add:app

Dockerfile

@@ -0,0 +1,24 @@
+FROM guillaumeai/ast:ap2404_v1-195ik
+
+RUN apt update && apt install -y sudo imagemagick
+# RUN apt upgrade -y
+
+RUN pip install -U pip
+# #RUN pip install -U pyyaml
+# RUN pip install -U runway-python 
+# #runway --force-reinstall
+# #RUN pip install -U tensorflow
+
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY server.py .
+COPY compo-singleone-v1-dev-acc.py .
+COPY compo-singleone-v2-dev-acc.py .
+
+EXPOSE 7860
+
+#compo-singleone-v1-dev-acc.py
+
+CMD ["python", "compo-singleone-v2-dev-acc.py"]

compo-singleone-v1-dev-acc.py

@@ -0,0 +1,368 @@
+#####################################################
+# AST Composite Server Double Two
+# By Guillaume Descoteaux-Isabelle, 20021
+#
+# This server compose two Adaptive Style Transfer model (output of the first pass serve as input to the second using the same model)
+########################################################
+#v1-dev
+#Receive the 2 res from arguments in the request...
+
+
+import os
+import numpy as np
+import tensorflow as tf
+import cv2
+from module import encoder, decoder
+from glob import glob
+import runway
+from runway.data_types import number, text
+
+
+#from utils import *
+import scipy
+from datetime import datetime
+import time
+
+
+#/var/lib/ast/model/model_cezanne:/data/styleCheckpoint/model_cezanne
+
+# Determining the size of the passes
+pass1_image_size = 1328
+if not os.getenv('PASS1IMAGESIZE'):
+   print("PASS1IMAGESIZE env var non existent;using default:" + str(pass1_image_size)) 
+else:
+   pass1_image_size = os.getenv('PASS1IMAGESIZE', 1328)
+   print("PASS1IMAGESIZE value:" + str(pass1_image_size))
+
+
+# Determining the size of the passes
+autoabc = 1
+if not os.getenv('AUTOABC'):
+   print("AUTOABC env var non existent;using default:")
+   print(autoabc)
+   abcdefault = 1
+   print("NOTE----> when running docker, set   AUTOABC variable")
+   print("   docker run ...  -e AUTOABC=1  #enabled, 0 to disabled (default)")
+else:
+   autoabc = os.getenv('AUTOABC',1)
+   print("AUTOABC value:")
+   print(autoabc)
+   abcdefault = autoabc
+
+
+#pass2_image_size = 1024
+#if not os.getenv('PASS2IMAGESIZE'):
+#   print("PASS2IMAGESIZE env var non existent;using default:" + pass2_image_size)
+#else:
+#   pass2_image_size = os.getenv('PASS2IMAGESIZE')
+#   print("PASS2IMAGESIZE value:" + pass2_image_size)
+
+# pass3_image_size = 2048
+# if not os.getenv('PASS3IMAGESIZE'):
+#    print("PASS3IMAGESIZE env var non existent;using default:" + pass3_image_size)
+# else:
+#    pass3_image_size = os.getenv('PASS3IMAGESIZE')
+#    print("PASS3IMAGESIZE value:" + pass3_image_size)
+
+##########################################
+##   MODELS
+#model name for sending it in the response
+model1name = "UNNAMED"
+if not os.getenv('MODEL1NAME'):
+   print("MODEL1NAME env var non existent;using default:" + model1name)
+else:
+   model1name = os.getenv('MODEL1NAME', "UNNAMED")
+   print("MODEL1NAME value:" + model1name)
+   
+# #m2
+# model2name = "UNNAMED"
+# if not os.getenv('MODEL2NAME'):   print("MODEL2NAME env var non existent;using default:" + model2name)
+# else:
+#    model2name = os.getenv('MODEL2NAME')
+#    print("MODEL2NAME value:" + model2name)
+
+# #m3
+# model3name = "UNNAMED"
+# if not os.getenv('MODEL3NAME'):   print("MODEL3NAME env var non existent;using default:" + model3name)
+# else:
+#    model3name = os.getenv('MODEL3NAME')
+#    print("MODEL3NAME value:" + model3name)
+
+#######################################################
+
+
+#########################################################
+# SETUP
+
+@runway.setup(options={'styleCheckpoint': runway.file(is_directory=True)})
+def setup(opts):
+    sess = tf.Session()
+    # sess2 = tf.Session()
+    # sess3 = tf.Session()
+    init_op = tf.global_variables_initializer()
+    # init_op2 = tf.global_variables_initializer()
+    # init_op3 = tf.global_variables_initializer()
+    sess.run(init_op)
+    # sess2.run(init_op2)
+    # sess3.run(init_op3)
+    with tf.name_scope('placeholder'):
+        input_photo = tf.placeholder(dtype=tf.float32,
