113 lines
4.4 KiB
Python
113 lines
4.4 KiB
Python
import os,sys
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import torch
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import numpy as np
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sys.path.extend(['../AIlib2/obbUtils'])
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#import datasets.DOTA_devkit.ResultMerge_multi_process
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#from datasets.DOTA_devkit.ResultMerge_multi_process import py_cpu_nms_poly_fast, py_cpu_nms_poly
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from dotadevkit.ops.ResultMerge import py_cpu_nms_poly_fast, py_cpu_nms_poly
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import time
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# def decode_prediction(predictions, dsets, args, img_id, down_ratio):
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def decode_prediction(predictions, category, model_size, down_ratio,ori_image):
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t1=time.time()
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predictions = predictions[0, :, :]
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# ttt1=time.time()
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# # ori_image = dsets.load_image(dsets.img_ids.index(img_id)) #加载了原图第2次？？？？这里耗时 改1
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# ttt2 = time.time()
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# print(f'jiazaitupian. ({(1E3 * (ttt2 - ttt1)):.1f}ms) ')
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h, w, c = ori_image.shape
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pts0 = {cat: [] for cat in category}
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scores0 = {cat: [] for cat in category}
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for pred in predictions:
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cen_pt = np.asarray([pred[0], pred[1]], np.float32)
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tt = np.asarray([pred[2], pred[3]], np.float32)
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rr = np.asarray([pred[4], pred[5]], np.float32)
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bb = np.asarray([pred[6], pred[7]], np.float32)
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ll = np.asarray([pred[8], pred[9]], np.float32)
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tl = tt + ll - cen_pt
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bl = bb + ll - cen_pt
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tr = tt + rr - cen_pt
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br = bb + rr - cen_pt
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score = pred[10]
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clse = pred[11]
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pts = np.asarray([tr, br, bl, tl], np.float32)
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pts[:, 0] = pts[:, 0] * down_ratio / model_size[0] * w
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pts[:, 1] = pts[:, 1] * down_ratio / model_size[1] * h
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pts0[category[int(clse)]].append(pts)
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scores0[category[int(clse)]].append(score)
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t2=time.time()
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#print('###line40:decode_prediction time: %.1f ',(t2-t1)*1000.0)
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return pts0, scores0
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def non_maximum_suppression(pts, scores):
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nms_item = np.concatenate([pts[:, 0:1, 0],
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pts[:, 0:1, 1],
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pts[:, 1:2, 0],
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pts[:, 1:2, 1],
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pts[:, 2:3, 0],
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pts[:, 2:3, 1],
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pts[:, 3:4, 0],
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pts[:, 3:4, 1],
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scores[:, np.newaxis]], axis=1)
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nms_item = np.asarray(nms_item, np.float64)
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keep_index = py_cpu_nms_poly_fast(dets=nms_item, thresh=0.1)
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return nms_item[keep_index]
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def write_results(args,
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model,
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dsets,
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down_ratio,
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device,
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decoder,
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result_path,
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print_ps=False):
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results = {cat: {img_id: [] for img_id in dsets.img_ids} for cat in dsets.category}
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for index in range(len(dsets)):
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data_dict = dsets.__getitem__(index)
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image = data_dict['image'].to(device)
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img_id = data_dict['img_id']
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image_w = data_dict['image_w']
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image_h = data_dict['image_h']
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with torch.no_grad():
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pr_decs = model(image)
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decoded_pts = []
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decoded_scores = []
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torch.cuda.synchronize(device)
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predictions = decoder.ctdet_decode(pr_decs)
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pts0, scores0 = decode_prediction(predictions, dsets, args, img_id, down_ratio)
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decoded_pts.append(pts0)
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decoded_scores.append(scores0)
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# nms
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for cat in dsets.category:
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if cat == 'background':
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continue
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pts_cat = []
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scores_cat = []
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for pts0, scores0 in zip(decoded_pts, decoded_scores):
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pts_cat.extend(pts0[cat])
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scores_cat.extend(scores0[cat])
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pts_cat = np.asarray(pts_cat, np.float32)
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scores_cat = np.asarray(scores_cat, np.float32)
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if pts_cat.shape[0]:
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nms_results = non_maximum_suppression(pts_cat, scores_cat)
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results[cat][img_id].extend(nms_results)
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if print_ps:
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print('testing {}/{} data {}'.format(index+1, len(dsets), img_id))
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for cat in dsets.category:
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if cat == 'background':
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continue
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with open(os.path.join(result_path, 'Task1_{}.txt'.format(cat)), 'w') as f:
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for img_id in results[cat]:
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for pt in results[cat][img_id]:
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f.write('{} {:.12f} {:.1f} {:.1f} {:.1f} {:.1f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(
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img_id, pt[8], pt[0], pt[1], pt[2], pt[3], pt[4], pt[5], pt[6], pt[7]))
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