kopia lustrzana https://github.com/OpenDroneMap/ODM
Piero Toffanin
rodzic
0e8a325996
commit
a61b5b308a
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@ -1,5 +1,9 @@
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from opendm import dls
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import math
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import numpy as np
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from opendm import log
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import cv2 # TODO REMOVE
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# Loosely based on https://github.com/micasense/imageprocessing/blob/master/micasense/utils.py
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def dn_to_radiance(photo, image):
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@ -9,10 +13,13 @@ def dn_to_radiance(photo, image):
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:param image numpy array containing image data
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:return numpy array with radiance image values
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"""
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image = image.astype(float)
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# Handle thermal bands (experimental)
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if photo.band_name == 'LWIR':
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image -= (273.15 * 100.0) # Convert Kelvin to Celsius
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image = image.astype(float) * 0.01
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image *= 0.01
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return image
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# All others
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@ -21,6 +28,14 @@ def dn_to_radiance(photo, image):
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exposure_time = photo.exposure_time
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gain = photo.get_gain()
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photometric_exp = photo.get_photometric_exposure()
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if a1 is None and photometric_exp is None:
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log.ODM_WARNING("Cannot perform radiometric calibration, no FNumber/Exposure Time or Radiometric Calibration EXIF tags found in %s. Using Digital Number." % photo.filename)
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return image
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if a1 is None and photometric_exp is not None:
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a1 = photometric_exp
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V, x, y = vignette_map(photo)
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if x is None:
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@ -28,25 +43,9 @@ def dn_to_radiance(photo, image):
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if dark_level is not None:
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image -= dark_level
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print("Adjusted black")
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if V is not None:
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# vignette correction
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image *= V
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if exposure_time and a2 is not None and a3 is not None:
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# row gradient correction
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R = 1.0 / (1.0 + a2 * y / exposure_time - a3 * y)
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image *= R
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# Floor any negative radiances to zero (can happend due to noise around blackLevel)
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if dark_level is not None:
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image[image < 0] = 0
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# apply the radiometric calibration - i.e. scale by the gain-exposure product and
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# multiply with the radiometric calibration coefficient
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# need to normalize by 2^16 for 16 bit images
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# because coefficients are scaled to work with input values of max 1.0
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# Normalize DN to 0 - 1.0
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bps = photo.bits_per_sample
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if bps:
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bit_depth_max = float(2 ** bps)
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@ -54,16 +53,34 @@ def dn_to_radiance(photo, image):
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# Infer from array dtype
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info = np.iinfo(image.dtype)
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bit_depth_max = info.max - info.min
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image /= bit_depth_max
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if V is not None:
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# vignette correction
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image *= V
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print("Adjusted vignette")
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if exposure_time and a2 is not None and a3 is not None:
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# row gradient correction
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R = 1.0 / (1.0 + a2 * y / exposure_time - a3 * y)
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image *= R
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cv2.imwrite("/datasets/mica/R.tif", R)
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print("Row gradient")
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image = image.astype(float)
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# Floor any negative radiances to zero (can happend due to noise around blackLevel)
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if dark_level is not None:
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image[image < 0] = 0
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# apply the radiometric calibration - i.e. scale by the gain-exposure product and
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# multiply with the radiometric calibration coefficient
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if gain is not None and exposure_time is not None:
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image /= (gain * exposure_time)
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print("Gain adjustment")
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if a1 is not None:
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image *= a1
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image /= bit_depth_max
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image *= a1
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return image
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@ -72,8 +89,7 @@ def vignette_map(photo):
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polynomial = photo.get_vignetting_polynomial()
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if x_vc and polynomial:
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# reverse list and append 1., so that we can call with numpy polyval
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polynomial.reverse()
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# append 1., so that we can call with numpy polyval
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polynomial.append(1.0)
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vignette_poly = np.array(polynomial)
