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Different types of image pre-processing for neural networks
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README.md

#Server docker run --net=host --runtime=nvidia -it --rm -p 8900:8500 -p 8901:8501 -v /home/alex/coding/IPython_neuralnet/models:/models -e MODEL_NAME=retinanet tensorflow/serving:latest-gpu --rest_api_port=0 --enable_batching=true --model_config_file=/models/model_configs/retinanet.json

#Client

docker run -it --runtime=nvidia --net=host -v /home/alex/coding/IPython_neuralnet:/coding --rm alexcpn/tfserving-keras-retinanet-dev-gpu To run TF Client unset http_proxy unset https_proxy root@drone-OMEN:/coding/tfserving_client#python retinanet_client.py -num_tests=1 -server=127.0.0.1:8500 -batch_size=1 -img_path='../examples/google1.jpg'

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retinanet_client_orig_preprocessing.py
""" GRPC Client for Retinanet TF Serving Model"""
__author__ = "Alex Punnen"
__date__ = "March 2019"
import grpc
import numpy
import tensorflow as tf
import time
import numpy as np
from timeit import default_timer as timer
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
# for reitnanet image processing
from keras.preprocessing import image
from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
from keras_retinanet.utils.visualization import draw_box, draw_caption
from keras_retinanet.utils.colors import label_color
import cv2
from PIL import Image
tf.app.flags.DEFINE_integer('num_tests', 1, 'Number of test images')
tf.app.flags.DEFINE_string('server', '127.0.0.1:8500', 'PredictionService host:port')
tf.app.flags.DEFINE_integer('batch_size', 1, 'No of images to batch -32,64,138 ')
tf.app.flags.DEFINE_string('img_path', '', 'realtive/fullpath to jpegfile')
FLAGS = tf.app.flags.FLAGS
_counter = 0
_start = 0
_draw = None
# load label to names mapping for visualization purposes
labels_to_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
_response_awaiting = True
def _callback(result_future):
"""Callback function.
Calculates the statistics for the prediction result.
Args:
result_future: Result future of the RPC.
"""
#print ("Something came - Hee haw")
global _counter
global _start
global _response_awaiting
exception = result_future.exception()
if exception:
print(exception)
"""
Querying the save model gives
The given SavedModel SignatureDef contains the following output(s):
outputs['filtered_detections/map/TensorArrayStack/TensorArrayGatherV3:0'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 300, 4)
name: filtered_detections/map/TensorArrayStack/TensorArrayGatherV3:0
outputs['filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3:0'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 300)
name: filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3:0
outputs['filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3:0'] tensor_info:
dtype: DT_INT32
shape: (-1, 300)
name: filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3:0
Method name is: tensorflow/serving/predict
---
From retina-net
In general, inference of the network works as follows:
boxes, scores, labels = model.predict_on_batch(inputs)
Where `boxes` are shaped `(None, None, 4)` (for `(x1, y1, x2, y2)`), scores is shaped `(None, None)` (classification score) and labels is shaped `(None, None)` (label corresponding to the score). In all three outputs, the first dimension represents the shape and the second dimension indexes the list of detections.
"""
parse_result(result_future.result())
def parse_result(result_future):
"""Callback function.
Calculates the statistics for the prediction result.
Args:
result_future: Result future of the RPC.
