suppres_ghost = True nms_kernel = 3 scales = configs["db"]["test_scales"] weight_exp = 8 categories = configs["db"]["categories"] print('''[demo] configs["db"]''', configs["db"]) nms_threshold = configs["db"]["nms_threshold"] max_per_image = configs["db"]["max_per_image"] nms_algorithm = { "nms": 0, "linear_soft_nms": 1, "exp_soft_nms": 2 }["exp_soft_nms"] if args.show_mask: dextr = Dextr() mean = np.array([0.40789654, 0.44719302, 0.47026115], dtype=np.float32) std = np.array([0.28863828, 0.27408164, 0.27809835], dtype=np.float32) top_bboxes = {} # print("[demo] args.demo", args.demo, "os.path.isdir(args.demo)", os.path.isdir(args.demo)) if os.path.isdir(args.demo): image_names = [] ls = os.listdir(args.demo) # print("os.listdir(args.demo)", ls) for file_name in sorted(ls): ext = file_name[file_name.rfind('.') + 1:].lower() if ext in image_ext: image_names.append(os.path.join(args.demo, file_name)) else:
center_thresh = configs["db"]["center_thresh"] suppres_ghost = True nms_kernel = 3 scales = configs["db"]["test_scales"] weight_exp = 8 categories = configs["db"]["categories"] nms_threshold = configs["db"]["nms_threshold"] max_per_image = configs["db"]["max_per_image"] nms_algorithm = { "nms": 0, "linear_soft_nms": 1, "exp_soft_nms": 2 }["exp_soft_nms"] if args.show_mask: dextr = Dextr() mean = np.array([0.40789654, 0.44719302, 0.47026115], dtype=np.float32) std = np.array([0.28863828, 0.27408164, 0.27809835], dtype=np.float32) top_bboxes = {} if os.path.isdir(args.demo): image_names = [] ls = os.listdir(args.demo) for file_name in sorted(ls): ext = file_name[file_name.rfind('.') + 1:].lower() if ext in image_ext: image_names.append(os.path.join(args.demo, file_name)) else: image_names = [args.demo]
from dextr import Dextr import pycocotools.coco as cocoapi from pycocotools.cocoeval import COCOeval from pycocotools import mask as COCOmask import numpy as np import sys import cv2 import json from progress.bar import Bar DEBUG = False ANN_PATH = '/ldap_home/zichen.liu/data/coco/annotations/instances_val2017.json' IMG_DIR = '/ldap_home/zichen.liu/data/coco/val2017/' if __name__ == '__main__': dextr = Dextr() coco = cocoapi.COCO(ANN_PATH) pred_path = sys.argv[1] out_path = pred_path[:-5] + '_segm.json' anns = json.load(open(pred_path, 'r')) results = [] score_thresh = 0.2 num_boxes = 0 for i, ann in enumerate(anns): if ann['score'] >= score_thresh: num_boxes += 1 bar = Bar('Pred + Dextr', max=num_boxes) for i, ann in enumerate(anns): if ann['score'] < score_thresh: continue