質問編集履歴

全体のソースコードを追加しました。

2021/10/14 06:32

投稿

Kota1

スコア15

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@@ -2,7 +2,9 @@
-画像を入力としていて、「画像の名前.txt」というファイルに実行結果を保存したいです。
+画像を入力としていて、「画像の名前.txt」というファイルに実行結果を保存したいです。保存先はカレントディレクトリです。
+複数の画像を入力としたいため、.txtファイルの区別をしたいです。
@@ -14,6 +16,434 @@
+####全体のソースコード
+```Python
+"""Utils for monoDepth.
+"""
+import sys
+import re
+import numpy as np
+import cv2
+import torch
+def read_pfm(path):
+    """Read pfm file.
+    Args:
+        path (str): path to file
+    Returns:
+        tuple: (data, scale)
+    """
+    with open(path, "rb") as file:
+        color = None
+        width = None
+        height = None
+        scale = None
+        endian = None
+        header = file.readline().rstrip()
+        if header.decode("ascii") == "PF":
+            color = True
+        elif header.decode("ascii") == "Pf":
+            color = False
+        else:
+            raise Exception("Not a PFM file: " + path)
+        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
+        if dim_match:
+            width, height = list(map(int, dim_match.groups()))
+        else:
+            raise Exception("Malformed PFM header.")
+        scale = float(file.readline().decode("ascii").rstrip())
+        if scale < 0:
+            # little-endian
+            endian = "<"
+            scale = -scale
+        else:
+            # big-endian
+            endian = ">"
+        data = np.fromfile(file, endian + "f")
+        shape = (height, width, 3) if color else (height, width)
+        data = np.reshape(data, shape)
+        data = np.flipud(data)
+        return data, scale
+def write_pfm(path, image, scale=1):
+    """Write pfm file.
+    Args:
+        path (str): pathto file
+        image (array): data
+        scale (int, optional): Scale. Defaults to 1.
+    """
+    with open(path, "wb") as file:
+        color = None
+        if image.dtype.name != "float32":
+            raise Exception("Image dtype must be float32.")
+        image = np.flipud(image)
+        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
+            color = True
+        elif (
+            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
+        ):  # greyscale
+            color = False
+        else:
+            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+        file.write("PF\n" if color else "Pf\n".encode())
+        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+        endian = image.dtype.byteorder
+        if endian == "<" or endian == "=" and sys.byteorder == "little":
+            scale = -scale
+        file.write("%f\n".encode() % scale)
+        image.tofile(file)
+def read_image(path):
+    """Read image and output RGB image (0-1).
+    Args:
+        path (str): path to file
+    Returns:
+        array: RGB image (0-1)
+    """
+    img = cv2.imread(path)
+    if img.ndim == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
+    return img
+def resize_image(img):
+    """Resize image and make it fit for network.
+    Args:
+        img (array): image
+    Returns:
+        tensor: data ready for network
+    """
+    height_orig = img.shape[0]
+    width_orig = img.shape[1]
+    if width_orig > height_orig:
+        scale = width_orig / 384
+    else:
+        scale = height_orig / 384
+    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
+    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
+    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
+    img_resized = (
+        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
+    )
+    img_resized = img_resized.unsqueeze(0)
+    return img_resized
+def resize_depth(depth, width, height):
+    """Resize depth map and bring to CPU (numpy).
+    Args:
+        depth (tensor): depth
+        width (int): image width
+        height (int): image height
+    Returns:
+        array: processed depth
+    """
+    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
+    depth_resized = cv2.resize(
+        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
+    )
+    return depth_resized
+def write_depth(path, depth, bits=1 , colored=False):
+    """Write depth map to pfm and png file.
+    Args:
+        path (str): filepath without extension
+        depth (array): depth
+    """
+    # write_pfm(path + ".pfm", depth.astype(np.float32))
+    if colored == True:
+        bits = 1
+    depth_min = depth.min()
+    depth_max = depth.max()
+    max_val = (2**(8*bits))-1 #8bit演算で計算するときの最大値
+    # if depth_max>max_val:
+    #     print('Warning: Depth being clipped')
+    #
+    # if depth_max - depth_min > np.finfo("float").eps:
+    #     out = depth
+    #     out [depth > max_val] = max_val
+    # else:
+    #     out = 0
+       #最大値最小値
+    print(str(depth_min)+","+str(depth_max))
+    with open('min,max.txt', 'w') as f:
+        print(str(depth_min)+","+str(depth_max), file=f)
+    cv2.imwrite(path+'.txt')
+    if depth_max - depth_min > np.finfo("float").eps:
+        out = max_val * (depth - depth_min) / (depth_max - depth_min)
+    else:
+        out = 0
+    if bits == 1 or colored:
+        out = out.astype("uint8")
+        if colored:
+            out = cv2.applyColorMap(out,cv2.COLORMAP_INFERNO)
+        cv2.imwrite(path+'.png', out)
+    elif bits == 2:
+        cv2.imwrite(path+'.png', out.astype("uint16"))
+    return
+```
 ### 該当のソースコード