Initial commit

README.md

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+# Recognising Displaced People from Images by Exploiting Dominance Level
+
+<p align="center">
+  <img src="https://github.com/GKalliatakis/DisplaceNet/blob/master/DisplaceNet.png?raw=true"/>
+</p>
+
+
+**[Grigorios Kalliatakis](https://scholar.google.com/citations?user=LMY5lhwAAAAJ&hl=en&oi=ao)
+ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [Shoaib Ehsan](https://scholar.google.com/citations?user=40KlWugAAAAJ&hl=en)
+ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [Maria Fasli](https://scholar.google.com/citations?user=Hg2osmAAAAAJ&hl=en)
+ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [Klaus McDonald-Maier](https://scholar.google.com/citations?user=xYARJTQAAAAJ&hl=en) &nbsp; &nbsp;**
+
+**To appear in 1<sup>st</sup> CVPR Workshop on [COMPUTER VISION FOR GLOBAL CHALLENGES (CV4GC)](https://www.cv4gc.org/)**
+
+**[[arXiv preprint]]()**
+
+
+
+### Citing DisplaceNet
+
+Please cite our paper in your publications if it helps your research:
+
+    @inproceedings{
+    }

abusenet_evaluator_mini.py

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+from __future__ import print_function
+import os
+
+
+from sklearn.metrics import accuracy_score, classification_report, precision_score, confusion_matrix, average_precision_score
+from inference.displacenet_single_image_inference_unified import displaceNet_inference
+
+class AbuseNetBaseEvaluator(object):
+    """Perfofmance metrics base class.
+    """
+
+
+    def __init__(self,
+                 hra_model_backend_name,nb_of_conv_layers_to_fine_tune,
+                 emotic_model_a_backend_name,emotic_model_b_backend_name,emotic_model_c_backend_name,
+                 violation_class,
+                 main_test_dir ='/home/sandbox/Desktop/Human_Rights_Archive_DB/test',
+                 ):
+
+        self.hra_model_backend_name = hra_model_backend_name
+        self.nb_of_conv_layers_to_fine_tune = nb_of_conv_layers_to_fine_tune
+        self.emotic_model_a_backend_name = emotic_model_a_backend_name
+        self.emotic_model_b_backend_name = emotic_model_b_backend_name
+        self.emotic_model_c_backend_name = emotic_model_c_backend_name
+        self.main_test_dir = main_test_dir
+        self.total_nb_of_test_images = sum([len(files) for r, d, files in os.walk(main_test_dir)])
+        self.sorted_categories_names = sorted(os.listdir(main_test_dir))
+        self.violation_class = violation_class
+        self.y_true = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1,]
+
+
+
+    def _obtain_y_pred(self,
+                       prob_threshold=0.75):
+
+        y_pred = []
+        y_scores = []
+
+        predicted_class_list = []
+        actual_class_list = []
+        coverage_count = 0
+
+        for hra_class in self.sorted_categories_names:
+
+            # variable that contains the main dir alongside the selected category
+            tmp = os.path.join(self.main_test_dir, hra_class)
+            img_names = sorted(os.listdir(tmp))
+
+
+            for raw_img in img_names:
+                # variable that contains the final image to be loaded
+                print('Processing  [' + raw_img + ']')
+                final_img = os.path.join(tmp, raw_img)
+
+                preds = displaceNet_inference(img_path=final_img,
+                                              emotic_model_a_backend_name=self.emotic_model_a_backend_name,
+                                              emotic_model_b_backend_name=self.emotic_model_b_backend_name,
+                                              emotic_model_c_backend_name=self.emotic_model_c_backend_name,
+                                              hra_model_backend_name=self.hra_model_backend_name,
