Improved preprocess script performance
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1 changed files with 18 additions and 8 deletions
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@ -23,6 +23,7 @@ import random
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import numpy as np
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import cv2
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import scipy.io as io
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import scipy.sparse as sparse
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import h5py
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CWD = os.getcwd()
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@ -75,16 +76,25 @@ def pre_dataset_sh():
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gt_data[:, 1] = gt_data[:, 1] * rate_y
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if is_portrait:
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print("Portrait img: \'{}\' -- rotating 90 deg clockwise...".format(img_path))
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img_data = cv2.rotate(img_data, cv2.ROTATE_90_CLOCKWISE)
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print("Portrait img: \'{}\' -- transposing...".format(img_path))
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img_data = cv2.transpose(img_data)
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gt_data = gt_data[:, ::-1]
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# Compute 0/1 counts from density map
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kpoint = np.zeros((img_data.shape[0], img_data.shape[1]))
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for i in range(len(gt_data)):
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if ( int(gt_data[i][1]) < img_data.shape[0]
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and int(gt_data[i][0]) < img_data.shape[1]):
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kpoint[int(gt_data[i][1]), int(gt_data[i][0])] = 1
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assert img_data.shape[:2] == (768, 1152)
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coordinates = gt_data.round().astype(int) # To integer coords
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coordinates[:, 0] = np.clip(coordinates[:, 0], a_min=0, a_max=1151)
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coordinates[:, 1] = np.clip(coordinates[:, 1], a_min=0, a_max=767)
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assert max(coordinates[:, 0]) < 1152
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assert max(coordinates[:, 1]) < 768
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sparse_mat = sparse.coo_matrix((
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np.ones(coordinates.shape[0]), # data
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(coordinates[:, 1], coordinates[:, 0]), # (i, j)
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), # N.B. all k |- ret[i[k], j[k]] = data[k]
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shape=(768, 1152),
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dtype=int,
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) # To same shape as image, so i, j flipped wrt. coordinates
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kpoint = sparse_mat.toarray()
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fname = img_path.split("/")[-1]
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root_path = img_path.split("IMG_")[0].replace("images", "images_crop")
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