Spaces:
Running
Running
File size: 19,106 Bytes
569f484 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 |
import sys
sys.path.append('XGBoost_Prediction_Model/')
import warnings
warnings.filterwarnings("ignore")
import cv2 as cv
import numpy as np
from pulp import *
import Predict
import torch
from torchvision.io import read_image
#Global Paths for Models and Dictionaries
text_detection_model_path = 'XGBoost_Prediction_Model/EAST-Text-Detection/frozen_east_text_detection.pb'
LDA_model_pth = 'XGBoost_Prediction_Model/LDA_Model_trained/lda_model_best_tot.model'
training_ad_text_dictionary_path = 'XGBoost_Prediction_Model/LDA_Model_trained/object_word_dictionary'
training_lang_preposition_path = 'XGBoost_Prediction_Model/LDA_Model_trained/dutch_preposition'
def Preference_Matrix(Magazine_Pages, Magazine_Slots, Ad_Groups, Ad_Element_Sizes,
Ad_embeddings, Ctpg_embeddings,
Textboxes=None, Obj_and_Topics=None, Costs=None,
Method='XGBoost'):
#Magazine_Pages: A list containing all paths to Magazine Ads and Editorials
#Magazine_Slots: 0 (right), 1 (left), 2 (full-page)
#Costs Specification
if Costs is None:
Costs = np.ones(len(Magazine_Pages))
#Separate Images into Ads and Counterpages
Ads = []
Counterpages = []
Assign_ids = []
Costs_Ctpg = []
ad_locations = []
prod_groups = []
ad_elements = []
ad_embeds = []
ctpg_embeds = []
if Textboxes is not None:
ad_textbox = []; ctpg_textbox = []
if Obj_and_Topics is not None:
ad_num_obj = []; ctpg_num_obj = []
ad_topic_weight = []; ctpg_topic_weight = []
double_page_ad_attention = []
double_page_brand_attention = []
for i, path in enumerate(Magazine_Pages):
if Magazine_Slots[i] == 2:
if Textboxes is None:
textboxes_curr = None
else:
textboxes_curr = Textboxes[i]
if Obj_and_Topics is None:
obj_and_topics_curr = None
else:
obj_and_topics_curr = Obj_and_Topics[i]
if Method == 'XGBoost':
ad_attention = Predict.Ad_Gaze_Prediction(input_ad_path=path, input_ctpg_path=None, ad_location=None,
text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path,
training_language='dutch', ad_embeddings=Ad_embeddings[i].reshape(1,768), ctpg_embeddings=Ctpg_embeddings[i].reshape(1,768),
surface_sizes=list(Ad_Element_Sizes[i]), Product_Group=list(Ad_Groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr,
obj_detection_model_pth=None, num_topic=20, Gaze_Time_Type='Ad', Info_printing=False)
brand_attention = Predict.Ad_Gaze_Prediction(input_ad_path=path, input_ctpg_path=None, ad_location=None,
text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth,
training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path,
training_language='dutch', ad_embeddings=Ad_embeddings[i].reshape(1,768), ctpg_embeddings=Ctpg_embeddings[i].reshape(1,768),
surface_sizes=list(Ad_Element_Sizes[i]), Product_Group=list(Ad_Groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr,
obj_detection_model_pth=None, num_topic=20, Gaze_Time_Type='Brand', Info_printing=False)
elif Method == 'CNN':
img_curr = read_image(path)[:,89:921,:].unsqueeze(0)
ad_img_CNN = img_curr[:,:,:,:640]
ctpg_img_CNN = img_curr[:,:,:,640:]
ad_location = torch.tensor([[1,1]])
ad_attention = Predict.CNN_Prediction(ad_img_CNN, ctpg_img_CNN, ad_location, Gaze_Type='AG').item()
brand_attention = Predict.CNN_Prediction(ad_img_CNN, ctpg_img_CNN, ad_location, Gaze_Type='BG').item()
double_page_ad_attention.append(ad_attention/Costs[i])
double_page_brand_attention.append(brand_attention/Costs[i])
else:
Assign_ids.append(i)
img_curr = cv.imread(path)