+                                     shape=[1, None, None, 3],
+                                     name='photo')
+    input_photo_features = encoder(image=input_photo,
+                                   options={'gf_dim': 32},
+                                   reuse=False)
+    output_photo = decoder(features=input_photo_features,
+                           options={'gf_dim': 32},
+                           reuse=False)
+    saver = tf.train.Saver()
+    # saver2 = tf.train.Saver()
+    # saver3 = tf.train.Saver()
+    path = opts['styleCheckpoint']
+    #Getting the model name
+    model_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][0]    
+    if not os.getenv('MODELNAME'):
+        dtprint("CONFIG::MODELNAME env var non existent;using default:" + model_name)
+    else:
+        model_name = os.getenv('MODELNAME')
+    
+    
+
+    # #Getting the model2 name
+    # model2_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][1]
+    # if not os.getenv('MODEL2NAME'):
+    #     dtprint("CONFIG::MODEL2NAME env var non existent;using default:" + model2_name)
+    # else:
+    #     model2_name = os.getenv('MODEL2NAME')
+        
+
+    ##Getting the model3 name
+    # model3_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][2]
+    # if not os.getenv('MODEL3NAME'):
+    #     dtprint("CONFIG::MODEL3NAME env var non existent;using default:" + model3_name)
+    # else:
+    #     model3_name = os.getenv('MODEL3NAME')
+    
+    
+
+    checkpoint_dir = os.path.join(path, model_name, 'checkpoint_long')
+    #checkpoint2_dir = os.path.join(path, model2_name, 'checkpoint_long')
+    # checkpoint3_dir = os.path.join(path, model3_name, 'checkpoint_long')
+    print("-----------------------------------------")
+    print("modelname is : " + model_name)
+    #print("model2name is : " + model2_name)
+    # print("model3name is : " + model3_name)
+    print("checkpoint_dir is : " + checkpoint_dir)
+
+    print("Auto Brightness-Contrast Correction can be set as the x2 of this SingleOne Server")
+
+    
+    #print("checkpoint2_dir is : " + checkpoint2_dir)
+    # print("checkpoint3_dir is : " + checkpoint3_dir)
+    print("-----------------------------------------")
+    ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
+    #ckpt2 = tf.train.get_checkpoint_state(checkpoint2_dir)
+    # ckpt3 = tf.train.get_checkpoint_state(checkpoint3_dir)
+    ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
+    #ckpt2_name = os.path.basename(ckpt2.model_checkpoint_path)
+    # ckpt3_name = os.path.basename(ckpt3.model_checkpoint_path)
+    saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
+    #saver2.restore(sess2, os.path.join(checkpoint2_dir, ckpt2_name))
+    # saver3.restore(sess3, os.path.join(checkpoint3_dir, ckpt3_name))
+    m1 = dict(sess=sess, input_photo=input_photo, output_photo=output_photo)
+    #m2 = dict(sess=sess2, input_photo=input_photo, output_photo=output_photo)
+    # m3 = dict(sess=sess3, input_photo=input_photo, output_photo=output_photo)
+    models = type('', (), {})()
+    models.m1 = m1
+    #models.m2 = m2
+    # models.m3 = m3
+    return models
+
+
+#@STCGoal add number or text to specify resolution of the three pass
+inputs={'contentImage': runway.image,'x1':number(default=1024,min=24,max=17000),'x2':number(default=0,min=-99,max=99)}
+outputs={'stylizedImage': runway.image,'totaltime':number,'x1': number,'c1':number,'model1name':text}
+
+@runway.command('stylize', inputs=inputs, outputs=outputs)
+def stylize(models, inp):
+    start = time.time()
+    dtprint("Composing.1..")
+    model = models.m1
+    #model2 = models.m2
+    # model3 = models.m3
+    
+    #Getting our names back (even though I think we dont need)
+    #@STCIssue BUGGED
+    # m1name=models.m1.name
+    # m2name=models.m2.name
+    # m3name=models.m3.name
+
+    #get size from inputs rather than env
+    x1 = inp['x1']
+    c1 = inp['x2']
+    # x3 = inp['x3']
+    if c1 > 99:
+        ci = abcdefault
+
+
+    #
+    img = inp['contentImage']
+    img = np.array(img)
+    img = img / 127.5 - 1.
