kopia lustrzana https://github.com/OpenDroneMap/ODM
Added support of radiometric calibration for DJI Mavic 2 Enterprize Advanced. Added methods for adding FLIR sensor into reconstruction
markFieldman
rodzic
3957278c2e
commit
27e6116977
|
|
@ -617,6 +617,9 @@ class ODM_Photo:
|
|||
self.gps_xy_stddev = self.gps_z_stddev = dop
|
||||
|
||||
def is_thermal(self):
|
||||
#Added for support M2EA camera sensor
|
||||
if(self.camera_make == "DJI"):
|
||||
return self.camera_model == "MAVIC2-ENTERPRISE-ADVANCED" and self.width == 640 and self.height == 512
|
||||
return self.band_name.upper() in ["LWIR"] # TODO: more?
|
||||
|
||||
def camera_id(self):
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
from opendm import log
|
||||
from opendm.thermal_tools import dji_unpack
|
||||
import cv2
|
||||
|
||||
def resize_to_match(image, match_photo = None):
|
||||
|
|
@ -17,16 +18,16 @@ def resize_to_match(image, match_photo = None):
|
|||
interpolation=cv2.INTER_LANCZOS4)
|
||||
return image
|
||||
|
||||
def dn_to_temperature(photo, image):
|
||||
def dn_to_temperature(photo, image, dataset_tree):
|
||||
"""
|
||||
Convert Digital Number values to temperature (C) values
|
||||
:param photo ODM_Photo
|
||||
:param image numpy array containing image data
|
||||
:param resize_to_photo ODM_Photo that photo should be resized to (to match its dimensions)
|
||||
:param dataset_tree path to original source image to read data using PIL for DJI thermal photos
|
||||
:return numpy array with temperature (C) image values
|
||||
"""
|
||||
|
||||
image = image.astype("float32")
|
||||
|
||||
|
||||
# Handle thermal bands
|
||||
if photo.is_thermal():
|
||||
|
|
@ -34,12 +35,18 @@ def dn_to_temperature(photo, image):
|
|||
# The following will work for MicaSense Altum cameras
|
||||
# but not necessarily for others
|
||||
if photo.camera_make == "MicaSense" and photo.camera_model == "Altum":
|
||||
image = image.astype("float32")
|
||||
image -= (273.15 * 100.0) # Convert Kelvin to Celsius
|
||||
image *= 0.01
|
||||
return image
|
||||
elif photo.camera_make == "DJI" and photo.camera_model == "MAVIC2-ENTERPRISE-ADVANCED":
|
||||
image = dji_unpack.extract_temperatures_dji(photo, image, dataset_tree)
|
||||
image = image.astype("float32")
|
||||
return image
|
||||
else:
|
||||
log.ODM_WARNING("Unsupported camera [%s %s], thermal band will have digital numbers." % (photo.camera_make, photo.camera_model))
|
||||
else:
|
||||
image = image.astype("float32")
|
||||
log.ODM_WARNING("Tried to radiometrically calibrate a non-thermal image with temperature values (%s)" % photo.filename)
|
||||
return image
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,51 @@
|
|||
from PIL import Image
|
||||
import numpy as np
|
||||
from opendm import system
|
||||
from opendm import log
|
||||
|
||||
from opendm.thermal_tools.thermal_utils import sensor_vals_to_temp
|
||||
|
||||
def extract_temperatures_dji(photo, image, dataset_tree):
|
||||
"""Extracts the DJI-encoded thermal image as 2D floating-point numpy array with temperatures in degC.
|
||||
The raw sensor values are obtained using the sample binaries provided in the official Thermal SDK by DJI.
|
||||
The executable file is run and generates a 16 bit unsigned RAW image with Little Endian byte order.