img_curr = cv.resize(img_curr, (1280,1024))
_, w, _ = img_curr.shape
page_width = w // 2
ad_locations.append(1-Magazine_Slots[i])
ctpg_location = Magazine_Slots[i]
ad_img = img_curr[:, (Magazine_Slots[i]*page_width):((Magazine_Slots[i]+1)*page_width)]
ctpg_img = img_curr[:, (ctpg_location*page_width):((ctpg_location+1)*page_width)]
Ads.append(ad_img)
Counterpages.append(ctpg_img)
prod_groups.append(Ad_Groups[i])
ad_elements.append(Ad_Element_Sizes[i])
Costs_Ctpg.append(Costs[i])
ad_embeds.append(Ad_embeddings[i])
ctpg_embeds.append(Ctpg_embeddings[i])
if Textboxes is not None:
ad_textbox_curr, ctpg_textbox_curr = Textboxes[i]
ad_textbox.append(ad_textbox_curr); ctpg_textbox.append(ctpg_textbox_curr)
if Obj_and_Topics is not None:
ad_obj_curr, ctpg_obj_curr, ad_topic_curr, ctpg_topic_curr = Obj_and_Topics[i]
ad_num_obj.append(ad_obj_curr); ctpg_num_obj.append(ctpg_obj_curr)
ad_topic_weight.append(ad_topic_curr); ctpg_topic_weight.append(ctpg_topic_curr)
Ad_Attention_Preference = np.zeros((len(Ads),len(Counterpages)))
Brand_Attention_Preference = np.zeros((len(Ads),len(Counterpages)))
for i, ad in enumerate(Ads):
print('Ad '+str(i)+" Assigning...")
if Method == 'CNN':
ad_images_stack = []
ctpg_images_stack = []
ad_locations_stack = []
for j, ctpg in enumerate(Counterpages):
# if ad_locations[j] == 0:
# new_image = np.concatenate((ad,ctpg),axis=1)
# else:
# new_image = np.concatenate((ctpg,ad),axis=1)
if Textboxes is not None:
textboxes_curr = [ad_textbox[i],ctpg_textbox[j]]
else:
textboxes_curr = None
if Obj_and_Topics is not None:
obj_and_topics_curr = [ad_num_obj[i],ctpg_num_obj[j],ad_topic_weight[i],ctpg_topic_weight[j]]
else:
obj_and_topics_curr = None
if Method == 'XGBoost':
ad_attention = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=ctpg,
text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth,
training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path,
training_language='dutch', ad_embeddings=ad_embeds[i].reshape(1,768), ctpg_embeddings=ctpg_embeds[i].reshape(1,768),
surface_sizes=list(ad_elements[i]), Product_Group=list(prod_groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr,
obj_detection_model_pth=None, ad_location=ad_locations[j], num_topic=20, Gaze_Time_Type='Ad', Info_printing=False)
brand_attention = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=ctpg,
text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth,
training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path,
training_language='dutch', ad_embeddings=ad_embeds[i].reshape(1,768), ctpg_embeddings=ctpg_embeds[i].reshape(1,768),
surface_sizes=list(ad_elements[i]), Product_Group=list(prod_groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr,
obj_detection_model_pth=None, ad_location=ad_locations[j], num_topic=20, Gaze_Time_Type='Brand', Info_printing=False)
Ad_Attention_Preference[i,j] = ad_attention/Costs_Ctpg[j]
Brand_Attention_Preference[i,j] = brand_attention/Costs_Ctpg[j]
elif Method == 'CNN':
ad_img_CNN = torch.tensor(ad).permute(2,0,1).unsqueeze(0)[:,:,89:921,:]
ad_images_stack.append(ad_img_CNN)
ctpg_img_CNN = torch.tensor(ctpg).permute(2,0,1).unsqueeze(0)[:,:,89:921,:]
ctpg_images_stack.append(ctpg_img_CNN)
ad_locations_stack.append(torch.tensor([[1,0]]))
# ad_attention = Predict.CNN_Prediction(ad_img_CNN, ctpg_img_CNN, ad_location, Gaze_Type='AG').item()
# brand_attention = Predict.CNN_Prediction(ad_img_CNN, ctpg_img_CNN, ad_location, Gaze_Type='BG').item()
if Method == 'CNN':