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@ -82,8 +98,8 @@ def vignette_map(photo):
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x, y = np.meshgrid(np.arange(photo.width), np.arange(photo.height))
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# meshgrid returns transposed arrays
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x = x.T
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y = y.T
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# x = x.T
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# y = y.T
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# compute matrix of distances from image center
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r = np.hypot((x - x_vc), (y - y_vc))
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@ -116,7 +132,7 @@ def compute_irradiance_scale_factor(photo, use_sun_sensor=True):
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sun_vector_ned, sensor_vector_ned, sun_sensor_angle, \
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solar_elevation, solar_azimuth = dls.compute_sun_angle([photo.latitude, photo.longitude],
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(0,0,0), # TODO: add support for sun sensor pose
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photo.utc_time,
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photo.get_utc_time(),
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dls_orientation_vector)
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angular_correction = dls.fresnel(sun_sensor_angle)
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@ -36,6 +36,7 @@ class ODM_Photo:
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self.band_index = 0
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# Multi-spectral fields
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self.fnumber = None
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self.radiometric_calibration = None
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self.black_level = None
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@ -91,8 +92,18 @@ class ODM_Photo:
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if 'EXIF ExposureTime' in tags:
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self.exposure_time = self.float_value(tags['EXIF ExposureTime'])
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if 'EXIF FNumber' in tags:
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self.fnumber = self.float_value(tags['EXIF FNumber'])
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if 'EXIF ISOSpeed' in tags:
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self.iso_speed = self.int_value(tags['EXIF ISOSpeed'])
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elif 'EXIF PhotographicSensitivity' in tags:
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self.iso_speed = self.int_value(tags['EXIF PhotographicSensitivity'])
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elif 'EXIF ISOSpeedRatings' in tags:
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self.iso_speed = self.int_value(tags['EXIF ISOSpeedRatings'])
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if 'Image BitsPerSample' in tags:
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self.bits_per_sample = self.int_value(tags['Image BitsPerSample'])
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if 'EXIF DateTimeOriginal' in tags:
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@ -169,7 +180,8 @@ class ODM_Photo:
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# print(self.bits_per_sample)
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# print(self.vignetting_center)
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# print(self.sun_sensor)
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# exit(1)
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#print(self.get_vignetting_polynomial())
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#exit(1)
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self.width, self.height = get_image_size.get_image_size(_path_file)
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# Sanitize band name since we use it in folder paths
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@ -233,7 +245,10 @@ class ODM_Photo:
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)
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def float_values(self, tag):
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return map(lambda v: float(v.num) / float(v.den), tag.values)
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if isinstance(tag.values, list):
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return map(lambda v: float(v.num) / float(v.den), tag.values)
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else:
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return [float(tag.values.num) / float(tag.values.den)]
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def float_value(self, tag):
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v = self.float_values(tag)
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@ -241,7 +256,10 @@ class ODM_Photo:
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return v[0]
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def int_values(self, tag):
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return map(int, tag.values)
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if isinstance(tag.values, list):
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return map(int, tag.values)
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else:
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return [int(tag.values)]
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def int_value(self, tag):
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v = self.int_values(tag)
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@ -263,12 +281,11 @@ class ODM_Photo:
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if self.black_level:
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levels = np.array([float(v) for v in self.black_level.split(" ")])
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return levels.mean()
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return None
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def get_gain(self):
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#(gain = ISO/100)
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if self.iso_speed:
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return self.iso_speed / 100.0
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return None
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def get_vignetting_center(self):
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if self.vignetting_center:
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@ -281,6 +298,18 @@ class ODM_Photo:
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if self.vignetting_polynomial:
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parts = self.vignetting_polynomial.split(" ")
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if len(parts) > 0:
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return list(map(float, parts))
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return None
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coeffs = list(map(float, parts))
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# Different camera vendors seem to use different ordering for the coefficients
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if self.camera_make != "Sentera":
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coeffs.reverse()
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return coeffs
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def get_utc_time(self):
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if self.utc_time:
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return datetime.utcfromtimestamp(self.utc_time / 1000)
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def get_photometric_exposure(self):
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# H ~= (exposure_time) / (f_number^2)