+
+    #@a Pass 1 RESIZE to 1368px the smaller side
+    image_size=pass1_image_size
+    image_size=x1
+    img_shape = img.shape[:2]
+    alpha = float(image_size) / float(min(img_shape))
+    dtprint ("DEBUG::content.imgshape:" +   str(tuple(img_shape)) + ", alpha:" + str(alpha))
+
+    try:
+        img = scipy.misc.imresize(img, size=alpha)
+    except:
+        pass
+        
+
+    img = np.expand_dims(img, axis=0)
+    #@a INFERENCE PASS 1
+    dtprint("INFO:Pass1 inference starting")
+    img = model['sess'].run(model['output_photo'], feed_dict={model['input_photo']: img})
+    dtprint("INFO:Pass1 inference done")
+    #
+    img = (img + 1.) * 127.5
+    img = img.astype('uint8')
+    img = img[0]
+    #dtprint("INFO:Upresing Pass1 for Pass 2 (STARTING) ")
+
+    #@a Pass 2 RESIZE to 1024px the smaller side
+    #image_size=pass2_image_size
+    #image_size=x2
+    #img_shape = img.shape[:2]
+    
+    
+    #alpha = float(image_size) / float(min(img_shape))
+    #dtprint ("DEBUG::pass1.imgshape:" +   str(tuple(img_shape)) + ", alpha:" + str(alpha))
+
+    #img = scipy.misc.imresize(img, size=alpha)
+    #dtprint("INFO:Upresing Pass1 (DONE) ")
+
+    #Iteration 2    
+    #img = np.array(img)
+    #img = img / 127.5 - 1.
+    #img = np.expand_dims(img, axis=0)
+    #@a INFERENCE PASS 2 using the same model
+    #dtprint("INFO:Pass2 inference (STARTING)")
+    #img = model['sess'].run(model['output_photo'], feed_dict={model['input_photo']: img})
+    #dtprint("INFO:Pass2 inference (DONE)")
+    #img = (img + 1.) * 127.5
+    #img = img.astype('uint8')
+    #img = img[0]
+
+
+
+    # #pass3
+
+    # #@a Pass 3 RESIZE to 2048px the smaller side
+    # image_size=pass3_image_size
+    # image_size=x3
+    # img_shape = img.shape[:2]
+    
+    
+    # alpha = float(image_size) / float(min(img_shape))
+    # dtprint ("DEBUG::pass2.imgshape:" +   str(tuple(img_shape)) + ", alpha:" + str(alpha))
+
+    # img = scipy.misc.imresize(img, size=alpha)
+    # dtprint("INFO:Upresing Pass2 (DONE) ")
+
+    # #Iteration 3
+    # img = np.array(img)
+    # img = img / 127.5 - 1.
+    # img = np.expand_dims(img, axis=0)
+    # #@a INFERENCE PASS 3
+    # dtprint("INFO:Pass3 inference (STARTING)")
+    # img = model3['sess'].run(model3['output_photo'], feed_dict={model3['input_photo']: img})
+    # dtprint("INFO:Pass3 inference (DONE)")
+    # img = (img + 1.) * 127.5
+    # img = img.astype('uint8')
+    # img = img[0]
+    # #pass3
+
+    #dtprint("INFO:Composing done")
+    print('autoabc value:')
+    print(c1)
+    if c1 != 0 :
+        print('Auto Brightening images...')
+        img = img, alpha2, beta = automatic_brightness_and_contrast(img,c1)
+
+    stop = time.time()
+    totaltime = stop - start
+    print("The time of the run:", totaltime)
+    res2 = dict(stylizedImage=img,totaltime=totaltime,x1=x1,model1name=model1name,c1=c1)
+    return res2
+
+
+
+def dtprint(msg):
+    dttag=getdttag()
+    print(dttag + "::" + msg  )
+
+def getdttag():
+    # datetime object containing current date and time
+    now = datetime.now()    
+
+    # dd/mm/YY H:M:S
+    # dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
+    return now.strftime("%H:%M:%S")
+
+
+
+# Automatic brightness and contrast optimization with optional histogram clipping
+def automatic_brightness_and_contrast(image, clip_hist_percent=25):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Calculate grayscale histogram
+    hist = cv2.calcHist([gray],[0],None,[256],[0,256])
+    hist_size = len(hist)
+
+    # Calculate cumulative distribution from the histogram
+    accumulator = []
+    accumulator.append(float(hist[0]))
+    for index in range(1, hist_size):
+        accumulator.append(accumulator[index -1] + float(hist[index]))
+
+    # Locate points to clip
+    maximum = accumulator[-1]
+    clip_hist_percent *= (maximum/100.0)
+    clip_hist_percent /= 2.0
+
+    # Locate left cut
+    minimum_gray = 0
+    while accumulator[minimum_gray] < clip_hist_percent:
+        minimum_gray += 1
+
+    # Locate right cut
+    maximum_gray = hist_size -1
+    while accumulator[maximum_gray] >= (maximum - clip_hist_percent):
+        maximum_gray -= 1
+
+    # Calculate alpha and beta values
+    alpha = 255 / (maximum_gray - minimum_gray)
+    beta = -minimum_gray * alpha
+
+    '''
+    # Calculate new histogram with desired range and show histogram 
+    new_hist = cv2.calcHist([gray],[0],None,[256],[minimum_gray,maximum_gray])
+    plt.plot(hist)
+    plt.plot(new_hist)
+    plt.xlim([0,256])
+    plt.show()
+    '''
+
+    auto_result = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)
+    return (auto_result, alpha, beta)
+
+
+if __name__ == '__main__':
+    #print('External Service port is:' +os.environ.get('SPORT'))    
+    os.environ["RW_PORT"] = "7860"
+    runway.run()

compo-singleone-v2-dev-acc.py

@@ -0,0 +1,553 @@
+#####################################################
+# AST Composite Server Double Two
+# By Guillaume Descoteaux-Isabelle, 20021
+#
+# This server compose two Adaptive Style Transfer model (output of the first pass serve as input to the second using the same model)
+########################################################
+#v1-dev
+#Receive the 2 res from arguments in the request...