|
||||
Link to DJI Forum post: https://forum.dji.com/forum.php?mod=redirect&goto=findpost&ptid=230321&pid=2389016
|
||||
"""
|
||||
# Harcoded metadata for mean of values
|
||||
# This is added to support the possibility of extracting RJPEG from DJI M2EA
|
||||
meta = {
|
||||
"Emissivity": 0.95,
|
||||
"ObjectDistance": 50, #This is mean value of flights for better results. Need to be changed later, or improved by bypassing options from task broker
|
||||
"AtmosphericTemperature": 20,
|
||||
"ReflectedApparentTemperature": 30,
|
||||
"IRWindowTemperature": 20,
|
||||
"IRWindowTransmission": 1,
|
||||
"RelativeHumidity": 40,
|
||||
"PlanckR1": 21106.77,
|
||||
"PlanckB": 1501,
|
||||
"PlanckF": 1,
|
||||
"PlanckO": -7340,
|
||||
"PlanckR2": 0.012545258,
|
||||
}
|
||||
|
||||
if photo.camera_model == "MAVIC2-ENTERPRISE-ADVANCED":
|
||||
# Adding support for MAVIC2-ENTERPRISE-ADVANCED Camera images
|
||||
im = Image.open(f"{dataset_tree}/{photo.filename}")
|
||||
# concatenate APP3 chunks of data
|
||||
a = im.applist[3][1]
|
||||
for i in range(4, 14):
|
||||
a += im.applist[i][1]
|
||||
# create image from bytes
|
||||
try:
|
||||
img = Image.frombytes("I;16L", (640, 512), a)
|
||||
except ValueError as e:
|
||||
log.ODM_ERROR(e)
|
||||
log.ODM_ERROR(photo.filename)
|
||||
else:
|
||||
log.ODM_DEBUG("Only DJI M2EA currently supported, please wait for new updates")
|
||||
return image
|
||||
# Extract raw sensor values from generated image into numpy array
|
||||
raw_sensor_np = np.array(img)
|
||||
## extracting the temperatures from thermal images
|
||||
thermal_np = sensor_vals_to_temp(raw_sensor_np, **meta)
|
||||
return thermal_np
|
||||
|
|
@ -0,0 +1,271 @@
|
|||
"""
|
||||
THIS IS WIP, DON'T USE THIS FILE, IT IS HERE FOR FURTHER IMPROVEMENT
|
||||
Tools for extracting thermal data from FLIR images.
|
||||
Derived from https://bitbucket.org/nimmerwoner/flyr/src/master/
|
||||
"""
|
||||
|
||||
import os
|
||||
from io import BufferedIOBase, BytesIO
|
||||
from typing import BinaryIO, Dict, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
# Constants
|
||||
SEGMENT_SEP = b"\xff"
|
||||
APP1_MARKER = b"\xe1"
|
||||
MAGIC_FLIR_DEF = b"FLIR\x00"
|
||||
|
||||
CHUNK_APP1_BYTES_COUNT = len(APP1_MARKER)
|
||||
CHUNK_LENGTH_BYTES_COUNT = 2
|
||||
CHUNK_MAGIC_BYTES_COUNT = len(MAGIC_FLIR_DEF)
|
||||
CHUNK_SKIP_BYTES_COUNT = 1
|
||||
CHUNK_NUM_BYTES_COUNT = 1
|
||||
CHUNK_TOT_BYTES_COUNT = 1
|
||||
CHUNK_PARTIAL_METADATA_LENGTH = CHUNK_APP1_BYTES_COUNT + CHUNK_LENGTH_BYTES_COUNT + CHUNK_MAGIC_BYTES_COUNT
|
||||
CHUNK_METADATA_LENGTH = (
|
||||
CHUNK_PARTIAL_METADATA_LENGTH + CHUNK_SKIP_BYTES_COUNT + CHUNK_NUM_BYTES_COUNT + CHUNK_TOT_BYTES_COUNT
|
||||
)
|
||||
|
||||
|
||||
def unpack(path_or_stream: Union[str, BinaryIO]) -> np.ndarray:
|
||||
"""Unpacks the FLIR image, meaning that it will return the thermal data embedded in the image.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path_or_stream : Union[str, BinaryIO]
|
||||
Either a path (string) to a FLIR file, or a byte stream such as
|
||||
BytesIO or file opened as `open(file_path, "rb")`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
FlyrThermogram
|
||||
When successful, a FlyrThermogram object containing thermogram data.
