why all the data in .pfm array are 1?

[husha1993]

the .pfm file is required by the following codes

responses = client.simGetImages([airsim.ImageRequest("0", airsim.ImageType.DepthPerspective, True)])
for idx, response in enumerate(responses):
filename = os.path.join(tmp_dir, str(idx) + '_' + str(response.image_type))
if response.pixels_as_float:
print("1, Type %d, size %d" % (response.image_type, len(response.image_data_float)))
airsim.write_pfm(os.path.normpath(filename + '.pfm'), airsim.get_pfm_array(response))

then read the .pfm file

airsim.read_pfm(r'C:\temp\airsim_drone\1_2.pfm')
(array([[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
...,
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]], dtype=float32), 1.0)

[husha1993]

when I set the pixels_as_float as default, then the depth data is not always '1'

responses = client.simGetImages([airsim.ImageRequest("0", airsim.ImageType.DepthPerspective)])
res= response[0]

np.frombuffer(res.image_data_uint8, np.uint8)
array([137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73,
72, 68, 82, 0, 0, 1, 0, 0, 0, 0, 144, 8, 6,
0, 0, 0, 231, 99, 181, 145, 0, 0, 3, 152, 73, 68,
65, 84, 120, 1, 237, 212, 1, 17, 0, 32, 12, 3, 49,
192, 191, 231, 193, 97, 227, 51, 7, 77, 119, 221, 243, 110,
57, 2, 4, 146, 2, 39, 153, 90, 104, 2, 4, 190, 128,
1, 240, 8, 4, 194, 2, 6, 32, 92, 190, 232, 4, 12,
128, 31, 32, 16, 22, 48, 0, 225, 242, 69, 39, 96, 0,
252, 0, 129, 176, 128, 1, 8, 151, 47, 58, 1, 3, 224,
7, 8, 132, 5, 12, 64, 184, 124, 209, 9, 24, 0, 63,
64, 32, 44, 96, 0, 194, 229, 139, 78, 192, 0, 248, 1,
2, 97, 1, 3, 16, 46, 95, 116, 2, 6, 192, 15, 16,
8, 11, 24, 128, 112, 249, 162, 19, 48, 0, 126, 128, 64,
88, 192, 0, 132, 203, 23, 157, 128, 1, 240, 3, 4, 194,
2, 6, 32, 92, 190, 232, 4, 12, 128, 31, 32, 16, 22,
48, 0, 225, 242, 69, 39, 96, 0, 252, 0, 129, 176, 128,
1, 8, 151, 47, 58, 1, 3, 224, 7, 8, 132, 5, 12,
64, 184, 124, 209, 9, 24, 0, 63, 64, 32, 44, 96, 0,
194, 229, 139, 78, 192, 0, 248, 1, 2, 97, 1, 3, 16,
46, 95, 116, 2, 6, 192, 15, 16, 8, 11, 24, 128, 112,
249, 162, 19, 48, 0, 126, 128, 64, 88, 192, 0, 132, 203,
23, 157, 128, 1, 240, 3, 4, 194, 2, 6, 32, 92, 190,
232, 4, 12, 128, 31, 32, 16, 22, 48, 0, 225, 242, 69,
39, 96, 0, 252, 0, 129, 176, 128, 1, 8, 151, 47, 58,
1, 3, 224, 7, 8, 132, 5, 12, 64, 184, 124, 209, 9,
24, 0, 63, 64, 32, 44, 96, 0, 194, 229, 139, 78, 192,
0, 248, 1, 2, 97, 1, 3, 16, 46, 95, 116, 2, 6,
192, 15, 16, 8, 11, 24, 128, 112, 249, 162, 19, 48, 0,
126, 128, 64, 88, 192, 0, 132, 203, 23, 157, 128, 1, 240,
3, 4, 194, 2, 6, 32, 92, 190, 232, 4, 12, 128, 31,
