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utils.py
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utils.py
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import os
import random
import json
def get_image(img_path, seg_num):
img_list = [os.path.join(img_path, img) for img in os.listdir(img_path)]
sampled_images = random.sample(img_list, seg_num)
return sampled_images
def create_question(question_id, image, Object, label, template):
question = dict()
question["question_id"] = question_id
question["image"] = image
template1 = template
template2 = template.replace("a", "an")
if Object[0] not in ["a", "e", "i", "o", "u"]:
question["text"] = template1.format(Object)
elif Object[0] in ["a", "e", "i", "o", "u"]:
question["text"] = template2.format(Object)
question["label"] = label
return question
def pope(ground_truth_objects, segment_results, sample_num, template, neg_strategy, save_path, dataset):
question_list = []
question_id = 1
output_file = os.path.join(save_path, dataset + "_pope_" + neg_strategy + ".json")
gt_objects_list = list(ground_truth_objects.keys())
sorted_objects = sorted(ground_truth_objects.items(), key=lambda x: x[1], reverse=True)
sorted_co_occur = compute_co_occurrence(segment_results, save_path, dataset)
for image in segment_results:
history_object_list = []
# Positive sampling
for i in range(sample_num):
pos_object = image["objects"][i]
history_object_list.append(pos_object)
question = create_question(question_id, image['image'], pos_object, 'yes', template)
question_list.append(question)
question_id += 1
# Negative sampling (random)
if neg_strategy == "random":
selected_object = random.choice(gt_objects_list)
while selected_object in history_object_list or selected_object in image["objects"]:
selected_object = random.choice(gt_objects_list)
history_object_list.append(selected_object)
question = create_question(question_id, image["image"], selected_object, 'no', template)
question_list.append(question)
question_id += 1
# Negative sampling (popular)
elif neg_strategy == "popular":
flag = 0
for j in range(len(sorted_objects)):
selected_object = sorted_objects[j][0]
if selected_object not in history_object_list and selected_object not in image["objects"]:
history_object_list.append(selected_object)
question = create_question(question_id, image["image"], selected_object, 'no', template)
question_list.append(question)
question_id += 1
flag = 1
break
# In case no object is selected
if not flag:
while True:
selected_object = random.choice(gt_objects_list)
if selected_object not in history_object_list and selected_object not in image["objects"]:
history_object_list.append(selected_object)
question = create_question(question_id, image["image"], selected_object, 'no', template)
question_list.append(question)
question_id += 1
break
# Negative sampling (Adversarial)
elif neg_strategy == "adversarial":
flag = 0
for j in range(len(sorted_co_occur[pos_object])):
selected_object = sorted_co_occur[pos_object][j]
if selected_object not in history_object_list and selected_object not in image["objects"]:
history_object_list.append(selected_object)
question = create_question(question_id, image["image"], selected_object, 'no', template)
question_list.append(question)
question_id += 1
flag = 1
break
if not flag:
while True:
selected_object = random.choice(gt_objects_list)
if selected_object not in history_object_list and selected_object not in image["objects"]:
history_object_list.append(selected_object)
question = create_question(question_id, image["image"], selected_object, 'no', template)
question_list.append(question)
question_id += 1
break
with open(output_file, 'w') as f:
for question in question_list:
json_str = json.dumps(question)
f.write(json_str + "\n")
def generate_ground_truth_objects(segment_results, save_path, dataset):
gt_objects = dict()
output_file = os.path.join(save_path, dataset + "_ground_truth_objects.json")
for image in segment_results:
seg = image['objects']
for o in seg:
if o not in gt_objects:
gt_objects[o] = 1
else:
gt_objects[o] += 1
with open(output_file, 'w') as f:
json_str = json.dumps(gt_objects)
f.write(json_str)
return gt_objects
def compute_co_occurrence(segment_results, save_path, dataset):
output_file = os.path.join(save_path, dataset + "_co_occur.json")
co_occur = dict()
for image in segment_results:
objects = image["objects"]
for o in objects:
if o not in co_occur:
co_occur[o] = dict()
for other_o in objects:
if o == other_o:
continue
if other_o not in co_occur[o]:
co_occur[o][other_o] = 1
else:
co_occur[o][other_o] += 1
sorted_co_occur = dict()
for o in co_occur:
objects = co_occur[o]
sorted_co_occur_objects = sorted(objects.items(), key=lambda x: x[1], reverse=True)
sorted_co_occur[o] = [item[0] for item in sorted_co_occur_objects]
with open(output_file, 'w') as f:
json_str = json.dumps(sorted_co_occur)
f.write(json_str)
return sorted_co_occur