|
||||
"""
|
||||
if isinstance(path_or_stream, str) and os.path.isfile(path_or_stream):
|
||||
with open(path_or_stream, "rb") as flirh:
|
||||
return unpack(flirh)
|
||||
elif isinstance(path_or_stream, BufferedIOBase):
|
||||
stream = path_or_stream
|
||||
flir_app1_stream = extract_flir_app1(stream)
|
||||
flir_records = parse_flir_app1(flir_app1_stream)
|
||||
raw_np = parse_thermal(flir_app1_stream, flir_records)
|
||||
|
||||
return raw_np
|
||||
else:
|
||||
raise ValueError("Incorrect input")
|
||||
|
||||
|
||||
def extract_flir_app1(stream: BinaryIO) -> BinaryIO:
|
||||
"""Extracts the FLIR APP1 bytes.
|
||||
|
||||
Parameters
|
||||
---------
|
||||
stream : BinaryIO
|
||||
A full bytes stream of a JPEG file, expected to be a FLIR file.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
When the file is invalid in one the next ways, a
|
||||
ValueError is thrown.
|
||||
|
||||
* File is not a JPEG
|
||||
* A FLIR chunk number occurs more than once
|
||||
* The total chunks count is inconsistent over multiple chunks
|
||||
* No APP1 segments are successfully parsed
|
||||
|
||||
Returns
|
||||
-------
|
||||
BinaryIO
|
||||
A bytes stream of the APP1 FLIR segments
|
||||
"""
|
||||
# Check JPEG-ness
|
||||
_ = stream.read(2)
|
||||
|
||||
chunks_count: Optional[int] = None
|
||||
chunks: Dict[int, bytes] = {}
|
||||
while True:
|
||||
b = stream.read(1)
|
||||
if b == b"":
|
||||
break
|
||||
|
||||
if b != SEGMENT_SEP:
|
||||
continue
|
||||
|
||||
parsed_chunk = parse_flir_chunk(stream, chunks_count)
|
||||
if not parsed_chunk:
|
||||
continue
|
||||
|
||||
chunks_count, chunk_num, chunk = parsed_chunk
|
||||
chunk_exists = chunks.get(chunk_num, None) is not None
|
||||
if chunk_exists:
|
||||
raise ValueError("Invalid FLIR: duplicate chunk number")
|
||||
chunks[chunk_num] = chunk
|
||||
|
||||
# Encountered all chunks, break out of loop to process found metadata
|
||||
if chunk_num == chunks_count:
|
||||
break
|
||||
|
||||
if chunks_count is None:
|
||||
raise ValueError("Invalid FLIR: no metadata encountered")
|
||||
|
||||
flir_app1_bytes = b""
|
||||
for chunk_num in range(chunks_count + 1):
|
||||
flir_app1_bytes += chunks[chunk_num]
|
||||
|
||||
flir_app1_stream = BytesIO(flir_app1_bytes)
|
||||
flir_app1_stream.seek(0)
|
||||
return flir_app1_stream
|
||||
|
||||
|
||||
def parse_flir_chunk(stream: BinaryIO, chunks_count: Optional[int]) -> Optional[Tuple[int, int, bytes]]:
|
||||
"""Parse flir chunk."""