For the example image the results would come like using retinanet pre-processing
'in image shape', (800, 1067, 3))
('Input shape=', (1, 800, 1067, 3))
('in tf shape', (2, 800, 1067, 3))
('result no', 0)
('boxes output', (1, 300, 4))
('scores output', (1, 300))
('labels output', (1, 300))
('Label', 'person', ' at ', array([409, 167, 728, 603]), ' Score ', 0.9681119)
('Label', 'person', ' at ', array([ 0, 426, 512, 785]), ' Score ', 0.8355836)
('Label', 'person', ' at ', array([ 723, 475, 1067, 791]), ' Score ', 0.72344124)
('Label', 'tie', ' at ', array([527, 335, 569, 505]), ' Score ', 0.525432)
('Time for ', 1, ' is ', 0.7898709774017334) in 1070 8Gb
"""
global _counter
global _start
global _response_awaiting
global _draw
boxes = result_future.\
outputs['filtered_detections/map/TensorArrayStack/TensorArrayGatherV3:0']
scores = result_future.\
outputs['filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3:0']
labels = result_future.\
outputs['filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3:0']
boxes= tf.make_ndarray(boxes)
scores= tf.make_ndarray(scores)
labels= tf.make_ndarray(labels)
print("result no",_counter)
print("boxes output",(boxes).shape)
print("scores output",(scores).shape)
print("labels output",(labels).shape)
# visualize detections
for box, score, label in zip(boxes[0], scores[0], labels[0]):
# scores are sorted so we can break
if score < 0.3:
break
b = box.astype(int)
print("Label",labels_to_names[label]," at ",b," Score ",score)
# draw the image and write out
color = label_color(label)
draw_box(_draw, b, color=color)
caption = "{} {:.3f}".format(labels_to_names[label], score)
draw_caption(_draw, b, caption)
_counter += 1
#if( (_counter % 1) ==0):#print every 100
# print("[", _counter,"] From Callback Predicted Result is ", prediction,"confidence= ",response[prediction])
if (_counter == FLAGS.num_tests):
end = time.time()
print("Time for ",FLAGS.num_tests," is ",end -_start)
_response_awaiting = False
cv2.imwrite("/coding/out.png", _draw)
def do_inference(server, batch_size, num_tests,img_path):
channel = grpc.insecure_channel(server)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = 'retinanet'
request.model_spec.signature_name = 'serving_default'
print("Image path",img_path)
#post process the image
#image,org= decode_image_opencv(img_path)
image,org = decode_image_retinanet(img_path)
#image,org = decode_image_tf_reader(img_path)
global _draw
_draw = org.copy()
print ("in image shape",image.shape)
#('in image shape', (480, 640, 3))
#input = np.expand_dims(image, axis=0)
# do this once as tf. make_tensor proto is slow on first use
#request.inputs['input_image'].CopyFrom(tf.contrib.util.make_tensor_proto \
# (input, shape=[1, 800, 1067, 3])) # (input, shape=[1, 500, 567, 3]))
global _start
_start = time.time()
global _response_awaiting
_response_awaiting =True
for i in range(num_tests):
#print("Going to send the request")
# batching
# If using anything other than decode_opencv uncomment line below
input = np.expand_dims(image, axis=0)
#('Input shape=', (1, 480, 640, 3))
#input = image # comment this if using anything other than retinanet
print("Input shape=",input.shape )
inputs = input
for _ in range(batch_size-1):
inputs = np.append(inputs, input, axis=0)
print("in tf shape",inputs.shape)
request.inputs['input_image'].CopyFrom(tf.contrib.util.make_tensor_proto
(inputs, shape=inputs.shape))
# call back way - this is faster
result_future = stub.Predict.future(request, 60.25) # Intial takes time
result_future.add_done_callback(_callback)
# request reponse way - this is slower
# result = stub.Predict(request, 10.25) # seconds
# parse_result(result)
_response_awaiting = True
#print("Send the request")
# End for loop
while(_response_awaiting):
time.sleep(.000010)
print("Response Received Exiting")
################################################################################
# Helper functions for image preoricessing #
################################################################################
def decode_image_retinanet(img_path):
## Going to read the image via retinanet helper- the original way
start = timer()
image = read_image_bgr(img_path)
