96 lines
No EOL
4.9 KiB
Python
96 lines
No EOL
4.9 KiB
Python
import os
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import pandas as pd
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import pickle
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from tqdm import tqdm
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def check_append(obj_name, m1, mind_name, obj_frame, flags, label):
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if label:
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if not obj_name in m1:
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m1[obj_name] = []
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m1[obj_name].append(
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[obj_frame.frame, [obj_frame.x_min, obj_frame.y_min, obj_frame.x_max, obj_frame.y_max], 0])
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flags[mind_name] = 1
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elif not flags[mind_name]:
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m1[obj_name].append(
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[obj_frame.frame, [obj_frame.x_min, obj_frame.y_min, obj_frame.x_max, obj_frame.y_max], 0])
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flags[mind_name] = 1
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else: # false belief
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if obj_name in m1:
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if flags[mind_name]:
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m1[obj_name].append(
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[obj_frame.frame, [obj_frame.x_min, obj_frame.y_min, obj_frame.x_max, obj_frame.y_max], 1])
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flags[mind_name] = 0
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return flags, m1
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def store_mind_set(clip, annotation_path, save_path):
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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annt = pd.read_csv(annotation_path + clip, sep=",", header=None)
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annt.columns = ["obj_id", "x_min", "y_min", "x_max", "y_max", "frame", "lost", "occluded", "generated", "name",
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"label"]
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obj_names = annt.name.unique()
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m1, m2, m12, m21, mc = {}, {}, {}, {}, {}
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flags = {'m1':0, 'm2':0, 'm12':0, 'm21':0, 'mc':0}
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for obj_name in obj_names:
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if obj_name == 'P1' or obj_name == 'P2':
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continue
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obj_frames = annt.loc[annt.name == obj_name]
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for index, obj_frame in obj_frames.iterrows():
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if type(obj_frame.label) == float:
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continue
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labels = obj_frame.label.split()
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for label in labels:
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if label == 'in_m1' or label == '"in_m1"':
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flags, m1 = check_append(obj_name, m1, 'm1', obj_frame, flags, 1)
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elif label == 'in_m2' or label == '"in_m2"':
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flags, m2 = check_append(obj_name, m2, 'm2', obj_frame, flags, 1)
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elif label == 'in_m12'or label == '"in_m12"':
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flags, m12 = check_append(obj_name, m12, 'm12', obj_frame, flags, 1)
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elif label == 'in_m21' or label == '"in_m21"':
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flags, m21 = check_append(obj_name, m21, 'm21', obj_frame, flags, 1)
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elif label == 'in_mc'or label == '"in_mc"':
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flags, mc = check_append(obj_name, mc, 'mc', obj_frame, flags, 1)
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elif label == 'm1_false' or label == '"m1_false"':
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flags, m1 = check_append(obj_name, m1, 'm1', obj_frame, flags, 0)
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flags, m12 = check_append(obj_name, m12, 'm12', obj_frame, flags, 0)
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flags, m21 = check_append(obj_name, m21, 'm21', obj_frame, flags, 0)
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false_belief = 'm1_false'
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with open(save_path + clip.split('.')[0] + '.txt', "a") as file:
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file.write(f"{obj_frame['frame']},{obj_name},{false_belief}\n")
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elif label == 'm2_false' or label == '"m2_false"':
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flags, m2 = check_append(obj_name, m2, 'm2', obj_frame, flags, 0)
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flags, m12 = check_append(obj_name, m12, 'm12', obj_frame, flags, 0)
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flags, m21 = check_append(obj_name, m21, 'm21', obj_frame, flags, 0)
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false_belief = 'm2_false'
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with open(save_path + clip.split('.')[0] + '.txt', "a") as file:
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file.write(f"{obj_frame['frame']},{obj_name},{false_belief}\n")
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elif label == 'm12_false' or label == '"m12_false"':
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flags, m12 = check_append(obj_name, m12, 'm12', obj_frame, flags, 0)
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flags, mc = check_append(obj_name, mc, 'mc', obj_frame, flags, 0)
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false_belief = 'm12_false'
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with open(save_path + clip.split('.')[0] + '.txt', "a") as file:
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file.write(f"{obj_frame['frame']},{obj_name},{false_belief}\n")
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elif label == 'm21_false' or label == '"m21_false"':
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flags, m21 = check_append(obj_name, m2, 'm21', obj_frame, flags, 0)
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flags, mc = check_append(obj_name, mc, 'mc', obj_frame, flags, 0)
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false_belief = 'm21_false'
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with open(save_path + clip.split('.')[0] + '.txt', "a") as file:
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file.write(f"{obj_frame['frame']},{obj_name},{false_belief}\n")
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# print('m1', m1)
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# print('m2', m2)
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# print('m12', m12)
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# print('m21', m21)
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# print('mc', mc)
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#with open(save_path + clip.split('.')[0] + '.p', 'wb') as f:
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# pickle.dump([m1, m2, m12, m21, mc], f)
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if __name__ == "__main__":
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annotation_path = '/scratch/bortoletto/data/tbd/reformat_annotation/'
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save_path = '/scratch/bortoletto/data/tbd/store_mind_set/'
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for clip in tqdm(os.listdir(annotation_path), desc="Processing videos", unit="item"):
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store_mind_set(clip, annotation_path, save_path) |