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import os |
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import re |
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import cv2 |
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import numpy as np |
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from ..utils.common import Notify |
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def read_list(list_path): |
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"""Read list.""" |
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if list_path is None or not os.path.exists(list_path): |
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print(Notify.FAIL, "Not exist", list_path, Notify.ENDC) |
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exit(-1) |
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content = open(list_path).read().splitlines() |
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return content |
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def load_pfm(pfm_path): |
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with open(pfm_path, "rb") as fin: |
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color = None |
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width = None |
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height = None |
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scale = None |
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data_type = None |
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header = str(fin.readline().decode("UTF-8")).rstrip() |
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if header == "PF": |
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color = True |
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elif header == "Pf": |
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color = False |
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else: |
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raise Exception("Not a PFM file.") |
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dim_match = re.match(r"^(\d+)\s(\d+)\s$", fin.readline().decode("UTF-8")) |
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if dim_match: |
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width, height = map(int, dim_match.groups()) |
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else: |
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raise Exception("Malformed PFM header.") |
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scale = float((fin.readline().decode("UTF-8")).rstrip()) |
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if scale < 0: |
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data_type = "<f" |
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else: |
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data_type = ">f" |
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data_string = fin.read() |
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data = np.fromstring(data_string, data_type) |
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shape = (height, width, 3) if color else (height, width) |
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data = np.reshape(data, shape) |
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data = np.flip(data, 0) |
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return data |
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def _parse_img(img_paths, idx, config): |
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img_path = img_paths[idx] |
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img = cv2.imread(img_path)[:, :, ::-1] |
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if config["resize"] > 0: |
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img = cv2.resize(img, (config["resize"], config["resize"])) |
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return img |
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def _parse_depth(depth_paths, idx, config): |
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depth = load_pfm(depth_paths[idx]) |
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if config["resize"] > 0: |
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target_size = config["resize"] |
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if config["input_type"] == "raw": |
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depth = cv2.resize(depth, (int(target_size / 2), int(target_size / 2))) |
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else: |
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depth = cv2.resize(depth, (target_size, target_size)) |
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return depth |
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def _parse_kpts(kpts_paths, idx, config): |
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kpts = np.load(kpts_paths[idx])["pts"] |
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return kpts |
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