ad_images_stack = torch.cat(ad_images_stack,dim=0)
ctpg_images_stack = torch.cat(ctpg_images_stack,dim=0)
ad_locations_stack = torch.cat(ad_locations_stack,dim=0)
ad_attentions = Predict.CNN_Prediction(ad_images_stack, ctpg_images_stack, ad_locations_stack, Gaze_Type='AG').to('cpu').squeeze()
brand_attentions = Predict.CNN_Prediction(ad_images_stack, ctpg_images_stack, ad_locations_stack, Gaze_Type='BG').to('cpu').squeeze()
Ad_Attention_Preference[i] = ad_attentions.numpy()/np.array(Costs_Ctpg)
Brand_Attention_Preference[i] = brand_attentions.numpy()/np.array(Costs_Ctpg)
return Ad_Attention_Preference, Brand_Attention_Preference, double_page_ad_attention, double_page_brand_attention, Assign_ids
def Preference_Matrix_different_magazine(Magzine_Target, Magzine_Ad,
Magazine_Slots_Target, Magazine_Slots_Ad,
Ad_Groups, Ad_Element_Sizes,
Textboxes_Target=None, Textboxes_Ad=None,
Obj_and_Topics_Target=None, Obj_and_Topics_Ad=None,
Costs=None):
#Separate Images into Ads and Counterpage
Ads = []
Counterpages = []
Assign_ids_target = []
Assign_ids_ad = []
ad_locations = []
prod_groups = []
ad_elements = []
if Textboxes_Target is not None:
ad_textbox = []; ctpg_textbox = []
if Obj_and_Topics_Target is not None:
ad_num_obj = []; ctpg_num_obj = []
ad_topic_weight = []; ctpg_topic_weight = []
double_page_ad_attention = []
double_page_brand_attention = []
#Target magazine (Counterpage)
for i, path in enumerate(Magzine_Target):
if Magazine_Slots_Target[i] == 2:
continue
else:
Assign_ids_target.append(i)
img_curr = cv.imread(path)
img_curr = cv.resize(img_curr, (1280,1024))
_, w, _ = img_curr.shape
page_width = w // 2
ad_locations.append(Magazine_Slots_Target[i])
ctpg_location = 1-Magazine_Slots_Target[i]
ctpg_img = img_curr[:, (ctpg_location*page_width):((ctpg_location+1)*page_width)]
Counterpages.append(ctpg_img)
if Textboxes_Target is not None:
_, ctpg_textbox_curr = Textboxes_Target[i]
ctpg_textbox.append(ctpg_textbox_curr)
if Obj_and_Topics_Target is not None:
_, ctpg_obj_curr, _, ctpg_topic_curr = Obj_and_Topics_Target[i]
ctpg_num_obj.append(ctpg_obj_curr)
ctpg_topic_weight.append(ctpg_topic_curr)
#Ad magazine (Ads)
for i, path in enumerate(Magzine_Ad):
if Magazine_Slots_Ad[i] == 2:
if Textboxes_Ad is None:
textboxes_curr = None
else:
textboxes_curr = Textboxes_Ad[i]
if Obj_and_Topics_Ad is None:
obj_and_topics_curr = None
else:
obj_and_topics_curr = Obj_and_Topics_Ad[i]
ad_attention = Predict.Ad_Gaze_Prediction(input_ad_path=path, input_ctpg_path=None, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth,
training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch',
surface_sizes=list(Ad_Element_Sizes[i]), Product_Group=list(Ad_Groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr,
obj_detection_model_pth=None, ad_location=None, num_topic=20, Gaze_Time_Type='Ad', Info_printing=False)
brand_attention = Predict.Ad_Gaze_Prediction(input_ad_path=path, input_ctpg_path=None, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth,
training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch',
surface_sizes=list(Ad_Element_Sizes[i]), Product_Group=list(Ad_Groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr,
obj_detection_model_pth=None, ad_location=None, num_topic=20, Gaze_Time_Type='Brand', Info_printing=False)
double_page_ad_attention.append(ad_attention)
double_page_brand_attention.append(brand_attention)
else:
Assign_ids_ad.append(i)
img_curr = cv.imread(path)
img_curr = cv.resize(img_curr, (1280,1024))