|
||||
# Parse the chunk header. Headers are as follows (definition with example):
|
||||
#
|
||||
# \xff\xe1<length: 2 bytes>FLIR\x00\x01<chunk nr: 1 byte><chunk count: 1 byte>
|
||||
# \xff\xe1\xff\xfeFLIR\x00\x01\x01\x0b
|
||||
#
|
||||
# Meaning: Exif APP1, 65534 long, FLIR chunk 1 out of 12
|
||||
marker = stream.read(CHUNK_APP1_BYTES_COUNT)
|
||||
|
||||
length_bytes = stream.read(CHUNK_LENGTH_BYTES_COUNT)
|
||||
length = int.from_bytes(length_bytes, "big")
|
||||
length -= CHUNK_METADATA_LENGTH
|
||||
magic_flir = stream.read(CHUNK_MAGIC_BYTES_COUNT)
|
||||
|
||||
if not (marker == APP1_MARKER and magic_flir == MAGIC_FLIR_DEF):
|
||||
# Seek back to just after byte b and continue searching for chunks
|
||||
stream.seek(-len(marker) - len(length_bytes) - len(magic_flir), 1)
|
||||
return None
|
||||
|
||||
stream.seek(1, 1) # skip 1 byte, unsure what it is for
|
||||
|
||||
chunk_num = int.from_bytes(stream.read(CHUNK_NUM_BYTES_COUNT), "big")
|
||||
chunks_tot = int.from_bytes(stream.read(CHUNK_TOT_BYTES_COUNT), "big")
|
||||
|
||||
# Remember total chunks to verify metadata consistency
|
||||
if chunks_count is None:
|
||||
chunks_count = chunks_tot
|
||||
|
||||
if ( # Check whether chunk metadata is consistent
|
||||
chunks_tot is None or chunk_num < 0 or chunk_num > chunks_tot or chunks_tot != chunks_count
|
||||
):
|
||||
raise ValueError(f"Invalid FLIR: inconsistent total chunks, should be 0 or greater, but is {chunks_tot}")
|
||||
|
||||
return chunks_tot, chunk_num, stream.read(length + 1)
|
||||
|
||||
|
||||
def parse_thermal(stream: BinaryIO, records: Dict[int, Tuple[int, int, int, int]]) -> np.ndarray:
|
||||
"""Parse thermal."""
|
||||
RECORD_IDX_RAW_DATA = 1
|
||||
raw_data_md = records[RECORD_IDX_RAW_DATA]
|
||||
_, _, raw_data = parse_raw_data(stream, raw_data_md)
|
||||
return raw_data
|
||||
|
||||
|
||||
def parse_flir_app1(stream: BinaryIO) -> Dict[int, Tuple[int, int, int, int]]:
|
||||
"""Parse flir app1."""
|
||||
# 0x00 - string[4] file format ID = "FFF\0"
|
||||
# 0x04 - string[16] file creator: seen "\0","MTX IR\0","CAMCTRL\0"
|
||||
# 0x14 - int32u file format version = 100
|
||||
# 0x18 - int32u offset to record directory
|
||||
# 0x1c - int32u number of entries in record directory
|
||||
# 0x20 - int32u next free index ID = 2
|
||||
# 0x24 - int16u swap pattern = 0 (?)
|
||||
# 0x28 - int16u[7] spares
|
||||
# 0x34 - int32u[2] reserved
|
||||
# 0x3c - int32u checksum
|
||||
|
||||
# 1. Read 0x40 bytes and verify that its contents equals AFF\0 or FFF\0
|
||||
_ = stream.read(4)
|
||||
|
||||
# 2. Read FLIR record directory metadata (ref 3)
|
||||
stream.seek(16, 1)
|
||||
_ = int.from_bytes(stream.read(4), "big")
|
||||
record_dir_offset = int.from_bytes(stream.read(4), "big")
|
||||
record_dir_entries_count = int.from_bytes(stream.read(4), "big")
|
||||
stream.seek(28, 1)
|
||||
_ = int.from_bytes(stream.read(4), "big")
|
||||
|
||||
# 3. Read record directory (which is a FLIR record entry repeated
|
||||
# `record_dir_entries_count` times)
|
||||
stream.seek(record_dir_offset)
|
||||
record_dir_stream = BytesIO(stream.read(32 * record_dir_entries_count))
|
||||
|
||||
# First parse the record metadata
|
||||
record_details: Dict[int, Tuple[int, int, int, int]] = {}
|
||||
for record_nr in range(record_dir_entries_count):
|
||||
record_dir_stream.seek(0)
|
||||
details = parse_flir_record_metadata(stream, record_nr)
|
||||
if details:
|
||||
record_details[details[1]] = details
|
||||
|
||||
# Then parse the actual records
|
||||
# for (entry_idx, type, offset, length) in record_details:
|
||||
# parse_record = record_parsers[type]
|
||||
# stream.seek(offset)
|
||||
# record = BytesIO(stream.read(length + 36)) # + 36 needed to find end
|
||||
# parse_record(record, offset, length)
|
||||
|
||||
return record_details
|
||||
|
||||
|
||||
def parse_flir_record_metadata(stream: BinaryIO, record_nr: int) -> Optional[Tuple[int, int, int, int]]:
|
||||
"""Parse flir record metadata."""