# preprocess image for network
image = preprocess_image(image)
image, scale = resize_image(image)
end = timer()
print("decode time=",end - start)
#('decode time=', 0.028119802474975586)
# these are the best scores
#('Label', 'person', ' at ', array([409, 167, 728, 603]), ' Score ', 0.9681119)
#('Label', 'person', ' at ', array([ 0, 426, 512, 785]), ' Score ', 0.8355836)
#('Label', 'person', ' at ', array([ 723, 475, 1067, 791]), ' Score ', 0.72344124)
#('Label', 'tie', ' at ', array([527, 335, 569, 505]), ' Score ', 0.525432)
return image,image
def decode_image_tf_reader(img_path):
## Going to read the image via TF helper
img_raw = tf.read_file(img_path)
start = timer()
img_tensor = tf.image.decode_jpeg(img_raw, channels=0,
dct_method="INTEGER_FAST") #not much effect here decode time 30ms
print("img_tensor.shape=",img_tensor.shape)
image = tf.cast(img_tensor, tf.float32)
#image = tf.image.resize_images(img_tensor, [800,1067])
smallest_side = 480.0 # will losse some info
height, width = tf.shape(image)[0], tf.shape(image)[1]
height = tf.to_float(height)
width = tf.to_float(width)
scale = tf.cond(tf.greater(height, width),
lambda: smallest_side / width,
lambda: smallest_side / height)
new_height = tf.to_int32(height * scale)
new_width = tf.to_int32(width * scale)
image = tf.image.resize_images(image, [new_height, new_width])
#image = tf.image.resize_images(image, [800,1200])
#https://forums.fast.ai/t/how-is-vgg16-mean-calculated/4577/19
VGG_MEAN = [123.68, 116.78, 103.94] # This is R-G-B for Imagenet
#means = tf.reshape(tf.constant(VGG_MEAN), [1, 1, 3])
image = image - means
# swap to BGR
img_channel_swap = image[..., ::-1]
image = tf.reverse(image, axis=[-1])
#without the above preprocessing there is a miss of detection and change in weight
image = tf.Session().run(image)
#image = image[:, :, [2,1,0]] # swap channel from RGB to BGR
end = timer()
print("decode time=",end - start)
#('decode time=', 0.032473087310791016)
#('Label', 'person', ' at ', array([ 0, 252, 306, 470]), ' Score ', 0.8479492)
#('Label', 'person', ' at ', array([241, 97, 435, 365]), ' Score ', 0.7183717)
#('Label', 'person', ' at ', array([429, 287, 635, 475]), ' Score ', 0.67711633)
#('Label', 'tie', ' at ', array([313, 199, 340, 311]), ' Score ', 0.5820301
return image,image
def decode_image_opencv(img_path):
### Going to create image vector via OpenCV
#todo https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/examples/getting%20started.html
start = timer()
image = cv2.imread(img_path,1)
image = image_resize(image,height=800)
org = image
image = cv2.dnn.blobFromImage(image, scalefactor=1.0,mean=(103.939, 116.779, 123.68), swapRB=True)
# this gives shape as (1, 3, 480, 640))
# we need it as ('Input shape=', (1, 480, 640, 3))
image = np.transpose(image, (0, 2, 3, 1))
#image = image.astype('f')
#image = image -127.5
end = timer()
#VGG_MEAN = [103.939, 116.779, 123.68]
#out = np.copy(image)
#out = out[:, :, [2,1,0]] # swap channel from RGB to BGR
print("decode time=",end - start)
#'decode time=', 0.007803916931152344)
return image,org
#https://stackoverflow.com/a/44659589/429476
def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation = inter)
# return the resized image
return resized
def decode_image_pillow(img_path):
### Going to create image vector via PIL
## does not work
start = timer()
image = np.asarray(Image.open(img_path).convert('RGB'))
image = image.astype('f')
end = timer()
print("decode time=",end - start)
#('decode time=', 0.023459911346435547)
# and without conversion as in
# https://github.com/fizyr/keras-retinanet/blob/master/keras_retinanet/utils/image.py
# it is not able to detect one person object
#Label', 'person', ' at ', array([ 0, 258, 308, 473]), ' Score ', 0.8666027)
#('Label', 'person', ' at ', array([239, 98, 427, 378]), ' Score ', 0.745802)
#('Label', 'tie', ' at ', array([312, 199, 340, 314]), ' Score ', 0.7037207)
return image
def create_dummy_image(width=1067,height=800,channels=3):
#Create a random numpy array
return numpy.random.rand(height,width, channels).astype('f')
def main(_):
#img_path ='../examples/000000008021.jpg'
#img_path ='../examples/business_people_highdefinition_picture_hd_pictures.jpg'