+
+
+import os
+import numpy as np
+import tensorflow as tf
+import cv2
+from module import encoder, decoder
+from glob import glob
+import runway
+from runway.data_types import number, text
+
+
+#from utils import *
+import scipy
+from datetime import datetime
+import time
+
+
+import re
+
+
+SRV_TYPE="s1"
+
+#set env var RW_ if not already set
+if not os.getenv('RW_PORT'):
+    os.environ["RW_PORT"] = "7860"
+
+if not os.getenv('RW_DEBUG'):
+    os.environ["RW_DEBUG"] = "0"
+if not os.getenv('RW_HOST'):
+    os.environ["RW_HOST"] = "0.0.0.0"
+#RW_MODEL_OPTIONS
+if not os.getenv('RW_MODEL_OPTIONS'):
+    os.environ["RW_MODEL_OPTIONS"]='{"styleCheckpoint":"/data/styleCheckpoint"}'
+
+# Determining the size of the passes
+pass1_image_size = 1328
+if not os.getenv('PASS1IMAGESIZE'):
+   print("PASS1IMAGESIZE env var non existent;using default:" + str(pass1_image_size)) 
+else:
+   pass1_image_size = os.getenv('PASS1IMAGESIZE', 1328)
+   print("PASS1IMAGESIZE value:" + str(pass1_image_size))
+
+
+# Determining the size of the passes
+autoabc = 1
+if not os.getenv('AUTOABC'):
+   print("AUTOABC env var non existent;using default:")
+   print(autoabc)
+   abcdefault = 1
+   print("NOTE----> when running docker, set   AUTOABC variable")
+   print("   docker run ...  -e AUTOABC=1  #enabled, 0 to disabled (default)")
+else:
+   autoabc = os.getenv('AUTOABC',1)
+   print("AUTOABC value:")
+   print(autoabc)
+   abcdefault = autoabc
+
+
+#pass2_image_size = 1024
+#if not os.getenv('PASS2IMAGESIZE'):
+#   print("PASS2IMAGESIZE env var non existent;using default:" + pass2_image_size)
+#else:
+#   pass2_image_size = os.getenv('PASS2IMAGESIZE')
+#   print("PASS2IMAGESIZE value:" + pass2_image_size)
+
+# pass3_image_size = 2048
+# if not os.getenv('PASS3IMAGESIZE'):
+#    print("PASS3IMAGESIZE env var non existent;using default:" + pass3_image_size)
+# else:
+#    pass3_image_size = os.getenv('PASS3IMAGESIZE')
+#    print("PASS3IMAGESIZE value:" + pass3_image_size)
+
+##########################################
+##   MODELS
+#model name for sending it in the response
+model1name = "UNNAMED"
+if not os.getenv('MODEL1NAME'):
+   print("MODEL1NAME env var non existent;using default:" + model1name)
+else:
+   model1name = os.getenv('MODEL1NAME', "UNNAMED")
+   print("MODEL1NAME value:" + model1name)
+   
+# #m2
+# model2name = "UNNAMED"
+# if not os.getenv('MODEL2NAME'):   print("MODEL2NAME env var non existent;using default:" + model2name)
+# else:
+#    model2name = os.getenv('MODEL2NAME')
+#    print("MODEL2NAME value:" + model2name)
+
+# #m3
+# model3name = "UNNAMED"
+# if not os.getenv('MODEL3NAME'):   print("MODEL3NAME env var non existent;using default:" + model3name)
+# else:
+#    model3name = os.getenv('MODEL3NAME')
+#    print("MODEL3NAME value:" + model3name)
+
+#######################################################
+
+def get_model_simplified_name_from_dirname(dirname):
+    result_simple_name = dirname.replace("model_","").replace("_864x","").replace("_864","").replace("_new","").replace("-864","")
+    print("   result_simple_name:" + result_simple_name)
+    return result_simple_name
+
+def get_padded_checkpoint_no_from_filename(checkpoint_filename):
+    match = re.search(r'ckpt-(\d+)', checkpoint_filename)
+    if match:
+      number = int(match.group(1))
+    checkpoint_number = round(number/1000,0)
+    print(checkpoint_number)
+
+    padded_checkpoint_number = str(str(checkpoint_number).zfill(3))
+    return padded_checkpoint_number.replace('.0','')
+
+found_model='none'
+found_model_checkpoint='0'
+
+#########################################################
+# SETUP
+
+
+runway_files = runway.file(is_directory=True)
+@runway.setup(options={'styleCheckpoint': runway_files})
+def setup(opts):
+    global found_model,found_model_checkpoint
+    sess = tf.Session()
+    # sess2 = tf.Session()
+    # sess3 = tf.Session()
+    init_op = tf.global_variables_initializer()
+    # init_op2 = tf.global_variables_initializer()
+    # init_op3 = tf.global_variables_initializer()
+    sess.run(init_op)
+    # sess2.run(init_op2)
+    # sess3.run(init_op3)
+    with tf.name_scope('placeholder'):
+        input_photo = tf.placeholder(dtype=tf.float32,
+                                     shape=[1, None, None, 3],
+                                     name='photo')