|
||||
# FLIR record entry (ref 3):
|
||||
# 0x00 - int16u record type
|
||||
# 0x02 - int16u record subtype: RawData 1=BE, 2=LE, 3=PNG; 1 for other record types
|
||||
# 0x04 - int32u record version: seen 0x64,0x66,0x67,0x68,0x6f,0x104
|
||||
# 0x08 - int32u index id = 1
|
||||
# 0x0c - int32u record offset from start of FLIR data
|
||||
# 0x10 - int32u record length
|
||||
# 0x14 - int32u parent = 0 (?)
|
||||
# 0x18 - int32u object number = 0 (?)
|
||||
# 0x1c - int32u checksum: 0 for no checksum
|
||||
entry = 32 * record_nr
|
||||
stream.seek(entry)
|
||||
record_type = int.from_bytes(stream.read(2), "big")
|
||||
if record_type < 1:
|
||||
return None
|
||||
|
||||
_ = int.from_bytes(stream.read(2), "big")
|
||||
_ = int.from_bytes(stream.read(4), "big")
|
||||
_ = int.from_bytes(stream.read(4), "big")
|
||||
record_offset = int.from_bytes(stream.read(4), "big")
|
||||
record_length = int.from_bytes(stream.read(4), "big")
|
||||
_ = int.from_bytes(stream.read(4), "big")
|
||||
_ = int.from_bytes(stream.read(4), "big")
|
||||
_ = int.from_bytes(stream.read(4), "big")
|
||||
return (entry, record_type, record_offset, record_length)
|
||||
|
||||
|
||||
def parse_raw_data(stream: BinaryIO, metadata: Tuple[int, int, int, int]):
|
||||
"""Parse raw data."""
|
||||
(_, _, offset, length) = metadata
|
||||
stream.seek(offset)
|
||||
|
||||
stream.seek(2, 1)
|
||||
width = int.from_bytes(stream.read(2), "little")
|
||||
height = int.from_bytes(stream.read(2), "little")
|
||||
|
||||
stream.seek(offset + 32)
|
||||
|
||||
# Read the bytes with the raw thermal data and decode using PIL
|
||||
thermal_bytes = stream.read(length)
|
||||
thermal_stream = BytesIO(thermal_bytes)
|
||||
thermal_img = Image.open(thermal_stream)
|
||||
thermal_np = np.array(thermal_img)
|
||||
|
||||
# Check shape
|
||||
if thermal_np.shape != (height, width):
|
||||
msg = "Invalid FLIR: metadata's width and height don't match thermal data's actual width\
|
||||
and height ({} vs ({}, {})"
|
||||
msg = msg.format(thermal_np.shape, height, width)
|
||||
raise ValueError(msg)
|
||||
|
||||
# FLIR PNG data is in the wrong byte order, fix that
|
||||
fix_byte_order = np.vectorize(lambda x: (x >> 8) + ((x & 0x00FF) << 8))
|
||||
thermal_np = fix_byte_order(thermal_np)
|
||||
|
||||
return width, height, thermal_np
|
||||
|
|
@ -0,0 +1,139 @@
|
|||
"""Thermal Image manipulation utilities."""