+                                              nb_of_fine_tuned_conv_layers=self.nb_of_conv_layers_to_fine_tune,
+                                              violation_class=self.violation_class)
+
+
+                preds = preds[0]
+
+                y_pred.append(int(preds[0][0]))
+                y_scores.append(preds[0][2])
+
+                top_1_predicted_probability = preds[0][2]
+
+                # top_1_predicted = np.argmax(preds)
+                top_1_predicted_label = preds[0][1]
+
+                if top_1_predicted_probability >= prob_threshold:
+                    coverage_count += 1
+
+                print ('`' + hra_class + '/' + raw_img + '`  ===>  `' +
+                       top_1_predicted_label + '`' + ' with ' + str(top_1_predicted_probability) + ' P')
+
+                predicted_class_list.append(top_1_predicted_label)
+                actual_class_list.append(hra_class)
+
+        total_coverage_per = (coverage_count * 100) / self.total_nb_of_test_images
+
+        return y_pred, self.y_true, y_scores, total_coverage_per
+
+
+
+if __name__ == "__main__":
+
+    violation_class = 'cl'
+    hra_model_backend_name = 'VGG16'
+    nb_of_conv_layers_to_fine_tune = 1
+
+    emotic_model_a_backend_name = 'VGG19'
+    emotic_model_b_backend_name = 'VGG16'
+    emotic_model_c_backend_name = None
+
+    model_backend_name = 'VGG16'
+
+    # server
+    # if violation_class == 'cl':
+    #     main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test/ChildLabour'
+    # elif violation_class =='dp':
+    #     main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test/DisplacedPopulations'
+
+    if violation_class == 'cl':
+        main_test_dir = '/home/sandbox/Desktop/HRA-2clas-full-test-mini/cl-1'
+    elif violation_class =='dp':
+        main_test_dir = '/home/sandbox/Desktop/HRA-2clas-full-test-mini/DisplacedPopulations'
+
+    # ---------------------------------------------------- #
+
+
+
+
+
+    base_evaluator = AbuseNetBaseEvaluator(hra_model_backend_name=hra_model_backend_name,nb_of_conv_layers_to_fine_tune=nb_of_conv_layers_to_fine_tune,
+                                           emotic_model_a_backend_name=emotic_model_a_backend_name,
+                                           emotic_model_b_backend_name=emotic_model_b_backend_name,
+                                           emotic_model_c_backend_name=emotic_model_c_backend_name,
+                                           violation_class=violation_class,
+                                           main_test_dir =main_test_dir,
+                                           )
+
+    y_pred, y_true, y_scores, total_coverage_per = base_evaluator._obtain_y_pred()
+
+    # print y_true
+    top1_acc = accuracy_score(y_true, y_pred)
+
+    AP = average_precision_score(y_true, y_scores, 'micro')
+
+
+    string = model_backend_name+'-'+violation_class+'-'+str(nb_of_conv_layers_to_fine_tune)+'layer(s)'
+
+    print('\n')
+    print( '============================= %s =============================' %string)
+    print(' Top-1 acc. =>  ' + str(top1_acc))
+    print(' Coverage =>  ' + str(total_coverage_per) + '%')
+    print(' Average Precision (AP)  =>  ' + str(AP) + '%')

abusenet_evaluator_v2.py

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+# -*- coding: utf-8 -*-
+'''Evaluates AbuseNet. Whole test-set cannot fit into the memory, therefore it is splitted into 5 folds of 10 images each.
+
+'''
+from __future__ import print_function
+import os
+import argparse
+import time
+from utils.generic_utils import hms_string
+
+
+from sklearn.metrics import accuracy_score, classification_report, precision_score, confusion_matrix, average_precision_score
+from inference.displacenet_single_image_inference_unified import displaceNet_inference
+
+class AbuseNetBaseEvaluator(object):
+    """Perfofmance metrics base class.