#img_path ='../examples/Emeraude-Toubia-Hd-Photos-At-People-Choice-Awards-2018-2.jpg'
#img_path ='../examples/google1.jpg'
if not FLAGS.img_path:
img_path = FLAGS.img_path
if not FLAGS.num_tests:
print('Please specify num_tests -num_tests=n')
return
if not FLAGS.server:
print('please specify server -server host:port')
return
print("Number of test=",FLAGS.num_tests)
error_rate = do_inference(FLAGS.server, FLAGS.batch_size,
FLAGS.num_tests,FLAGS.img_path)
if __name__ == '__main__':
print ("Retinanet TFServing Client < -num_tests=1 -server=127.0.0.1:8500 -batch_size=2>")
print ("Override these default values by command line args")
tf.app.run()
Raw
retinanet_client_with_opencv.py
""" GRPC Client for Retinanet TF Serving Model"""
__author__ = "Alex Punnen"
__date__ = "March 2019"
import grpc
import numpy
import tensorflow as tf
import time
import numpy as np
from timeit import default_timer as timer
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
# for reitnanet image processing
from keras.preprocessing import image
from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
from keras_retinanet.utils.visualization import draw_box, draw_caption
from keras_retinanet.utils.colors import label_color
import cv2
from PIL import Image
tf.app.flags.DEFINE_integer('num_tests', 1, 'Number of test images')
tf.app.flags.DEFINE_string('server', '127.0.0.1:8500', 'PredictionService host:port')
tf.app.flags.DEFINE_integer('batch_size', 1, 'No of images to batch -32,64,138 ')
tf.app.flags.DEFINE_string('img_path', '', 'realtive/fullpath to jpegfile')
FLAGS = tf.app.flags.FLAGS
_counter = 0
_start = 0
_draw = None
# load label to names mapping for visualization purposes
labels_to_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
_response_awaiting = True
def _callback(result_future):
"""Callback function.
Calculates the statistics for the prediction result.
Args:
result_future: Result future of the RPC.
"""
#print ("Something came - Hee haw")
global _counter
global _start
global _response_awaiting
exception = result_future.exception()
if exception:
print(exception)
"""
Querying the save model gives
The given SavedModel SignatureDef contains the following output(s):
outputs['filtered_detections/map/TensorArrayStack/TensorArrayGatherV3:0'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 300, 4)
name: filtered_detections/map/TensorArrayStack/TensorArrayGatherV3:0
outputs['filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3:0'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 300)
name: filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3:0
outputs['filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3:0'] tensor_info:
dtype: DT_INT32
shape: (-1, 300)
name: filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3:0
Method name is: tensorflow/serving/predict
---
From retina-net
In general, inference of the network works as follows:
boxes, scores, labels = model.predict_on_batch(inputs)
Where `boxes` are shaped `(None, None, 4)` (for `(x1, y1, x2, y2)`), scores is shaped `(None, None)` (classification score) and labels is shaped `(None, None)` (label corresponding to the score). In all three outputs, the first dimension represents the shape and the second dimension indexes the list of detections.
"""
parse_result(result_future.result())
def parse_result(result_future):
"""Callback function.
Calculates the statistics for the prediction result.
Args:
result_future: Result future of the RPC.
For the example image the results would come like using retinanet pre-processing
'in image shape', (800, 1067, 3))
('Input shape=', (1, 800, 1067, 3))
('in tf shape', (2, 800, 1067, 3))
('result no', 0)
('boxes output', (1, 300, 4))
('scores output', (1, 300))
('labels output', (1, 300))
('Label', 'person', ' at ', array([409, 167, 728, 603]), ' Score ', 0.9681119)
('Label', 'person', ' at ', array([ 0, 426, 512, 785]), ' Score ', 0.8355836)
('Label', 'person', ' at ', array([ 723, 475, 1067, 791]), ' Score ', 0.72344124)
('Label', 'tie', ' at ', array([527, 335, 569, 505]), ' Score ', 0.525432)
('Time for ', 1, ' is ', 0.7898709774017334) in 1070 8Gb
"""
global _counter
