101 (Conv2D) (None, None, None, 1 32768 leaky_100[0][0]
__________________________________________________________________________________________________
bnorm_101 (BatchNormalization) (None, None, None, 1 512 conv_101[0][0]
__________________________________________________________________________________________________
leaky_101 (LeakyReLU) (None, None, None, 1 0 bnorm_101[0][0]
__________________________________________________________________________________________________
conv_102 (Conv2D) (None, None, None, 2 294912 leaky_101[0][0]
__________________________________________________________________________________________________
bnorm_102 (BatchNormalization) (None, None, None, 2 1024 conv_102[0][0]
__________________________________________________________________________________________________
leaky_102 (LeakyReLU) (None, None, None, 2 0 bnorm_102[0][0]
__________________________________________________________________________________________________
conv_103 (Conv2D) (None, None, None, 1 32768 leaky_102[0][0]
__________________________________________________________________________________________________
bnorm_103 (BatchNormalization) (None, None, None, 1 512 conv_103[0][0]
__________________________________________________________________________________________________
leaky_103 (LeakyReLU) (None, None, None, 1 0 bnorm_103[0][0]
__________________________________________________________________________________________________
conv_80 (Conv2D) (None, None, None, 1 4718592 leaky_79[0][0]
__________________________________________________________________________________________________
conv_92 (Conv2D) (None, None, None, 5 1179648 leaky_91[0][0]
__________________________________________________________________________________________________
conv_104 (Conv2D) (None, None, None, 2 294912 leaky_103[0][0]
__________________________________________________________________________________________________
bnorm_80 (BatchNormalization) (None, None, None, 1 4096 conv_80[0][0]
__________________________________________________________________________________________________
bnorm_92 (BatchNormalization) (None, None, None, 5 2048 conv_92[0][0]
__________________________________________________________________________________________________
bnorm_104 (BatchNormalization) (None, None, None, 2 1024 conv_104[0][0]
__________________________________________________________________________________________________
leaky_80 (LeakyReLU) (None, None, None, 1 0 bnorm_80[0][0]
__________________________________________________________________________________________________
leaky_92 (LeakyReLU) (None, None, None, 5 0 bnorm_92[0][0]
__________________________________________________________________________________________________
leaky_104 (LeakyReLU) (None, None, None, 2 0 bnorm_104[0][0]
__________________________________________________________________________________________________
conv_81 (Conv2D) (None, None, None, 2 261375 leaky_80[0][0]
__________________________________________________________________________________________________
conv_93 (Conv2D) (None, None, None, 2 130815 leaky_92[0][0]
__________________________________________________________________________________________________
conv_105 (Conv2D) (None, None, None, 2 65535 leaky_104[0][0]
==================================================================================================
Total params: 62,001,757
Trainable params: 61,949,149
Non-trainable params: 52,608
__________________________________________________________________________________________________
In [2]:
# From Jason Brownlee
# load yolov3 model and perform object detection
# based on https://github.com/experiencor/keras-yolo3
from numpy import expand_dims
from keras.models import load_model
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
# load and prepare an image
def load_image_pixels(filename, shape):
# load the image to get its shape
image = load_img(filename)
width, height = image.size
# load the image with the required size
image = load_img(filename, target_size=shape)
# convert to numpy array
image = img_to_array(image)
# scale pixel values to [0, 1]
image = image.astype('float32')
image /= 255.0
# add a dimension so that we have one sample
image = expand_dims(image, 0)
return image, width, height
# load yolov3 model
model = load_model('model.h5')
# define the expected input shape for the model
input_w, input_h = 416, 416
# define our new photo
photo_filename = 'zebra.jpg'
# load and prepare image
image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))
# make prediction
yhat = model.predict(image)
# summarize the shape of the list of arrays
print([a.shape for a in yhat])
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually. [(1, 13, 13, 255), (1, 26, 26, 255), (1, 52, 52, 255)]
In [13]:
# From Jason Brownlee
# load yolov3 model and perform object detection
# based on https://github.com/experiencor/keras-yolo3
import numpy as np
from numpy import expand_dims
from keras.models import load_model
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from matplotlib import pyplot
from matplotlib.patches import Rectangle
class BoundBox:
def __init__(self, xmin, ymin, xmax, ymax, objness = None, classes = None):
self.xmin = xmin
self.ymin = ymin
self.xmax = xmax
self.ymax = ymax
self.objness = objness
self.classes = classes
self.label = -1
self.score = -1
def get_label(self):
if self.label == -1:
self.label = np.argmax(self.classes)
return self.label
def get_score(self):
if self.score == -1:
self.score = self.classes[self.get_label()]
return self.score
def _sigmoid(x):
return 1. / (1. + np.exp(-x))
def decode_netout(netout, anchors, obj_thresh, net_h, net_w):
grid_h, grid_w = netout.shape[:2]
nb_box = 3
netout = netout.reshape((grid_h, grid_w, nb_box, -1))
