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import math |
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import json |
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import os |
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from numpy import * |
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def euclidean_distance(point1, point2): |
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x1, y1 = point1 |
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x2, y2 = point2 |
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distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2) |
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return distance |
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gt_json = "/mmu-ocr/weijiawu/MovieDiffusion/ShowAnything/data/VIPSeg/VIPSeg_Video_Generation_Test/test_traject" |
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prediction_json = "/mmu-ocr/weijiawu/MovieDiffusion/ShowAnything/data/VIPSeg/VIPSeg_Video_Generation_Test/Prediction_Model/trajectory_1024_CoTracker_DragAnything14frames_OriginalSize1" |
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gt_list = os.listdir(gt_json) |
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pred_list = os.listdir(prediction_json) |
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json_list = [] |
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for i in gt_list: |
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if i in pred_list: |
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json_list.append(i) |
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ED_list = [] |
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for json_one in json_list: |
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with open(os.path.join(gt_json,json_one), 'r') as json_file: |
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trajectory_gt = json.load(json_file) |
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with open(os.path.join(prediction_json,json_one), 'r') as json_file: |
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trajectory_pred = json.load(json_file) |
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for index in trajectory_gt: |
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gt_points = trajectory_gt[index] |
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pred_points = trajectory_pred[index] |
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for point1,point2 in zip(gt_points,pred_points): |
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ED = euclidean_distance(point1,point2) |
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ED_list.append(ED) |
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print("mean euclidean distance",mean(ED_list)) |