+    input_photo_features = encoder(image=input_photo,
+                                   options={'gf_dim': 32},
+                                   reuse=False)
+    output_photo = decoder(features=input_photo_features,
+                           options={'gf_dim': 32},
+                           reuse=False)
+    saver = tf.train.Saver()
+    # saver2 = tf.train.Saver()
+    # saver3 = tf.train.Saver()
+    print("-------------====PATH---------------------->>>>--")
+    path_default = '/data/styleCheckpoint'
+    print("opts:")
+    print(opts)
+    print("----------------------------------------")
+    if opts is None:
+        print("ERROR:opts is None")
+        path = path_default
+    try:
+        path = opts['styleCheckpoint']
+    except:
+        opts= {'styleCheckpoint': u'/data/styleCheckpoint'}
+        path = opts['styleCheckpoint']
+    if not os.path.exists(path):
+        print("ERROR:Path does not exist:" + path)
+        path = path_default
+    print(path)
+    print("----------------PATH=======---------------<<<<--")
+    #Getting the model name
+    model_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][0]    
+    if not os.getenv('MODELNAME'):
+        dtprint("CONFIG::MODELNAME env var non existent;using default:" + model_name)
+    else:
+        model_name = os.getenv('MODELNAME')
+    
+    
+
+    # #Getting the model2 name
+    # model2_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][1]
+    # if not os.getenv('MODEL2NAME'):
+    #     dtprint("CONFIG::MODEL2NAME env var non existent;using default:" + model2_name)
+    # else:
+    #     model2_name = os.getenv('MODEL2NAME')
+        
+
+    ##Getting the model3 name
+    # model3_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][2]
+    # if not os.getenv('MODEL3NAME'):
+    #     dtprint("CONFIG::MODEL3NAME env var non existent;using default:" + model3_name)
+    # else:
+    #     model3_name = os.getenv('MODEL3NAME')
+    
+    
+
+    checkpoint_dir = os.path.join(path, model_name, 'checkpoint_long')
+    #checkpoint2_dir = os.path.join(path, model2_name, 'checkpoint_long')
+    # checkpoint3_dir = os.path.join(path, model3_name, 'checkpoint_long')
+    print("-----------------------------------------")
+    print("modelname is : " + model_name)
+    
+    found_model=get_model_simplified_name_from_dirname(model_name)
+    
+    #print("model2name is : " + model2_name)
+    # print("model3name is : " + model3_name)
+    print("checkpoint_dir is : " + checkpoint_dir)
+
+    
+
+    
+    #print("checkpoint2_dir is : " + checkpoint2_dir)
+    # print("checkpoint3_dir is : " + checkpoint3_dir)
+    print("-----------------------------------------")
+    ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
+    #ckpt2 = tf.train.get_checkpoint_state(checkpoint2_dir)
+    # ckpt3 = tf.train.get_checkpoint_state(checkpoint3_dir)
+    ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
+    
+    found_model_checkpoint= get_padded_checkpoint_no_from_filename(ckpt_name)
+    
+    #ckpt2_name = os.path.basename(ckpt2.model_checkpoint_path)
+    # ckpt3_name = os.path.basename(ckpt3.model_checkpoint_path)
+    saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
+    #saver2.restore(sess2, os.path.join(checkpoint2_dir, ckpt2_name))
+    # saver3.restore(sess3, os.path.join(checkpoint3_dir, ckpt3_name))
+    m1 = dict(sess=sess, input_photo=input_photo, output_photo=output_photo)
+    #m2 = dict(sess=sess2, input_photo=input_photo, output_photo=output_photo)
+    # m3 = dict(sess=sess3, input_photo=input_photo, output_photo=output_photo)
+    models = type('', (), {})()
+    models.m1 = m1
+    #models.m2 = m2
+    # models.m3 = m3
+    return models
+
+
+
+def make_target_output_filename(  mname,checkpoint, fn='',res1=0,abc=0, ext='.jpg',svrtype="s1", modelid='', suffix='', xtra_model_id='',verbose=False):
+    fn_base=fn.replace(ext,"")
+    fn_base=fn_base.replace(".jpg","")
+    fn_base=fn_base.replace(".jpeg","")
+    fn_base=fn_base.replace(".JPG","")
+    fn_base=fn_base.replace(".JPEG","")
+    fn_base=fn_base.replace(".png","")
+    fn_base=fn_base.replace(".PNG","")
+    
+    #pad res1 and res2 to 4 digits
+    res1_pad=str(res1).zfill(4)
+    
+    abc_pad=str(abc).zfill(2)
+    if res1_pad=="0000":
+        res1_pad=""
+        
+    
+    #pad checkpoint to 3 digits