|
||||
"""Based on https://github.com/detecttechnologies/thermal_base"""
|
||||
import numpy as np
|
||||
|
||||
def sensor_vals_to_temp(
|
||||
raw,
|
||||
Emissivity=1.0,
|
||||
ObjectDistance=1,
|
||||
AtmosphericTemperature=20,
|
||||
ReflectedApparentTemperature=20,
|
||||
IRWindowTemperature=20,
|
||||
IRWindowTransmission=1,
|
||||
RelativeHumidity=50,
|
||||
PlanckR1=21106.77,
|
||||
PlanckB=1501,
|
||||
PlanckF=1,
|
||||
PlanckO=-7340,
|
||||
PlanckR2=0.012545258,
|
||||
**kwargs,):
|
||||
"""Convert raw values from the thermographic sensor sensor to temperatures in °C. Tested for Flir and DJI cams."""
|
||||
# this calculation has been ported to python from https://github.com/gtatters/Thermimage/blob/master/R/raw2temp.R
|
||||
# a detailed explanation of what is going on here can be found there
|
||||
|
||||
# constants
|
||||
ATA1 = 0.006569
|
||||
ATA2 = 0.01262
|
||||
ATB1 = -0.002276
|
||||
ATB2 = -0.00667
|
||||
ATX = 1.9
|
||||
|
||||
# transmission through window (calibrated)
|
||||
emiss_wind = 1 - IRWindowTransmission
|
||||
refl_wind = 0
|
||||
|
||||
# transmission through the air
|
||||
h2o = (RelativeHumidity / 100) * np.exp(
|
||||
1.5587
|
||||
+ 0.06939 * (AtmosphericTemperature)
|
||||
- 0.00027816 * (AtmosphericTemperature) ** 2
|
||||
+ 0.00000068455 * (AtmosphericTemperature) ** 3
|
||||
)
|
||||
tau1 = ATX * np.exp(-np.sqrt(ObjectDistance / 2) * (ATA1 + ATB1 * np.sqrt(h2o))) + (1 - ATX) * np.exp(
|
||||
-np.sqrt(ObjectDistance / 2) * (ATA2 + ATB2 * np.sqrt(h2o))
|
||||
)
|
||||
tau2 = ATX * np.exp(-np.sqrt(ObjectDistance / 2) * (ATA1 + ATB1 * np.sqrt(h2o))) + (1 - ATX) * np.exp(
|
||||
-np.sqrt(ObjectDistance / 2) * (ATA2 + ATB2 * np.sqrt(h2o))
|
||||
)
|
||||
# radiance from the environment
|
||||
raw_refl1 = PlanckR1 / (PlanckR2 * (np.exp(PlanckB / (ReflectedApparentTemperature + 273.15)) - PlanckF)) - PlanckO
|
||||
|
||||
# Reflected component
|
||||
raw_refl1_attn = (1 - Emissivity) / Emissivity * raw_refl1
|
||||
|
||||
# Emission from atmosphere 1
|
||||
raw_atm1 = (
|
||||
PlanckR1 / (PlanckR2 * (np.exp(PlanckB / (AtmosphericTemperature + 273.15)) - PlanckF)) - PlanckO
|
||||
)
|
||||
|
||||
# attenuation for atmospheric 1 emission
|
||||
raw_atm1_attn = (1 - tau1) / Emissivity / tau1 * raw_atm1
|
||||
|
||||
# Emission from window due to its own temp
|
||||
raw_wind = (
|
||||
PlanckR1 / (PlanckR2 * (np.exp(PlanckB / (IRWindowTemperature + 273.15)) - PlanckF)) - PlanckO
|
||||
)
|
||||
# Componen due to window emissivity
|
||||
raw_wind_attn = (
|
||||
emiss_wind / Emissivity / tau1 / IRWindowTransmission * raw_wind
|
||||
)
|
||||
# Reflection from window due to external objects
|
||||
raw_refl2 = (
|
||||
PlanckR1 / (PlanckR2 * (np.exp(PlanckB / (ReflectedApparentTemperature + 273.15)) - PlanckF)) - PlanckO
|
||||
)
|
||||
# component due to window reflectivity
|
||||
raw_refl2_attn = (
|
||||
refl_wind / Emissivity / tau1 / IRWindowTransmission * raw_refl2
