def processImage(iset, sub, imagePath, imageRoot, band, radianceToReflectance):
'''Does 4 steps: converts raw image to radiance based on metadata, converts radiance to relfectance based on panel calibration, un-distorts based on lens correction, and adds metadata to output'''
outnm = 'Output\\%04i_%s_%s_%s_radiance.tiff' % (band, iset, sub,
imageRoot)
img = image.Image(imagePath)
outImg = img.undistorted(img.reflectance(radianceToReflectance))
rows, cols = outImg.shape
driver = gdal.GetDriverByName('GTiff')
outRaster = driver.Create(outnm, cols, rows, 1, gdal.GDT_Float32)
outband = outRaster.GetRasterBand(1)
outband.WriteArray(outImg[:, :])
outband.FlushCache()
tagsToCopy = ["EXIF:GPSAltitude"] #,
# "EXIF:GPSAltitudeRef",
# "EXIF:GPSDOP",
# "EXIF:GPSLatitude",
# "EXIF:GPSLatitudeRef",
# "EXIF:GPSLongitude",
# "EXIF:GPSLongitudeRef",
# "EXIF:GPSVersionID"
# ]
print("Copying")
meta = metadata.Metadata(imagePath,
exiftoolPath=os.environ['exiftoolpath'])
meta.copy(outnm, tagsToCopy)
printExif(imagePath, tagsToCopy)
printExif(outnm, tagsToCopy)
def reading_gain_exposure(image_path, ms_ext='tif', sub_dir=False):
columns = ['image', 'gain', 'exposure', 'dls_gain', 'dls_exposure']
data_list = []
if image_path.endswith(ms_ext) or image_path.endswith(
'tiff'): # loading only one image
names_list = image_path
elif sub_dir:
names_list = [
f for f in sorted(
glob.glob(image_path + "/**/*." + ms_ext, recursive=True))
]
else:
names_list = [
f for f in sorted(
glob.glob(image_path + "/*." + ms_ext, recursive=True))
]
for n, im in enumerate(names_list):
meta = metadata.Metadata(im)
name = meta.get_item('File:FileName')[:-4]
gain = meta.get_item('XMP:Gain')
exposure = meta.get_item('XMP:Exposure')
dls_gain = meta.get_item('XMP:IrradianceGain')
dls_exposure = meta.get_item('XMP:IrradianceExposureTime')
row = [name, gain, exposure, dls_gain, dls_exposure]
data_list.append(row)
print('image {} out of {}'.format(n + 1, len(names_list)))
df = pd.DataFrame(data_list, index=None, columns=columns)
return df
def createJson(filename, bombCoors):
filepath = directory + filename
img = metadata.Metadata(filepath)
lat = img.get_item('Composite:GPSLatitude')
long = img.get_item('Composite:GPSLongitude')
yaw, pitch, roll = img.dls_pose()
alt = img.get_item('Composite:GPSAltitude')
elevation = getElevation(str(lat),str(long))
GPS, bombs = checkingIfBomb(getGPS(img, elevation, lat, long, alt), bombCoors)
image_name = filename.split('.')[0][:-2] + '_stacked.tiff'
data = {
"image_name" : image_name,
"yaw" : yaw,
"pitch" : pitch,
"roll": roll,
"height" : alt - elevation,
"GPS" : [
{"area" : "center", "lat" : lat, "long": long},
{"area": "top right", "lat" :GPS[0][0], "long": GPS[0][1]},
{"area": "top left", "lat" :GPS[1][0], "long": GPS[1][1]},
{"area": "bottom right", "lat" :GPS[2][0], "long": GPS[2][1]},
{"area": "bottom left", "lat" :GPS[3][0], "long": GPS[3][1]}
],
"bombs" : [
],
}
for index, bomb in enumerate(bombs):
item = {"id" : index , "lat" : bomb[0], "long" : bomb[1]}
data["bombs"].append(item)
print(json.dumps(data, indent=4, sort_keys=True))
print()
meta_json = os.path.join('stacked', 'metadata.json')
with open(meta_json) as json_file:
curr_data = json.load(json_file)
temp= curr_data["images"]
if( len(temp) == 0):
temp.append(data)
else:
append = True
for image in temp:
if(image["image_name"] == data["image_name"]):
append = False
if append:
temp.append(data)
write_json(curr_data, meta_json)
def __init__(self, image_path):
if not os.path.isfile(image_path):
raise IOError("Provided path is not a file: {}".format(image_path))
self.path = image_path
self.meta = metadata.Metadata(self.path)
if not self.meta.supports_radiometric_calibration():
raise ValueError(
'Library requires images taken with camera firmware v2.1.0 or later. '
+ 'Upgrade your camera firmware to use this library.')