+    checkpoint=checkpoint.zfill(3)
+    
+    if fn_base=="none":
+        fn_base=""
+    
+    if '/' in fn_base:
+        fn_base=fn_base.split('/')[-1]
+    # Print out all input info:
+    if verbose  :
+            
+        print("-----------------------------")
+        print("fn_base: ",fn_base)
+        print("mname: ",mname)
+        print("suffix: ",suffix)
+        print("res1: ",res1_pad)
+        print("abc: ",abc_pad)
+        print("ext: ",ext)
+        print("svrtype: ",svrtype)
+        print("modelid: ",modelid)
+        print("xtra_model_id: ",xtra_model_id)
+        print("checkpoint: ",checkpoint)
+        print("fn: ",fn)
+    
+    mtag = "{}__{}__{}x{}__{}__{}k".format(mname,suffix,res1_pad,abc_pad, svrtype, checkpoint).replace("_0x" + str(abc_pad), "")
+    if verbose:
+        print(mtag)
+    target_output = "{}__{}__{}{}{}".format(fn_base, modelid, mtag, xtra_model_id, ext).replace("_"+str(abc_pad)+"x"+str(abc_pad)+"_","").replace("_0x0_", "").replace("_0_", "").replace("_-", "_").replace("____", "__").replace("___", "__").replace("___", "__").replace("..",".").replace("model_","").replace("_x"+str(abc_pad)+"_","").replace("gia-ds-","")
+    target_output = replace_values_from_csv(target_output)
+
+    return target_output
+
+def replace_values_from_csv(target_output):
+    # Implement the logic to replace values from CSV
+    #load replacer.csv and replace the values (src,dst)
+    src_dest_file = 'replacer.csv'
+    if os.path.exists(src_dest_file):
+        with open(src_dest_file, 'r') as file:
+            lines = file.readlines()
+            for line in lines:
+                src, dst = line.split(',')
+                target_output = target_output.replace(src, dst)
+    return target_output.replace("\n", "").replace("\r", "").replace(" ", "_")
+    
+
+def _make_meta_as_json(x1=0,c1=0,inp=None,result_dict=None):
+    global found_model,found_model_checkpoint
+    fn='none'
+    if inp['fn'] != 'none':
+        fn=inp['fn']
+    ext='.jpg'
+    if inp['ext'] != '.jpg':
+        ext=inp['ext']
+    
+    filename=make_target_output_filename(found_model,found_model_checkpoint,fn,x1,c1,ext,SRV_TYPE)
+    
+    if result_dict is None:
+        json_return = {
+            "model": str(found_model),
+            "checkpoint": str(found_model_checkpoint),
+            "filename": str(filename)
+        }
+        return json_return
+    else: #support adding to the existing dict the data directly
+        result_dict['model']=str(found_model)
+        result_dict['checkpoint']=str(found_model_checkpoint)
+        result_dict['filename']=str(filename)
+        return result_dict
+    
+
+
+meta_inputs={'meta':text}
+meta_outputs={'model':text,'filename':text,'checkpoint':text}
+
+@runway.command('meta2', inputs=meta_inputs, outputs=meta_outputs)
+def get_geta(models, inp):
+    global found_model,found_model_checkpoint
+    
+    json_return = _make_meta_as_json()
+        # "files": "nothing yet"
+    print(json_return)
+    return json_return 
+
+
+
+@runway.command('meta', inputs=meta_inputs, outputs=meta_outputs)
+def get_geta(models, inp):
+    global found_model,found_model_checkpoint
+    
+    json_return = _make_meta_as_json(inp)
+        # "files": "nothing yet"
+    print(json_return)
+    return json_return 
+
+
+
+
+#@STCGoal add number or text to specify resolution of the three pass
+inputs={'contentImage': runway.image,'x1':number(default=1024,min=24,max=18000),'c1':number(default=0,min=-99,max=99),'fn':text(default='none'),'ext':text(default='.jpg')}
+outputs={'stylizedImage': runway.image,'totaltime':number,'x1': number,'c1':number,'model1name':text,'checkpoint':text,'filename':text,'model':text}
+
+@runway.command('stylize', inputs=inputs, outputs=outputs)
+def stylize(models, inp):
+    global found_model,found_model_checkpoint,model1name
+    start = time.time()
+    
+    model = models.m1
+    #model2 = models.m2
+    # model3 = models.m3
+    
+    #Getting our names back (even though I think we dont need)
+    #@STCIssue BUGGED
+    # m1name=models.m1.name
+    # m2name=models.m2.name
+    # m3name=models.m3.name
+
+    #get size from inputs rather than env
+    x1 = int(inp['x1'])
+    
+    c1 = int(inp['c1'])
+
+    #
+    img = inp['contentImage']
+    img = np.array(img)
+    img = img / 127.5 - 1.
