kopia lustrzana https://github.com/OpenDroneMap/ODM
Zenmuse XT support
Piero Toffanin
rodzic
a922aaecbc
commit
80e4b4d649
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@ -178,6 +178,7 @@ set(custom_libs OpenSfM
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PyPopsift
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Obj2Tiles
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OpenPointClass
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ExifTool
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)
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externalproject_add(mve
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@ -0,0 +1,94 @@
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import json
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import os
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import tempfile
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import base64
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from rasterio.io import MemoryFile
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from opendm.system import run
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from opendm import log
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from opendm.utils import double_quote
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def extract_raw_thermal_image_data(image_path):
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try:
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f, tmp_file_path = tempfile.mkstemp(suffix='.json')
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os.close(f)
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try:
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output = run("exiftool -b -x ThumbnailImage -x PreviewImage -j \"%s\" > \"%s\"" % (image_path, tmp_file_path), quiet=True)
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with open(tmp_file_path) as f:
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j = json.loads(f.read())
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if isinstance(j, list):
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j = j[0] # single file
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if "RawThermalImage" in j:
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imageBytes = base64.b64decode(j["RawThermalImage"][len("base64:"):])
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with MemoryFile(imageBytes) as memfile:
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with memfile.open() as dataset:
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img = dataset.read()
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bands, h, w = img.shape
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if bands != 1:
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raise Exception("Raw thermal image has more than one band? This is not supported")
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# (1, 512, 640) --> (512, 640, 1)
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img = img[0][:,:,None]
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del j["RawThermalImage"]
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return extract_temperature_params_from(j), img
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else:
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raise Exception("Invalid JSON (not a list)")
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except Exception as e:
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log.ODM_WARNING("Cannot extract tags using exiftool: %s" % str(e))
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return {}, None
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finally:
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if os.path.isfile(tmp_file_path):
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os.remove(tmp_file_path)
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except Exception as e:
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log.ODM_WARNING("Cannot create temporary file: %s" % str(e))
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return {}, None
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def unit(unit):
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def _convert(v):
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if isinstance(v, float):
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return v
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elif isinstance(v, str):
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if not v[-1].isnumeric():
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if v[-1].upper() != unit.upper():
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log.ODM_WARNING("Assuming %s is in %s" % (v, unit))
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return float(v[:-1])
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else:
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return float(v)
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else:
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return float(v)
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return _convert
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def extract_temperature_params_from(tags):
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# Defaults
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meta = {
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"Emissivity": float,
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"ObjectDistance": unit("m"),
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"AtmosphericTemperature": unit("C"),
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"ReflectedApparentTemperature": unit("C"),
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"IRWindowTemperature": unit("C"),
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"IRWindowTransmission": float,
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"RelativeHumidity": unit("%"),
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"PlanckR1": float,
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"PlanckB": float,
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"PlanckF": float,
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"PlanckO": float,
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"PlanckR2": float,
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}
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params = {}
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for m in meta:
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if m not in tags:
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# All or nothing
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raise Exception("Cannot find %s in tags" % m)
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params[m] = (meta[m])(tags[m])
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return params
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@ -305,7 +305,7 @@ class ODM_Photo:
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for xtags in xmp:
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try:
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band_name = self.get_xmp_tag(xtags, ['Camera:BandName', '@Camera:BandName'])
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band_name = self.get_xmp_tag(xtags, ['Camera:BandName', '@Camera:BandName', 'FLIR:BandName'])
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if band_name is not None:
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self.band_name = band_name.replace(" ", "")
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@ -66,11 +66,12 @@ def sighandler(signum, frame):
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signal.signal(signal.SIGINT, sighandler)
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signal.signal(signal.SIGTERM, sighandler)
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def run(cmd, env_paths=[context.superbuild_bin_path], env_vars={}, packages_paths=context.python_packages_paths):
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def run(cmd, env_paths=[context.superbuild_bin_path], env_vars={}, packages_paths=context.python_packages_paths, quiet=False):