self.utc_time = self.meta.utc_time()
self.latitude, self.longitude, self.altitude = self.meta.position()
self.dls_present = self.meta.dls_present()
self.dls_yaw, self.dls_pitch, self.dls_roll = self.meta.dls_pose()
self.dls_irradiance = self.meta.dls_irradiance()
self.capture_id = self.meta.capture_id()
self.flight_id = self.meta.flight_id()
self.band_name = self.meta.band_name()
self.band_index = self.meta.band_index()
self.black_level = self.meta.black_level()
self.radiometric_cal = self.meta.radiometric_cal()
self.exposure_time = self.meta.exposure()
self.gain = self.meta.gain()
self.bits_per_pixel = self.meta.bits_per_pixel()
self.vignette_center = self.meta.vignette_center()
self.vignette_polynomial = self.meta.vignette_polynomial()
self.distortion_parameters = self.meta.distortion_parameters()
self.principal_point = self.meta.principal_point()
self.focal_plane_resolution_px_per_mm = self.meta.focal_plane_resolution_px_per_mm(
)
self.focal_length = self.meta.focal_length_mm()
self.center_wavelength = self.meta.center_wavelength()
self.bandwidth = self.meta.bandwidth()
if self.bits_per_pixel != 16:
NotImplemented("Unsupported pixel bit depth: {} bits".format(
self.bits_per_pixel))
self.__raw_image = None # pure raw pixels
self.__intensity_image = None # black level and gain-exposure/radiometric compensated
self.__radiance_image = None # calibrated to radiance
self.__reflectance_image = None # calibrated to reflectance (0-1)
self.__reflectance_irradiance = None
self.__undistorted_source = None # can be any of raw, intensity, radiance
self.__undistorted_image = None # current undistorted image, depdining on source
def sortImageryByAlt(path, cutoffElev):
'''sort imagery into lists of panel images or flight images'''
data = {}
count = 0
for i in range(100):
for typ in ['SET', 'DUP']:
iset = '%04i%s' % (i, typ)
if os.path.exists(os.path.join(path, iset)):
for j in range(100):
sub = '%03i' % j
if os.path.exists(os.path.join(path, iset, sub)):
images = []
panels = []
for img in range(2001):
fname = 'IMG_%04i_*.tif' % (img)
found = True
for band in range(1, 6):
imageryPath = os.path.join(
path, iset, sub,
fname.replace("*", str(band)))
if os.path.exists(imageryPath):
count += 1
else:
found = False
print(count, imageryPath, "complete:", found)
if found:
meta = metadata.Metadata(
imageryPath,
exiftoolPath=os.environ['exiftoolpath'])
if meta.position()[2] > cutoffElev:
images.append(fname)
else:
panels.append(fname)
for k in [images, panels]:
if k:
if iset not in data: data[iset] = {}
if sub not in data[iset]: data[iset][sub] = {}
if images:
data[iset][sub]['images'] = images
if panels:
data[iset][sub]['panels'] = panels
return data
def updateDatabase(filename, bombCoors):
filepath = directory + filename
img = metadata.Metadata(filepath)
lat = img.get_item('Composite:GPSLatitude')
lon = img.get_item('Composite:GPSLongitude')
yaw, pitch, roll = img.dls_pose()
alt = img.get_item('Composite:GPSAltitude')
elevation = getElevation(str(lat),str(lon))
image_name = filename.split('.')[0][:-2] + '_stacked.tiff'
image_id = newImgId()
statement = "INSERT INTO images (image_id, image_name, yaw, pitch, roll, height) VALUES(" + str(image_id) + ", '"+ image_name + "', " + str(yaw) + "," + str(pitch) + "," + str(roll) + "," + str(height) + ")"
result = db.query_db (statement, 'EDIT')
GPS, bombs = checkingIfBomb(getGPS(img, elevation, lat, lon, alt), bombCoors)
updateGps(image_id, GPS, lat, lon)
updateBombs(image_id, bombs)