+
+    #@a Pass 1 RESIZE to 1368px the smaller side
+    image_size=pass1_image_size
+    image_size=x1
+    img_shape = img.shape[:2]
+    alpha = float(image_size) / float(min(img_shape))
+    #dtprint ("DEBUG::content.imgshape:" +   str(tuple(img_shape)) + ", alpha:" + str(alpha))
+
+    try:
+        img = scipy.misc.imresize(img, size=alpha)
+    except:
+        pass
+        
+
+    img = np.expand_dims(img, axis=0)
+    #@a INFERENCE PASS 1
+    dtprint("INFO:Pass1 inference starting")
+    img = model['sess'].run(model['output_photo'], feed_dict={model['input_photo']: img})
+    
+    #
+    img = (img + 1.) * 127.5
+    img = img.astype('uint8')
+    img = img[0]
+    #dtprint("INFO:Upresing Pass1 for Pass 2 (STARTING) ")
+
+    #@a Pass 2 RESIZE to 1024px the smaller side
+    #image_size=pass2_image_size
+    #image_size=x2
+    #img_shape = img.shape[:2]
+    
+    
+    #alpha = float(image_size) / float(min(img_shape))
+    #dtprint ("DEBUG::pass1.imgshape:" +   str(tuple(img_shape)) + ", alpha:" + str(alpha))
+
+    #img = scipy.misc.imresize(img, size=alpha)
+    #dtprint("INFO:Upresing Pass1 (DONE) ")
+
+    #Iteration 2    
+    #img = np.array(img)
+    #img = img / 127.5 - 1.
+    #img = np.expand_dims(img, axis=0)
+    #@a INFERENCE PASS 2 using the same model
+    #dtprint("INFO:Pass2 inference (STARTING)")
+    #img = model['sess'].run(model['output_photo'], feed_dict={model['input_photo']: img})
+    #dtprint("INFO:Pass2 inference (DONE)")
+    #img = (img + 1.) * 127.5
+    #img = img.astype('uint8')
+    #img = img[0]
+
+
+
+    # #pass3
+
+    # #@a Pass 3 RESIZE to 2048px the smaller side
+    # image_size=pass3_image_size
+    # image_size=x3
+    # img_shape = img.shape[:2]
+    
+    
+    # alpha = float(image_size) / float(min(img_shape))
+    # dtprint ("DEBUG::pass2.imgshape:" +   str(tuple(img_shape)) + ", alpha:" + str(alpha))
+
+    # img = scipy.misc.imresize(img, size=alpha)
+    # dtprint("INFO:Upresing Pass2 (DONE) ")
+
+    # #Iteration 3
+    # img = np.array(img)
+    # img = img / 127.5 - 1.
+    # img = np.expand_dims(img, axis=0)
+    # #@a INFERENCE PASS 3
+    # dtprint("INFO:Pass3 inference (STARTING)")
+    # img = model3['sess'].run(model3['output_photo'], feed_dict={model3['input_photo']: img})
+    # dtprint("INFO:Pass3 inference (DONE)")
+    # img = (img + 1.) * 127.5
+    # img = img.astype('uint8')
+    # img = img[0]
+    # #pass3
+
+    #dtprint("INFO:Composing done")
+
+    if c1 != 0 :
+        print('Auto Brightening images...' + str(c1))
+        img = img, alpha2, beta = automatic_brightness_and_contrast(img,c1)
+
+    stop = time.time()
+    totaltime = stop - start
+    print("The time of the run:", totaltime)
+    
+    #if model1name UNNAMED, use found_model
+    if model1name == "UNNAMED":
+        model1name=found_model
+    
+    include_meta_directly_in_result=True
+    
+    
+    if include_meta_directly_in_result:
+        result_dict = dict(stylizedImage=img,totaltime=totaltime,x1=x1,model1name=model1name,c1=c1)
+        result_dict = _make_meta_as_json(x1,c1,inp,result_dict)
+    else:
+        meta_data = _make_meta_as_json(x1,c1,inp)
+        result_dict = dict(stylizedImage=img,totaltime=totaltime,x1=x1,model1name=model1name,c1=c1,meta=meta_data)
+        
+    return result_dict
+
+
+
+def dtprint(msg):
+    dttag=getdttag()
+    print(dttag + "::" + msg  )
+
+def getdttag():
+    # datetime object containing current date and time
+    now = datetime.now()    
+
+    # dd/mm/YY H:M:S
+    # dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
+    return now.strftime("%H:%M:%S")
+
+
+
+# Automatic brightness and contrast optimization with optional histogram clipping
+def automatic_brightness_and_contrast(image, clip_hist_percent=25):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Calculate grayscale histogram
+    hist = cv2.calcHist([gray],[0],None,[256],[0,256])
+    hist_size = len(hist)
+
+    # Calculate cumulative distribution from the histogram
