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jessica_cv.py
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jessica_cv.py
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import time
import numpy
import hashlib
from PIL import *
from keras.utils import *
from keras.losses import *
from keras.layers import *
from keras.metrics import *
from jessica_local_spark_building import sqlContext
from pyspark.sql.types import StructType, StructField, StringType
from pyspark import StorageLevel
from keras.models import *
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications import xception
base_model_Xception = xception.Xception(weights='xception_weights_tf_dim_ordering_tf_kernels_notop.h5', include_top=False)
def file_json2file_npy(input_json,
sqlContext,
file_path_column_name = None,
x_xception_npy = None,
x_document_id_npy = None,
y_npy = None,
output_json = None):
start_time = time.time()
print('loading data from %s'%(input_json))
input_df = sqlContext.read.json(input_json)
input_df.registerTempTable('input_df')
input_df = sqlContext.sql(u"""
SELECT * FROM input_df ORDER BY document_id
""")
print('loaded %d records from %s'%(input_df.count(), input_json))
print('collecting data')
data =input_df.collect()
###
x_document_id = []
x_file_path = []
x_xception = []
x_inception_v3 = []
x_hash = []
y = []
for r in data:
r1 = r.asDict()
if x_xception_npy is not None:
img = image.load_img(r1[file_path_column_name], target_size=(224, 224))
x = image.img_to_array(img)
if output_json is not None:
hash_object = hashlib.md5(str(x.data.tobytes()).encode())
x_hash.append(hash_object.hexdigest())
x_xception.append(xception.preprocess_input(x))
x_document_id.append(r.document_id)
if y_npy is not None:
y.append(r.label)
if x_xception_npy is not None:
print('extracting and saving featurs')
x_document_id = numpy.array(x_document_id)
numpy.save(x_document_id_npy, x_document_id)
x_xception = numpy.array(x_xception)
x_xception = base_model_Xception.predict(x_xception)
numpy.save(x_xception_npy, x_xception)
if y_npy is not None:
print('saving labels')
y = numpy.array(y)
y = to_categorical(y)
numpy.save(y_npy, y)
if output_json is not None:
if 'content_hash' not in input_df.columns:
data = [(str(d), str(h))
for d, h in
zip(x_document_id, x_hash)]
sqlContext.createDataFrame(data, ['document_id', 'content_hash']).registerTempTable('df_content_hash')
sqlContext.sql(u"""
SELECT input_df.*,
df_content_hash.content_hash
FROM input_df
LEFT JOIN df_content_hash
ON df_content_hash.document_id
= input_df.document_id
""").write.mode('Overwrite').json(output_json)
else:
input_df.write.mode('Overwrite').json(output_json)
print('running time:\t%f secondes'%(time.time()-start_time))
return None
'''
>>> x_xception.shape
(8, 7, 7, 2048)
>>> x_inception_v3.shape
(8, 5, 5, 2048)
'''
def build_image_categorization_model(gpus = None):
model = Sequential()
model.add(GlobalAveragePooling2D(input_shape=(7, 7, 2048)))
model.add(Dense(1024, activation='relu'))
model.add(Dense(2, activation='softmax'))
if gpus is not None:
model = multi_gpu_model(model, gpus = gpus)
return model
def train_image_categorization_model(
x_npy, y_npy,
x_document_id_npy,
gpus = None,
epochs = 3,
positive_weight = 1,
batch_size = 512,
model_file = None,
output_prediction_json = None):
#####
print('load data and label from npy files')
x = numpy.load(x_npy)
x_document_id = numpy.load(x_document_id_npy)
y = numpy.load(y_npy)
####
print('building model')
model = build_image_categorization_model(gpus = gpus)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
print('training the model')
model.fit(x, y,
batch_size=batch_size,
epochs=epochs,