global _start
global _response_awaiting
global _draw
boxes = result_future.\
outputs['filtered_detections/map/TensorArrayStack/TensorArrayGatherV3:0']
scores = result_future.\
outputs['filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3:0']
labels = result_future.\
outputs['filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3:0']
boxes= tf.make_ndarray(boxes)
scores= tf.make_ndarray(scores)
labels= tf.make_ndarray(labels)
print("result no",_counter)
print("boxes output",(boxes).shape)
print("scores output",(scores).shape)
print("labels output",(labels).shape)
# visualize detections
for box, score, label in zip(boxes[0], scores[0], labels[0]):
# scores are sorted so we can break
if score < 0.3:
break
b = box.astype(int)
print("Label",labels_to_names[label]," at ",b," Score ",score)
# draw the image and write out
color = label_color(label)
draw_box(_draw, b, color=color)
caption = "{} {:.3f}".format(labels_to_names[label], score)
draw_caption(_draw, b, caption)
_counter += 1
#if( (_counter % 1) ==0):#print every 100
# print("[", _counter,"] From Callback Predicted Result is ", prediction,"confidence= ",response[prediction])
if (_counter == FLAGS.num_tests):
end = time.time()
print("Time for ",FLAGS.num_tests," is ",end -_start)
_response_awaiting = False
cv2.imwrite("/coding/out.png", _draw)
def do_inference(server, batch_size, num_tests,img_path):
channel = grpc.insecure_channel(server)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = 'retinanet'
request.model_spec.signature_name = 'serving_default'
print("Image path",img_path)
#post process the image
image,org= decode_image_opencv(img_path)
#image,org = decode_image_retinanet(img_path)
#image,org = decode_image_tf_reader(img_path)
global _draw
_draw = org.copy()
print ("in image shape",image.shape)
#('in image shape', (480, 640, 3))
#input = np.expand_dims(image, axis=0)
# do this once as tf. make_tensor proto is slow on first use
#request.inputs['input_image'].CopyFrom(tf.contrib.util.make_tensor_proto \
# (input, shape=[1, 800, 1067, 3])) # (input, shape=[1, 500, 567, 3]))
global _start
_start = time.time()
global _response_awaiting
_response_awaiting =True
for i in range(num_tests):
#print("Going to send the request")
# batching
# If using anything other than decode_opencv uncomment line below
#input = np.expand_dims(image, axis=0)
#('Input shape=', (1, 480, 640, 3))
input = image
print("Input shape=",input.shape )
inputs = input
for _ in range(batch_size-1):
inputs = np.append(inputs, input, axis=0)
print("in tf shape",inputs.shape)
request.inputs['input_image'].CopyFrom(tf.contrib.util.make_tensor_proto
(inputs, shape=inputs.shape))
# call back way - this is faster
result_future = stub.Predict.future(request, 60.25) # Intial takes time
result_future.add_done_callback(_callback)
# request reponse way - this is slower
# result = stub.Predict(request, 10.25) # seconds
# parse_result(result)
_response_awaiting = True
#print("Send the request")
# End for loop
while(_response_awaiting):
time.sleep(.000010)
print("Response Received Exiting")
################################################################################
# Helper functions for image preoricessing #
################################################################################
def decode_image_retinanet(img_path):
## Going to read the image via retinanet helper- the original way
start = timer()
image = read_image_bgr(img_path)
# preprocess image for network
image = preprocess_image(image)
image, scale = resize_image(image)
end = timer()
print("decode time=",end - start)
#('decode time=', 0.028119802474975586)
# these are the best scores
#('Label', 'person', ' at ', array([409, 167, 728, 603]), ' Score ', 0.9681119)
#('Label', 'person', ' at ', array([ 0, 426, 512, 785]), ' Score ', 0.8355836)
#('Label', 'person', ' at ', array([ 723, 475, 1067, 791]), ' Score ', 0.72344124)
#('Label', 'tie', ' at ', array([527, 335, 569, 505]), ' Score ', 0.525432)
return image,image
def decode_image_tf_reader(img_path):
## Going to read the image via TF helper
img_raw = tf.read_file(img_path)
start = timer()
img_tensor = tf.image.decode_jpeg(img_raw, channels=0,