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"""Run a system command"""
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global running_subprocesses
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log.ODM_INFO('running %s' % cmd)
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if not quiet:
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log.ODM_INFO('running %s' % cmd)
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env = os.environ.copy()
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sep = ":"
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@ -101,7 +102,8 @@ def run(cmd, env_paths=[context.superbuild_bin_path], env_vars={}, packages_path
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retcode = p.wait()
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log.logger.log_json_process(cmd, retcode, list(lines))
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if not quiet:
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log.logger.log_json_process(cmd, retcode, list(lines))
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running_subprocesses.remove(p)
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if retcode < 0:
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@ -1,7 +1,9 @@
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from opendm import log
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from opendm.thermal_tools import dji_unpack
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import cv2
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import os
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from opendm import log
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from opendm.thermal_tools import dji_unpack
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from opendm.exiftool import extract_raw_thermal_image_data
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from opendm.thermal_tools.thermal_utils import sensor_vals_to_temp
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def resize_to_match(image, match_photo = None):
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"""
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@ -19,17 +21,15 @@ def resize_to_match(image, match_photo = None):
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interpolation=cv2.INTER_LANCZOS4)
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return image
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def dn_to_temperature(photo, image, dataset_tree):
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def dn_to_temperature(photo, image, images_path):
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"""
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Convert Digital Number values to temperature (C) values
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:param photo ODM_Photo
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:param image numpy array containing image data
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:param dataset_tree path to original source image to read data using PIL for DJI thermal photos
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:param images_path path to original source image to read data using PIL for DJI thermal photos
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:return numpy array with temperature (C) image values
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"""
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# Handle thermal bands
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if photo.is_thermal():
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# Every camera stores thermal information differently
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@ -51,11 +51,18 @@ def dn_to_temperature(photo, image, dataset_tree):
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else:
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return image
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elif photo.camera_make == "DJI" and photo.camera_model == "MAVIC2-ENTERPRISE-ADVANCED":
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image = dji_unpack.extract_temperatures_dji(photo, image, dataset_tree)
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image = dji_unpack.extract_temperatures_dji(photo, image, images_path)
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image = image.astype("float32")
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return image
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else:
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log.ODM_WARNING("Unsupported camera [%s %s], thermal band will have digital numbers." % (photo.camera_make, photo.camera_model))
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try:
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params, image = extract_raw_thermal_image_data(os.path.join(images_path, photo.filename))
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image = sensor_vals_to_temp(image, **params)
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except Exception as e:
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log.ODM_WARNING("Cannot radiometrically calibrate %s: %s" % (photo.filename, str(e)))
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image = image.astype("float32")
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return image
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else:
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image = image.astype("float32")
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log.ODM_WARNING("Tried to radiometrically calibrate a non-thermal image with temperature values (%s)" % photo.filename)
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@ -1,271 +0,0 @@
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"""
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THIS IS WIP, DON'T USE THIS FILE, IT IS HERE FOR FURTHER IMPROVEMENT
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Tools for extracting thermal data from FLIR images.
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Derived from https://bitbucket.org/nimmerwoner/flyr/src/master/
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"""
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import os
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from io import BufferedIOBase, BytesIO
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from typing import BinaryIO, Dict, Optional, Tuple, Union
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import numpy as np
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from PIL import Image
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# Constants
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SEGMENT_SEP = b"\xff"
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APP1_MARKER = b"\xe1"
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MAGIC_FLIR_DEF = b"FLIR\x00"
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CHUNK_APP1_BYTES_COUNT = len(APP1_MARKER)
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CHUNK_LENGTH_BYTES_COUNT = 2
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CHUNK_MAGIC_BYTES_COUNT = len(MAGIC_FLIR_DEF)
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CHUNK_SKIP_BYTES_COUNT = 1
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CHUNK_NUM_BYTES_COUNT = 1
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CHUNK_TOT_BYTES_COUNT = 1
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CHUNK_PARTIAL_METADATA_LENGTH = CHUNK_APP1_BYTES_COUNT + CHUNK_LENGTH_BYTES_COUNT + CHUNK_MAGIC_BYTES_COUNT
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CHUNK_METADATA_LENGTH = (
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CHUNK_PARTIAL_METADATA_LENGTH + CHUNK_SKIP_BYTES_COUNT + CHUNK_NUM_BYTES_COUNT + CHUNK_TOT_BYTES_COUNT
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)
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def unpack(path_or_stream: Union[str, BinaryIO]) -> np.ndarray:
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"""Unpacks the FLIR image, meaning that it will return the thermal data embedded in the image.