class_weight = {1:positive_weight, 0:1})
if model_file is not None:
print('saving the model')
model.save_weights(model_file)
#####
print('predicting the labels from the trained model')
y_score = model.predict(x)
label_predicted = numpy.argmax(y_score,axis=-1)
label = numpy.argmax(y,axis=-1)
label_confidence = numpy.max(y_score,axis=1)
print('building the dataframe of the prediciton results')
data = [(str(d), int(l), int(p), float(s))
for d, l, p, s in zip(x_document_id,
label,
label_predicted,
label_confidence)]
###
df_prediction = sqlContext.createDataFrame(data,
['document_id', 'label', 'prediction', 'score']).persist(StorageLevel.MEMORY_AND_DISK)
####
if output_prediction_json is not None:
print('saving the prediction results')
df_prediction.write.mode('Overwrite').json(output_prediction_json)
#####
df_prediction.registerTempTable('df_prediction')
sqlContext.sql(u"""
SELECT label, prediction, COUNT(*)
FROM df_prediction
GROUP BY label, prediction
""").show()
return model
def update_label_from_positive_file_csv(
input_json,
output_json,
sqlContext,
input_positive_file_csv = None,
input_positive_hash_csv = None):
####
'''
input_positive_file_csv = 'uae_flag.csv'
input_json = 'image_set.json'
output_json = 'uae_flag_updated_label.json'
'''
sqlContext.read.json(input_json).withColumnRenamed('label', 'old_label').registerTempTable('input_df')
####
if input_positive_file_csv is not None:
additioal_postive_file = sqlContext.read.format('csv')\
.option("header", "false")\
.schema(StructType([StructField("document_id", StringType(), True)]))\
.load(input_positive_file_csv)\
.dropDuplicates()
additioal_postive_file.registerTempTable('additioal_postive_file')
print('loaded %d positive document_id'%(additioal_postive_file.count()))
else:
sqlContext.sql(u"""
SELECT NULL AS document_id
""").registerTempTable('additioal_postive_file')
####
if input_positive_hash_csv is not None:
additioal_postive_hash = sqlContext.read.format('csv')\
.option("header", "false")\
.schema(StructType([StructField("content_hash", StringType(), True)]))\
.load(input_positive_hash_csv)\
.dropDuplicates()
additioal_postive_hash.registerTempTable('additioal_postive_hash')
print('loaded %d positive content hash'%(additioal_postive_hash.count()))
else:
sqlContext.sql(u"""
SELECT NULL AS content_hash
""").registerTempTable('additioal_postive_hash')
###
sqlContext.sql(u"""
SELECT input_df.*,
CASE
WHEN additioal_postive_file.document_id IS NOT NULL
OR additioal_postive_hash.content_hash IS NOT NULL
THEN 1
ELSE old_label
END AS label
FROM input_df
LEFT JOIN additioal_postive_file
ON additioal_postive_file.document_id
= input_df.document_id
LEFT JOIN additioal_postive_hash
ON additioal_postive_hash.content_hash
= input_df.content_hash
ORDER BY input_df.document_id
""").write.mode('Overwrite').json(output_json)
sqlContext.read.json(output_json).registerTempTable('output_df')
sqlContext.sql(u"""
SELECT old_label, label, COUNT(*)
FROM output_df
GROUP BY old_label, label
""").show()
def load_build_image_categorization_model(
model_file,
gpus = None):
model = build_image_categorization_model(gpus = gpus)
model.load_weights(model_file)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model._make_predict_function()
return model
def image_tagging(
x, model,
tag_name):
output = {}
x = xception.preprocess_input(x)
x = numpy.array([x])
x = base_model_Xception.predict(x)
y_score = model.predict(x)
prediction = numpy.argmax(y_score)
score = numpy.max(y_score)
if prediction > 0:
output["tag"] = tag_name
output["score"] = score
return output
def read_image_from_local(file_path):
img = image.load_img(file_path, target_size=(224, 224))
x = image.img_to_array(img)
return x