+    """
+
+
+    def __init__(self,
+                 hra_model_backend_name,nb_of_conv_layers_to_fine_tune,
+                 emotic_model_a_backend_name,emotic_model_b_backend_name,emotic_model_c_backend_name,
+                 violation_class,
+                 main_test_dir ='/home/sandbox/Desktop/Human_Rights_Archive_DB/test',
+                 ):
+
+        self.hra_model_backend_name = hra_model_backend_name
+        self.nb_of_conv_layers_to_fine_tune = nb_of_conv_layers_to_fine_tune
+        self.emotic_model_a_backend_name = emotic_model_a_backend_name
+        self.emotic_model_b_backend_name = emotic_model_b_backend_name
+        self.emotic_model_c_backend_name = emotic_model_c_backend_name
+        self.main_test_dir = main_test_dir
+        self.total_nb_of_test_images = sum([len(files) for r, d, files in os.walk(main_test_dir)])
+        self.sorted_categories_names = sorted(os.listdir(main_test_dir))
+        self.violation_class = violation_class
+        self.y_true = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+
+
+
+    def _obtain_y_pred(self,
+                       prob_threshold=0.75):
+
+        y_pred = []
+        y_scores = []
+
+        predicted_class_list = []
+        actual_class_list = []
+        coverage_count = 0
+
+        for hra_class in self.sorted_categories_names:
+
+            # variable that contains the main dir alongside the selected category
+            tmp = os.path.join(self.main_test_dir, hra_class)
+            img_names = sorted(os.listdir(tmp))
+
+            for raw_img in img_names:
+                # variable that contains the final image to be loaded
+                print(' Processing  [' + raw_img + ']')
+                final_img = os.path.join(tmp, raw_img)
+
+                preds = displaceNet_inference(img_path=final_img,
+                                              emotic_model_a_backend_name=self.emotic_model_a_backend_name,
+                                              emotic_model_b_backend_name=self.emotic_model_b_backend_name,
+                                              emotic_model_c_backend_name=self.emotic_model_c_backend_name,
+                                              hra_model_backend_name=self.hra_model_backend_name,
+                                              nb_of_fine_tuned_conv_layers=self.nb_of_conv_layers_to_fine_tune,
+                                              violation_class=self.violation_class)
+
+
+                preds = preds[0]
+
+                y_pred.append(int(preds[0][0]))
+                y_scores.append(preds[0][2])
+
+                top_1_predicted_probability = preds[0][2]
+
+                # top_1_predicted = np.argmax(preds)
+                top_1_predicted_label = preds[0][1]
+
+                if top_1_predicted_probability >= prob_threshold:
+                    coverage_count += 1
+
+                # print ('`' + hra_class + '/' + raw_img + '`  ===>  `' +
+                #        top_1_predicted_label + '`' + ' with ' + str(top_1_predicted_probability) + ' P')
+
+                print('     GT `' + hra_class + '`' + '  <-->  PRED. `' +
+                      top_1_predicted_label + '`' + '  with ' + str(top_1_predicted_probability))
+
+                print ('\n')
+
+                predicted_class_list.append(top_1_predicted_label)
+                actual_class_list.append(hra_class)
+
+        total_coverage_per = (coverage_count * 100) / self.total_nb_of_test_images
+
+        return y_pred, self.y_true, y_scores, total_coverage_per
+
+
+
+if __name__ == "__main__":
+
+    def get_args():
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--violation_class", type=str,
+                            help='One of `cl` or `dp`')
+
+        parser.add_argument("--fold_number", type=int, default=None,
+                            help="Number of selected subset to test (1-5)")
+
+        parser.add_argument("--hra_model_backend_name", type=str,
+                            help='One of `VGG16`, `VGG19`, `ResNet50`, `VGG16_Places365`')
+
+        parser.add_argument("--nb_of_conv_layers", type=int, default=None,
+                            help="Number of fine-tuned conv. layers")
+