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Parameters
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----------
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path_or_stream : Union[str, BinaryIO]
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Either a path (string) to a FLIR file, or a byte stream such as
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BytesIO or file opened as `open(file_path, "rb")`.
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Returns
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-------
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FlyrThermogram
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When successful, a FlyrThermogram object containing thermogram data.
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"""
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if isinstance(path_or_stream, str) and os.path.isfile(path_or_stream):
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with open(path_or_stream, "rb") as flirh:
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return unpack(flirh)
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elif isinstance(path_or_stream, BufferedIOBase):
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stream = path_or_stream
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flir_app1_stream = extract_flir_app1(stream)
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flir_records = parse_flir_app1(flir_app1_stream)
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raw_np = parse_thermal(flir_app1_stream, flir_records)
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return raw_np
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else:
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raise ValueError("Incorrect input")
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def extract_flir_app1(stream: BinaryIO) -> BinaryIO:
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"""Extracts the FLIR APP1 bytes.
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Parameters
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---------
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stream : BinaryIO
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A full bytes stream of a JPEG file, expected to be a FLIR file.
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Raises
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------
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ValueError
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When the file is invalid in one the next ways, a
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ValueError is thrown.
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* File is not a JPEG
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* A FLIR chunk number occurs more than once
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* The total chunks count is inconsistent over multiple chunks
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* No APP1 segments are successfully parsed
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Returns
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-------
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BinaryIO
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A bytes stream of the APP1 FLIR segments
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"""
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# Check JPEG-ness
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_ = stream.read(2)
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chunks_count: Optional[int] = None
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chunks: Dict[int, bytes] = {}
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while True:
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b = stream.read(1)
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if b == b"":
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break
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if b != SEGMENT_SEP:
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continue
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parsed_chunk = parse_flir_chunk(stream, chunks_count)
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if not parsed_chunk:
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continue
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chunks_count, chunk_num, chunk = parsed_chunk
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chunk_exists = chunks.get(chunk_num, None) is not None
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if chunk_exists:
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raise ValueError("Invalid FLIR: duplicate chunk number")
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chunks[chunk_num] = chunk
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# Encountered all chunks, break out of loop to process found metadata
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if chunk_num == chunks_count:
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break
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if chunks_count is None:
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raise ValueError("Invalid FLIR: no metadata encountered")
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flir_app1_bytes = b""
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for chunk_num in range(chunks_count + 1):
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flir_app1_bytes += chunks[chunk_num]
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flir_app1_stream = BytesIO(flir_app1_bytes)
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flir_app1_stream.seek(0)
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return flir_app1_stream
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def parse_flir_chunk(stream: BinaryIO, chunks_count: Optional[int]) -> Optional[Tuple[int, int, bytes]]:
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"""Parse flir chunk."""
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# Parse the chunk header. Headers are as follows (definition with example):
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#
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# \xff\xe1<length: 2 bytes>FLIR\x00\x01<chunk nr: 1 byte><chunk count: 1 byte>
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# \xff\xe1\xff\xfeFLIR\x00\x01\x01\x0b
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#
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# Meaning: Exif APP1, 65534 long, FLIR chunk 1 out of 12
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marker = stream.read(CHUNK_APP1_BYTES_COUNT)
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length_bytes = stream.read(CHUNK_LENGTH_BYTES_COUNT)
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length = int.from_bytes(length_bytes, "big")
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length -= CHUNK_METADATA_LENGTH
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magic_flir = stream.read(CHUNK_MAGIC_BYTES_COUNT)
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if not (marker == APP1_MARKER and magic_flir == MAGIC_FLIR_DEF):
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# Seek back to just after byte b and continue searching for chunks
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stream.seek(-len(marker) - len(length_bytes) - len(magic_flir), 1)
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return None
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stream.seek(1, 1) # skip 1 byte, unsure what it is for
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chunk_num = int.from_bytes(stream.read(CHUNK_NUM_BYTES_COUNT), "big")
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chunks_tot = int.from_bytes(stream.read(CHUNK_TOT_BYTES_COUNT), "big")
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# Remember total chunks to verify metadata consistency
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if chunks_count is None:
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chunks_count = chunks_tot
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if ( # Check whether chunk metadata is consistent
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chunks_tot is None or chunk_num < 0 or chunk_num > chunks_tot or chunks_tot != chunks_count
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):
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raise ValueError(f"Invalid FLIR: inconsistent total chunks, should be 0 or greater, but is {chunks_tot}")
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return chunks_tot, chunk_num, stream.read(length + 1)
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def parse_thermal(stream: BinaryIO, records: Dict[int, Tuple[int, int, int, int]]) -> np.ndarray:
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"""Parse thermal."""