32, 16, 22, 48, 0, 225, 242, 69, 39, 96, 0, 252, 0,
129, 176, 128, 1, 8, 151, 47, 58, 1, 3, 224, 7, 8,
132, 5, 12, 64, 184, 124, 209, 9, 24, 0, 63, 64, 32,
44, 96, 0, 194, 229, 139, 78, 192, 0, 248, 1, 2, 97,
1, 3, 16, 46, 95, 116, 2, 6, 192, 15, 16, 8, 11,
24, 128, 112, 249, 162, 19, 48, 0, 126, 128, 64, 88, 192,
0, 132, 203, 23, 157, 128, 1, 240, 3, 4, 194, 2, 6,
32, 92, 190, 232, 4, 12, 128, 31, 32, 16, 22, 48, 0,
225, 242, 69, 39, 96, 0, 252, 0, 129, 176, 128, 1, 8,
151, 47, 58, 1, 3, 224, 7, 8, 132, 5, 12, 64, 184,
124, 209, 9, 24, 0, 63, 64, 32, 44, 96, 0, 194, 229,
139, 78, 192, 0, 248, 1, 2, 97, 1, 3, 16, 46, 95,
116, 2, 6, 192, 15, 16, 8, 11, 24, 128, 112, 249, 162,
19, 48, 0, 126, 128, 64, 88, 192, 0, 132, 203, 23, 157,
128, 1, 240, 3, 4, 194, 2, 6, 32, 92, 190, 232, 4,
12, 128, 31, 32, 16, 22, 48, 0, 225, 242, 69, 39, 96,
0, 252, 0, 129, 176, 128, 1, 8, 151, 47, 58, 1, 3,
224, 7, 8, 132, 5, 12, 64, 184, 124, 209, 9, 24, 0,
63, 64, 32, 44, 96, 0, 194, 229, 139, 78, 192, 0, 248,
1, 2, 97, 1, 3, 16, 46, 95, 116, 2, 6, 192, 15,
16, 8, 11, 24, 128, 112, 249, 162, 19, 48, 0, 126, 128,
64, 88, 192, 0, 132, 203, 23, 157, 128, 1, 240, 3, 4,
194, 2, 6, 32, 92, 190, 232, 4, 12, 128, 31, 32, 16,
22, 48, 0, 225, 242, 69, 39, 96, 0, 252, 0, 129, 176,
128, 1, 8, 151, 47, 58, 1, 3, 224, 7, 8, 132, 5,
12, 64, 184, 124, 209, 9, 24, 0, 63, 64, 32, 44, 96,
0, 194, 229, 139, 78, 192, 0, 248, 1, 2, 97, 1, 3,
16, 46, 95, 116, 2, 6, 192, 15, 16, 8, 11, 24, 128,
112, 249, 162, 19, 48, 0, 126, 128, 64, 88, 192, 0, 132,
203, 23, 157, 128, 1, 240, 3, 4, 194, 2, 6, 32, 92,
190, 232, 4, 12, 128, 31, 32, 16, 22, 48, 0, 225, 242,
69, 39, 96, 0, 252, 0, 129, 176, 128, 1, 8, 151, 47,
58, 1, 3, 224, 7, 8, 132, 5, 12, 64, 184, 124, 209,
9, 24, 0, 63, 64, 32, 44, 96, 0, 194, 229, 139, 78,
192, 0, 248, 1, 2, 97, 1, 3, 16, 46, 95, 116, 2,
6, 192, 15, 16, 8, 11, 24, 128, 112, 249, 162, 19, 48,
0, 126, 128, 64, 88, 192, 0, 132, 203, 23, 157, 128, 1,
240, 3, 4, 194, 2, 6, 32, 92, 190, 232, 4, 12, 128,
31, 32, 16, 22, 48, 0, 225, 242, 69, 39, 96, 0, 252,
0, 129, 176, 128, 1, 8, 151, 47, 58, 1, 3, 224, 7,
8, 132, 5, 12, 64, 184, 124, 209, 9, 24, 0, 63, 64,
32, 44, 96, 0, 194, 229, 139, 78, 192, 0, 248, 1, 2,
97, 1, 3, 16, 46, 95, 116, 2, 6, 192, 15, 16, 8,
11, 24, 128, 112, 249, 162, 19, 184, 95, 26, 5, 28, 126,
113, 254, 208, 0, 0, 0, 0, 73, 69, 78, 68, 174, 66,
96, 130], dtype=uint8)

maybe it relates to the decoding of buffer data as floats in simGetImages()

[husha1993]

np.frombuffer(response.image_data_uint8, np.uint8).shape
(977,)

it should be 144*256