|
||||
)
|
||||
# Emission from atmosphere 2
|
||||
raw_atm2 = (
|
||||
PlanckR1 / (PlanckR2 * (np.exp(PlanckB / (AtmosphericTemperature + 273.15)) - PlanckF)) - PlanckO
|
||||
)
|
||||
# attenuation for atmospheric 2 emission
|
||||
raw_atm2_attn = (
|
||||
(1 - tau2) / Emissivity / tau1 / IRWindowTransmission / tau2 * raw_atm2
|
||||
)
|
||||
|
||||
raw_obj = (
|
||||
raw / Emissivity / tau1 / IRWindowTransmission / tau2
|
||||
- raw_atm1_attn
|
||||
- raw_atm2_attn
|
||||
- raw_wind_attn
|
||||
- raw_refl1_attn
|
||||
- raw_refl2_attn
|
||||
)
|
||||
val_to_log = PlanckR1 / (PlanckR2 * (raw_obj + PlanckO)) + PlanckF
|
||||
if any(val_to_log.ravel() < 0):
|
||||
raise Exception("Image seems to be corrupted")
|
||||
# temperature from radiance
|
||||
return PlanckB / np.log(val_to_log) - 273.15
|
||||
|
||||
|
||||
def parse_from_exif_str(temp_str):
|
||||
"""String to float parser."""
|
||||
# we assume degrees celsius for temperature, metres for length
|
||||
if isinstance(temp_str, str):
|
||||
return float(temp_str.split()[0])
|
||||
return float(temp_str)
|
||||
|
||||
|
||||
def normalize_temp_matrix(thermal_np):
|
||||
"""Normalize a temperature matrix to the 0-255 uint8 image range."""
|
||||
num = thermal_np - np.amin(thermal_np)
|
||||
den = np.amax(thermal_np) - np.amin(thermal_np)
|
||||
thermal_np = num / den
|
||||
return thermal_np
|
||||
|
||||
def clip_temp_to_roi(thermal_np, thermal_roi_values):
|
||||
"""
|
||||
Given an RoI within a temperature matrix, this function clips the temperature values in the entire thermal.
|
||||
|
||||
Image temperature values above and below the max/min temperatures within the RoI are clipped to said max/min.
|
||||
|
||||
Args:
|
||||
thermal_np (np.ndarray): Floating point array containing the temperature matrix.
|
||||
thermal_roi_values (np.ndarray / list): Any iterable containing the temperature values within the RoI.
|
||||
|
||||
Returns:
|
||||
np.ndarray: The clipped temperature matrix.
|
||||
"""
|
||||
maximum = np.amax(thermal_roi_values)
|
||||
minimum = np.amin(thermal_roi_values)
|
||||
thermal_np[thermal_np > maximum] = maximum
|
||||
thermal_np[thermal_np < minimum] = minimum
|
||||
return thermal_np
|
||||
|
||||
|
||||
def scale_with_roi(thermal_np, thermal_roi_values):
|
||||
"""Alias for clip_temp_to_roi, to be deprecated in the future."""
|
||||
return clip_temp_to_roi(thermal_np, thermal_roi_values)
|
||||
|
|
@ -113,7 +113,7 @@ class ODMOpenSfMStage(types.ODM_Stage):
|
|||
def radiometric_calibrate(shot_id, image):
|
||||
photo = reconstruction.get_photo(shot_id)
|
||||
if photo.is_thermal():
|
||||
return thermal.dn_to_temperature(photo, image)
|
||||
return thermal.dn_to_temperature(photo, image, tree.dataset_raw)
|
||||
else:
|
||||
return multispectral.dn_to_reflectance(photo, image, use_sun_sensor=args.radiometric_calibration=="camera+sun")
|
||||
|
||||
|
|
|
|||
Ładowanie…
Reference in New Issue