+    accumulator = []
+    accumulator.append(float(hist[0]))
+    for index in range(1, hist_size):
+        accumulator.append(accumulator[index -1] + float(hist[index]))
+
+    # Locate points to clip
+    maximum = accumulator[-1]
+    clip_hist_percent *= (maximum/100.0)
+    clip_hist_percent /= 2.0
+
+    # Locate left cut
+    minimum_gray = 0
+    while accumulator[minimum_gray] < clip_hist_percent:
+        minimum_gray += 1
+
+    # Locate right cut
+    maximum_gray = hist_size -1
+    while accumulator[maximum_gray] >= (maximum - clip_hist_percent):
+        maximum_gray -= 1
+
+    # Calculate alpha and beta values
+    alpha = 255 / (maximum_gray - minimum_gray)
+    beta = -minimum_gray * alpha
+
+    '''
+    # Calculate new histogram with desired range and show histogram 
+    new_hist = cv2.calcHist([gray],[0],None,[256],[minimum_gray,maximum_gray])
+    plt.plot(hist)
+    plt.plot(new_hist)
+    plt.xlim([0,256])
+    plt.show()
+    '''
+
+    auto_result = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)
+    return (auto_result, alpha, beta)
+
+
+if __name__ == '__main__':
+    #print('External Service port is:' +os.environ.get('SPORT'))    
+    os.environ["RW_PORT"] = "7860"
+    print("Launched...")
+    runway.run()

requirements.txt

@@ -0,0 +1,69 @@
+absl-py
+asn1crypto
+astor
+backports.weakref
+Brotli
+certifi
+cffi
+chardet
+click
+colorcet
+conda
+conda-package-handling
+cryptography
+enum34
+Flask
+Flask-Compress
+Flask-Cors
+Flask-Sockets
+funcsigs
+functools32
+futures
+gast
+gevent
+gevent-websocket
+google-pasta
+greenlet
+grpcio
+h5py
+idna
+ipaddress
+itsdangerous
+Jinja2
+Keras-Applications
+Keras-Preprocessing
+Markdown
+MarkupSafe
+mock
+numpy
+opencv-python
+opt-einsum
+packaging
+param
+Pillow
+protobuf
+pycosat
+pycparser
+pycrypto
+pyct
+pyOpenSSL
+pyparsing
+PySocks
+PyYAML
+requests
+runway-model-runner
+runway-python
+scipy
+six
+tensorboard
+tensorflow
+tensorflow-estimator
+termcolor
+tqdm
+Unidecode
+urllib3
+Werkzeug
+wget
+wrapt
+zope.event
+zope.interface

server.py

@@ -0,0 +1,59 @@
+import os
+import numpy as np
+import tensorflow as tf
+from module import encoder, decoder
+from glob import glob
+import runway
+
+
+@runway.setup(options={"styleCheckpoint": runway.file(is_directory=True)})
+def setup(opts):
+    sess = tf.Session()
+    init_op = tf.global_variables_initializer()
+    sess.run(init_op)
+    with tf.name_scope("placeholder"):
+        input_photo = tf.placeholder(
+            dtype=tf.float32, shape=[1, None, None, 3], name="photo"
+        )
+    input_photo_features = encoder(
+        image=input_photo, options={"gf_dim": 32}, reuse=False
+    )
+    output_photo = decoder(
+        features=input_photo_features, options={"gf_dim": 32}, reuse=False
+    )
+    saver = tf.train.Saver()
+    path = opts["styleCheckpoint"]
+    model_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][
+        0
+    ]
+    checkpoint_dir = os.path.join(path, model_name, "checkpoint_long")
+    ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
+    ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
+    saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
+    return dict(sess=sess, input_photo=input_photo, output_photo=output_photo)
+
+
+@runway.command(
+    "stylize",
+    inputs={"contentImage": runway.image},
+    outputs={"stylizedImage": runway.image},
+)
+def stylize(model, inp):
+    img = inp["contentImage"]
+    img = np.array(img)
+    img = img / 127.5 - 1.0
+    img = np.expand_dims(img, axis=0)
+    img = model["sess"].run(
+        model["output_photo"], feed_dict={model["input_photo"]: img}
+    )
+    img = (img + 1.0) * 127.5
+    img = img.astype("uint8")
+    img = img[0]
+    return dict(stylizedImage=img)
+
+
+if __name__ == "__main__":
+    #print("External Service port is:" + os.environ.get("SPORT",7860))
+    #set env var: RW_PORT=7860
+    os.environ["RW_PORT"] = "7860"
+    runway.run()