dct_method="INTEGER_FAST") #not much effect here decode time 30ms
print("img_tensor.shape=",img_tensor.shape)
image = tf.cast(img_tensor, tf.float32)
#image = tf.image.resize_images(img_tensor, [800,1067])
smallest_side = 480.0 # will losse some info
height, width = tf.shape(image)[0], tf.shape(image)[1]
height = tf.to_float(height)
width = tf.to_float(width)
scale = tf.cond(tf.greater(height, width),
lambda: smallest_side / width,
lambda: smallest_side / height)
new_height = tf.to_int32(height * scale)
new_width = tf.to_int32(width * scale)
image = tf.image.resize_images(image, [new_height, new_width])
#image = tf.image.resize_images(image, [800,1200])
#https://forums.fast.ai/t/how-is-vgg16-mean-calculated/4577/19
VGG_MEAN = [123.68, 116.78, 103.94] # This is R-G-B for Imagenet
#means = tf.reshape(tf.constant(VGG_MEAN), [1, 1, 3])
image = image - means
# swap to BGR
img_channel_swap = image[..., ::-1]
image = tf.reverse(image, axis=[-1])
#without the above preprocessing there is a miss of detection and change in weight
image = tf.Session().run(image)
#image = image[:, :, [2,1,0]] # swap channel from RGB to BGR
end = timer()
print("decode time=",end - start)
#('decode time=', 0.032473087310791016)
#('Label', 'person', ' at ', array([ 0, 252, 306, 470]), ' Score ', 0.8479492)
#('Label', 'person', ' at ', array([241, 97, 435, 365]), ' Score ', 0.7183717)
#('Label', 'person', ' at ', array([429, 287, 635, 475]), ' Score ', 0.67711633)
#('Label', 'tie', ' at ', array([313, 199, 340, 311]), ' Score ', 0.5820301
return image,image
def decode_image_opencv(img_path):
### Going to create image vector via OpenCV
#todo https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/examples/getting%20started.html
start = timer()
image = cv2.imread(img_path,1)
image = image_resize(image,height=800)
org = image
image = cv2.dnn.blobFromImage(image, scalefactor=1.0,mean=(103.939, 116.779, 123.68), swapRB=True)
# this gives shape as (1, 3, 480, 640))
# we need it as ('Input shape=', (1, 480, 640, 3))
image = np.transpose(image, (0, 2, 3, 1))
#image = image.astype('f')
#image = image -127.5
end = timer()
#VGG_MEAN = [103.939, 116.779, 123.68]
#out = np.copy(image)
#out = out[:, :, [2,1,0]] # swap channel from RGB to BGR
print("decode time=",end - start)
#'decode time=', 0.007803916931152344)
return image,org
#https://stackoverflow.com/a/44659589/429476
def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation = inter)
# return the resized image
return resized
def decode_image_pillow(img_path):
### Going to create image vector via PIL
## does not work
start = timer()
image = np.asarray(Image.open(img_path).convert('RGB'))
image = image.astype('f')
end = timer()
print("decode time=",end - start)
#('decode time=', 0.023459911346435547)
# and without conversion as in
# https://github.com/fizyr/keras-retinanet/blob/master/keras_retinanet/utils/image.py
# it is not able to detect one person object
#Label', 'person', ' at ', array([ 0, 258, 308, 473]), ' Score ', 0.8666027)
#('Label', 'person', ' at ', array([239, 98, 427, 378]), ' Score ', 0.745802)
#('Label', 'tie', ' at ', array([312, 199, 340, 314]), ' Score ', 0.7037207)
return image
def create_dummy_image(width=1067,height=800,channels=3):
#Create a random numpy array
return numpy.random.rand(height,width, channels).astype('f')
def main(_):
#img_path ='../examples/000000008021.jpg'
#img_path ='../examples/business_people_highdefinition_picture_hd_pictures.jpg'
#img_path ='../examples/Emeraude-Toubia-Hd-Photos-At-People-Choice-Awards-2018-2.jpg'
#img_path ='../examples/google1.jpg'
if not FLAGS.img_path:
img_path = FLAGS.img_path
if not FLAGS.num_tests:
print('Please specify num_tests -num_tests=n')
return
if not FLAGS.server:
print('please specify server -server host:port')
return
print("Number of test=",FLAGS.num_tests)
error_rate = do_inference(FLAGS.server, FLAGS.batch_size,
FLAGS.num_tests,FLAGS.img_path)
if __name__ == '__main__':
print ("Retinanet TFServing Client < -num_tests=1 -server=127.0.0.1:8500 -batch_size=2>")
print ("Override these default values by command line args")
tf.app.run()
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