nb_class = netout.shape[-1] - 5
boxes = []
netout[..., :2] = _sigmoid(netout[..., :2])
netout[..., 4:] = _sigmoid(netout[..., 4:])
netout[..., 5:] = netout[..., 4][..., np.newaxis] * netout[..., 5:]
netout[..., 5:] *= netout[..., 5:] > obj_thresh
for i in range(grid_h*grid_w):
row = i / grid_w
col = i % grid_w
for b in range(nb_box):
# 4th element is objectness score
objectness = netout[int(row)][int(col)][b][4]
if (objectness.all() <= obj_thresh): continue
# first 4 elements are x, y, w, and h
x, y, w, h = netout[int(row)][int(col)][b][:4]
x = (col + x) / grid_w # center position, unit: image width
y = (row + y) / grid_h # center position, unit: image height
w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width
h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height
# last elements are class probabilities
classes = netout[int(row)][col][b][5:]
box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)
boxes.append(box)
return boxes
def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):
new_w, new_h = net_w, net_h
for i in range(len(boxes)):
x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w
y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h
boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)
boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)
boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)
boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)
def _interval_overlap(interval_a, interval_b):
x1, x2 = interval_a
x3, x4 = interval_b
if x3 < x1:
if x4 < x1:
return 0
else:
return min(x2,x4) - x1
else:
if x2 < x3:
return 0
else:
return min(x2,x4) - x3
def bbox_iou(box1, box2):
intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])
intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])
intersect = intersect_w * intersect_h
w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin
w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin
union = w1*h1 + w2*h2 - intersect
return float(intersect) / union
def do_nms(boxes, nms_thresh):
if len(boxes) > 0:
nb_class = len(boxes[0].classes)
else:
return
for c in range(nb_class):
sorted_indices = np.argsort([-box.classes[c] for box in boxes])
for i in range(len(sorted_indices)):
index_i = sorted_indices[i]
if boxes[index_i].classes[c] == 0: continue
for j in range(i+1, len(sorted_indices)):
index_j = sorted_indices[j]
if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:
boxes[index_j].classes[c] = 0
# load and prepare an image
def load_image_pixels(filename, shape):
# load the image to get its shape
image = load_img(filename)
width, height = image.size
# load the image with the required size
image = load_img(filename, target_size=shape)
# convert to numpy array
image = img_to_array(image)
# scale pixel values to [0, 1]
image = image.astype('float32')
image /= 255.0
# add a dimension so that we have one sample
image = expand_dims(image, 0)
return image, width, height
# get all of the results above a threshold
def get_boxes(boxes, labels, thresh):
v_boxes, v_labels, v_scores = list(), list(), list()
# enumerate all boxes
for box in boxes:
# enumerate all possible labels
for i in range(len(labels)):
# check if the threshold for this label is high enough
if box.classes[i] > thresh:
v_boxes.append(box)
v_labels.append(labels[i])
v_scores.append(box.classes[i]*100)
# don't break, many labels may trigger for one box
return v_boxes, v_labels, v_scores
# draw all results
def draw_boxes(filename, v_boxes, v_labels, v_scores):
# load the image
data = pyplot.imread(filename)
# plot the image
pyplot.imshow(data)
# get the context for drawing boxes
ax = pyplot.gca()
# plot each box
for i in range(len(v_boxes)):
box = v_boxes[i]
# get coordinates
y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax
# calculate width and height of the box
width, height = x2 - x1, y2 - y1
# create the shape
rect = Rectangle((x1, y1), width, height, fill=False, color='red')
# draw the box
ax.add_patch(rect)
# draw text and score in top left corner
label = "%s (%.3f)" % (v_labels[i], v_scores[i])
pyplot.text(x1, y1, label, color='black')
# show the plot
pyplot.show()
# load yolov3 model
model = load_model('model.h5')
# define the expected input shape for the model
input_w, input_h = 416, 416
# define our new photo
photo_filename = 'testphoto.jpg'
# load and prepare image
image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))
# make prediction
yhat = model.predict(image)
# summarize the shape of the list of arrays
print([a.shape for a in yhat])
# define the anchors
anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]
# define the probability threshold for detected objects
class_threshold = 0.6
boxes = list()
for i in range(len(yhat)):
# decode the output of the network
boxes += decode_netout(yhat[i][0], anchors[i], class_threshold, input_h, input_w)
# correct the sizes of the bounding boxes for the shape of the image
correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)
# suppress non-maximal boxes
do_nms(boxes, 0.5)
# define the labels
labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck",
"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana",
"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator",
"book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
# get the details of the detected objects
v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)
# summarize what we found
for i in range(len(v_boxes)):
print(v_labels[i], v_scores[i])
# draw what we found
draw_boxes(photo_filename, v_boxes, v_labels, v_scores)
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually. [(1, 13, 13, 255), (1, 26, 26, 255), (1, 52, 52, 255)] car 93.49286556243896
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len(labels)
In [14]:
import cv2
import numpy as np
import glob
import random
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