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if self.fnumber is not None and self.exposure_time > 0:
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return self.exposure_time / (self.fnumber * self.fnumber)
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@ -9,7 +9,7 @@ from opendm import gsd
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from opendm import point_cloud
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from opendm import types
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from opendm.osfm import OSFMContext
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from opendm.multispectral import dn_to_radiance
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from opendm import multispectral
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class ODMOpenSfMStage(types.ODM_Stage):
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def process(self, args, outputs):
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@ -22,40 +22,40 @@ class ODMOpenSfMStage(types.ODM_Stage):
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exit(1)
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octx = OSFMContext(tree.opensfm)
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# octx.setup(args, tree.dataset_raw, photos, reconstruction=reconstruction, rerun=self.rerun())
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# octx.extract_metadata(self.rerun())
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# self.update_progress(20)
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# octx.feature_matching(self.rerun())
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# self.update_progress(30)
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# octx.reconstruct(self.rerun())
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# octx.extract_cameras(tree.path("cameras.json"), self.rerun())
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# self.update_progress(70)
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octx.setup(args, tree.dataset_raw, photos, reconstruction=reconstruction, rerun=self.rerun())
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octx.extract_metadata(self.rerun())
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self.update_progress(20)
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octx.feature_matching(self.rerun())
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self.update_progress(30)
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octx.reconstruct(self.rerun())
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octx.extract_cameras(tree.path("cameras.json"), self.rerun())
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self.update_progress(70)
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# # If we find a special flag file for split/merge we stop right here
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# if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
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# log.ODM_INFO("Stopping OpenSfM early because we found: %s" % octx.path("split_merge_stop_at_reconstruction.txt"))
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# self.next_stage = None
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# return
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# If we find a special flag file for split/merge we stop right here
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if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
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log.ODM_INFO("Stopping OpenSfM early because we found: %s" % octx.path("split_merge_stop_at_reconstruction.txt"))
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self.next_stage = None
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return
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# if args.fast_orthophoto:
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# output_file = octx.path('reconstruction.ply')
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# elif args.use_opensfm_dense:
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# output_file = tree.opensfm_model
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# else:
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# output_file = tree.opensfm_reconstruction
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if args.fast_orthophoto:
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output_file = octx.path('reconstruction.ply')
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elif args.use_opensfm_dense:
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output_file = tree.opensfm_model
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else:
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output_file = tree.opensfm_reconstruction
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# updated_config_flag_file = octx.path('updated_config.txt')
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updated_config_flag_file = octx.path('updated_config.txt')
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# # Make sure it's capped by the depthmap-resolution arg,
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# # since the undistorted images are used for MVS
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# outputs['undist_image_max_size'] = max(
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# gsd.image_max_size(photos, args.orthophoto_resolution, tree.opensfm_reconstruction, ignore_gsd=args.ignore_gsd, has_gcp=reconstruction.has_gcp()),
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# args.depthmap_resolution
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# )
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# Make sure it's capped by the depthmap-resolution arg,
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# since the undistorted images are used for MVS
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outputs['undist_image_max_size'] = max(
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gsd.image_max_size(photos, args.orthophoto_resolution, tree.opensfm_reconstruction, ignore_gsd=args.ignore_gsd, has_gcp=reconstruction.has_gcp()),
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args.depthmap_resolution
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)
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# if not io.file_exists(updated_config_flag_file) or self.rerun():
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# octx.update_config({'undistorted_image_max_size': outputs['undist_image_max_size']})
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# octx.touch(updated_config_flag_file)
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if not io.file_exists(updated_config_flag_file) or self.rerun():
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octx.update_config({'undistorted_image_max_size': outputs['undist_image_max_size']})
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octx.touch(updated_config_flag_file)
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# These will be used for texturing / MVS
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if args.radiometric_calibration == "none":
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@ -63,7 +63,7 @@ class ODMOpenSfMStage(types.ODM_Stage):
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else:
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def radiometric_calibrate(shot_id, image):
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photo = reconstruction.get_photo(shot_id)
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return dn_to_reflectance(photo, image, use_sun_sensor=args.radiometric_calibration=="camera+sun")
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return multispectral.dn_to_reflectance(photo, image, use_sun_sensor=args.radiometric_calibration=="camera+sun")
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octx.convert_and_undistort(self.rerun(), radiometric_calibrate)
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