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RECORD_IDX_RAW_DATA = 1
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raw_data_md = records[RECORD_IDX_RAW_DATA]
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_, _, raw_data = parse_raw_data(stream, raw_data_md)
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return raw_data
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def parse_flir_app1(stream: BinaryIO) -> Dict[int, Tuple[int, int, int, int]]:
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"""Parse flir app1."""
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# 0x00 - string[4] file format ID = "FFF\0"
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# 0x04 - string[16] file creator: seen "\0","MTX IR\0","CAMCTRL\0"
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# 0x14 - int32u file format version = 100
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# 0x18 - int32u offset to record directory
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# 0x1c - int32u number of entries in record directory
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# 0x20 - int32u next free index ID = 2
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# 0x24 - int16u swap pattern = 0 (?)
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# 0x28 - int16u[7] spares
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# 0x34 - int32u[2] reserved
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# 0x3c - int32u checksum
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# 1. Read 0x40 bytes and verify that its contents equals AFF\0 or FFF\0
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_ = stream.read(4)
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# 2. Read FLIR record directory metadata (ref 3)
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stream.seek(16, 1)
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_ = int.from_bytes(stream.read(4), "big")
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record_dir_offset = int.from_bytes(stream.read(4), "big")
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record_dir_entries_count = int.from_bytes(stream.read(4), "big")
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stream.seek(28, 1)
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_ = int.from_bytes(stream.read(4), "big")
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# 3. Read record directory (which is a FLIR record entry repeated
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# `record_dir_entries_count` times)
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stream.seek(record_dir_offset)
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record_dir_stream = BytesIO(stream.read(32 * record_dir_entries_count))
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# First parse the record metadata
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record_details: Dict[int, Tuple[int, int, int, int]] = {}
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for record_nr in range(record_dir_entries_count):
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record_dir_stream.seek(0)
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details = parse_flir_record_metadata(stream, record_nr)
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if details:
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record_details[details[1]] = details
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# Then parse the actual records
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# for (entry_idx, type, offset, length) in record_details:
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# parse_record = record_parsers[type]
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# stream.seek(offset)
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# record = BytesIO(stream.read(length + 36)) # + 36 needed to find end
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# parse_record(record, offset, length)
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return record_details
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def parse_flir_record_metadata(stream: BinaryIO, record_nr: int) -> Optional[Tuple[int, int, int, int]]:
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"""Parse flir record metadata."""
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# FLIR record entry (ref 3):
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# 0x00 - int16u record type
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# 0x02 - int16u record subtype: RawData 1=BE, 2=LE, 3=PNG; 1 for other record types
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# 0x04 - int32u record version: seen 0x64,0x66,0x67,0x68,0x6f,0x104
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# 0x08 - int32u index id = 1
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# 0x0c - int32u record offset from start of FLIR data
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# 0x10 - int32u record length
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# 0x14 - int32u parent = 0 (?)
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# 0x18 - int32u object number = 0 (?)
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# 0x1c - int32u checksum: 0 for no checksum
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entry = 32 * record_nr
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stream.seek(entry)
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record_type = int.from_bytes(stream.read(2), "big")
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if record_type < 1:
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return None
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_ = int.from_bytes(stream.read(2), "big")
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_ = int.from_bytes(stream.read(4), "big")
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_ = int.from_bytes(stream.read(4), "big")
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record_offset = int.from_bytes(stream.read(4), "big")
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record_length = int.from_bytes(stream.read(4), "big")
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_ = int.from_bytes(stream.read(4), "big")
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_ = int.from_bytes(stream.read(4), "big")
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_ = int.from_bytes(stream.read(4), "big")
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return (entry, record_type, record_offset, record_length)
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def parse_raw_data(stream: BinaryIO, metadata: Tuple[int, int, int, int]):
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"""Parse raw data."""
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(_, _, 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
|
||||
Ładowanie…
Reference in New Issue