_, w, _ = img_curr.shape
page_width = w // 2
ad_img = img_curr[:, (Magazine_Slots_Ad[i]*page_width):((Magazine_Slots_Ad[i]+1)*page_width)]
Ads.append(ad_img)
prod_groups.append(Ad_Groups[i])
ad_elements.append(Ad_Element_Sizes[i])
if Textboxes_Ad is not None:
ad_textbox_curr, _ = Textboxes_Ad[i]
ad_textbox.append(ad_textbox_curr)
if Obj_and_Topics_Ad is not None:
ad_obj_curr, _, ad_topic_curr, _ = Obj_and_Topics_Ad[i]
ad_num_obj.append(ad_obj_curr)
ad_topic_weight.append(ad_topic_curr)
#Check costs on Ad position
if Costs is None:
Costs = np.ones(len(Counterpages))
#Matrix
if len(Ads) > len(Counterpages):
return None
else:
Ad_Attention_Preference = np.zeros((len(Ads),len(Counterpages)))
Brand_Attention_Preference = np.zeros((len(Ads),len(Counterpages)))
for i, ad in enumerate(Ads):
print('Ad '+str(i)+" Assigning...")
for j, ctpg in enumerate(Counterpages):
# if ad_locations[j] == 0:
# new_image = np.concatenate((ad,ctpg),axis=1)
# else:
# new_image = np.concatenate((ctpg,ad),axis=1)
if Textboxes_Target is not None:
textboxes_curr = [ad_textbox[i],ctpg_textbox[j]]
else:
textboxes_curr = None
if Obj_and_Topics_Target is not None:
obj_and_topics_curr = [ad_num_obj[i],ctpg_num_obj[j],ad_topic_weight[i],ctpg_topic_weight[j]]
else:
obj_and_topics_curr = None
ad_attention = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=ctpg, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth,
training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch',
surface_sizes=list(ad_elements[i]), Product_Group=list(prod_groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr,
obj_detection_model_pth=None, ad_location=ad_locations[j], num_topic=20, Gaze_Time_Type='Ad', Info_printing=False)
brand_attention = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=ctpg, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth,
training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch',
surface_sizes=list(ad_elements[i]), Product_Group=list(prod_groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr,
obj_detection_model_pth=None, ad_location=ad_locations[j], num_topic=20, Gaze_Time_Type='Brand', Info_printing=False)
Ad_Attention_Preference[i,j] = ad_attention/Costs[j]
Brand_Attention_Preference[i,j] = brand_attention/Costs[j]
return Ad_Attention_Preference, Brand_Attention_Preference, double_page_ad_attention, double_page_brand_attention, Assign_ids_ad, Assign_ids_target
def Assignment_Problem(costs, workers, jobs):
#https://machinelearninggeek.com/solving-assignment-problem-using-linear-programming-in-python/
prob = LpProblem("Assignment Problem", LpMinimize)
# The cost data is made into a dictionary
costs= makeDict([workers, jobs], costs, 0)
# Creates a list of tuples containing all the possible assignments
assign = [(w, j) for w in workers for j in jobs]
# A dictionary called 'Vars' is created to contain the referenced variables
vars = LpVariable.dicts("Assign", (workers, jobs), 0, None, LpBinary)
# The objective function is added to 'prob' first
prob += (
lpSum([vars[w][j] * costs[w][j] for (w, j) in assign]),
"Sum_of_Assignment_Costs",
)
# There are row constraints. Each job can be assigned to only one employee.
for j in jobs:
prob+= lpSum(vars[w][j] for w in workers) == 1
# There are column constraints. Each employee can be assigned to only one job.
for w in workers:
prob+= lpSum(vars[w][j] for j in jobs) == 1
# The problem is solved using PuLP's choice of Solver
prob.solve()
return prob
|