+        parser.add_argument("--emotic_model_a_backend_name", type=str,
+                            help='One of `VGG16`, `VGG19`, `ResNet50`')
+
+        parser.add_argument("--emotic_model_b_backend_name", type=str,
+                            help='One of `VGG16`, `VGG19`, `ResNet50`', default=None)
+
+        parser.add_argument("--emotic_model_c_backend_name", type=str,
+                            help='One of `VGG16`, `VGG19`, `ResNet50`', default=None)
+
+        args = parser.parse_args()
+        return args
+
+
+    args = get_args()
+
+
+
+
+    # server
+    if args.violation_class == 'cl':
+        if args.fold_number == 1:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/cl-1'
+
+        elif args.fold_number == 2:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/cl-2'
+
+        elif args.fold_number == 3:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/cl-3'
+
+        elif args.fold_number == 4:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/cl-4'
+
+        elif args.fold_number == 5:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/cl-5'
+
+
+    elif args.violation_class =='dp':
+        if args.fold_number == 1:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/dp-1'
+
+        elif args.fold_number == 2:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/dp-2'
+
+        elif args.fold_number == 3:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/dp-3'
+
+        elif args.fold_number == 4:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/dp-4'
+
+        elif args.fold_number == 5:
+            main_test_dir = '/home/gkallia/git/AbuseNet/datasets/HRA-2clas-full-test-mini/dp-5'
+
+
+    # if violation_class == 'cl':
+    #     main_test_dir = '/home/sandbox/Desktop/HRA-2clas-full-test-mini/ChildLabour'
+    # elif violation_class =='dp':
+    #     main_test_dir = '/home/sandbox/Desktop/HRA-2clas-full-test-mini/DisplacedPopulations'
+
+    # ---------------------------------------------------- #
+
+
+
+
+
+    base_evaluator = AbuseNetBaseEvaluator(hra_model_backend_name=args.hra_model_backend_name,
+                                           nb_of_conv_layers_to_fine_tune=args.nb_of_conv_layers,
+                                           emotic_model_a_backend_name=args.emotic_model_a_backend_name,
+                                           emotic_model_b_backend_name=args.emotic_model_b_backend_name,
+                                           emotic_model_c_backend_name=args.emotic_model_c_backend_name,
+                                           violation_class=args.violation_class,
+                                           main_test_dir =main_test_dir,
+                                           )
+
+    start_time = time.time()
+
+    y_pred, y_true, y_scores, total_coverage_per = base_evaluator._obtain_y_pred()
+
+    end_time = time.time()
+    print("[INFO] It took {} to obtain AbuseNet predictions".format(hms_string(end_time - start_time)))
+
+    # print y_true
+    top1_acc = accuracy_score(y_true, y_pred)
+
+    AP = average_precision_score(y_true, y_scores, 'micro')
+
+
+    string = args.hra_model_backend_name+'-'+args.violation_class+'-'+str(args.nb_of_conv_layers)+'layer(s)-'+\
+             str(args.fold_number) +'fold-' +str(args.emotic_model_a_backend_name) \
+             +'-' +str(args.emotic_model_b_backend_name) +'-'+str(args.emotic_model_c_backend_name)
+
+    print('\n')
+    print( '============================= %s =============================' %string)
+    print(' Top-1 acc. =>  ' + str(top1_acc))
+    print(' Coverage =>  ' + str(total_coverage_per) + '%')
+    print(' Average Precision (AP)  =>  ' + str(AP) + '%')
+
+    text_file = open("25_March_dp_dominance_VGG16_Places365__VGG16.txt", "a+")
+    text_file.write( '============================= %s =============================\n' %string)
+    text_file.write('Acc. =>  ' + str(top1_acc)+'\n')
+    text_file.write('Coverage =>  ' + str(total_coverage_per) + '%\n')
+    text_file.write('Average Precision (AP)  =>  ' + str(AP) + '%\n')
+    text_file.write('\n')
+
+    text_file.close()
+
+    print("[INFO] Results for fold %s were successfully saved " %str(args.fold_number))
+