def meta_v3():
image_path = os.path.join('data', '0001SET', '000')
return metadata.Metadata(os.path.join(image_path, 'IMG_0002_4.tif'))
def meta():
image_path = os.path.join('data', '0000SET', '000')
return metadata.Metadata(os.path.join(image_path, 'IMG_0000_1.tif'))
def __init__(self, image_path, exiftool_obj=None):
if not os.path.isfile(image_path):
raise IOError("Provided path is not a file: {}".format(image_path))
self.path = image_path
self.meta = metadata.Metadata(self.path, exiftool_obj=exiftool_obj)
if self.meta.band_name() is None:
raise ValueError("Provided file path does not have a band name: {}".format(image_path))
if self.meta.band_name().upper() != 'LWIR' and not self.meta.supports_radiometric_calibration():
raise ValueError('Library requires images taken with RedEdge-(3/M/MX) camera firmware v2.1.0 or later. ' +
'Upgrade your camera firmware to at least version 2.1.0 to use this library with RedEdge-(3/M/MX) cameras.')
self.utc_time = self.meta.utc_time()
self.latitude, self.longitude, self.altitude = self.meta.position()
self.location = (self.latitude, self.longitude, self.altitude)
self.dls_present = self.meta.dls_present()
self.dls_yaw, self.dls_pitch, self.dls_roll = self.meta.dls_pose()
self.capture_id = self.meta.capture_id()
self.flight_id = self.meta.flight_id()
self.band_name = self.meta.band_name()
self.band_index = self.meta.band_index()
self.black_level = self.meta.black_level()
if self.meta.supports_radiometric_calibration():
self.radiometric_cal = self.meta.radiometric_cal()
self.exposure_time = self.meta.exposure()
self.gain = self.meta.gain()
self.bits_per_pixel = self.meta.bits_per_pixel()
self.vignette_center = self.meta.vignette_center()
self.vignette_polynomial = self.meta.vignette_polynomial()
self.distortion_parameters = self.meta.distortion_parameters()
self.principal_point = self.meta.principal_point()
self.focal_plane_resolution_px_per_mm = self.meta.focal_plane_resolution_px_per_mm()
self.focal_length = self.meta.focal_length_mm()
self.focal_length_35 = self.meta.focal_length_35_mm_eq()
self.center_wavelength = self.meta.center_wavelength()
self.bandwidth = self.meta.bandwidth()
self.rig_relatives = self.meta.rig_relatives()
self.spectral_irradiance = self.meta.spectral_irradiance()
self.auto_calibration_image = self.meta.auto_calibration_image()
self.panel_albedo = self.meta.panel_albedo()
self.panel_region = self.meta.panel_region()
self.panel_serial = self.meta.panel_serial()
if self.dls_present:
self.dls_orientation_vector = np.array([0, 0, -1])
self.sun_vector_ned, \
self.sensor_vector_ned, \
self.sun_sensor_angle, \
self.solar_elevation, \
self.solar_azimuth = dls.compute_sun_angle(self.location,
self.meta.dls_pose(),
self.utc_time,
self.dls_orientation_vector)
self.angular_correction = dls.fresnel(self.sun_sensor_angle)
# when we have good horizontal irradiance the camera provides the solar az and el also
if self.meta.scattered_irradiance() != 0 and self.meta.direct_irradiance() != 0:
self.solar_azimuth = self.meta.solar_azimuth()
self.solar_elevation = self.meta.solar_elevation()
self.scattered_irradiance = self.meta.scattered_irradiance()
self.direct_irradiance = self.meta.direct_irradiance()
self.direct_to_diffuse_ratio = self.meta.direct_irradiance() / self.meta.scattered_irradiance()
self.estimated_direct_vector = self.meta.estimated_direct_vector()
if self.meta.horizontal_irradiance_valid():
self.horizontal_irradiance = self.meta.horizontal_irradiance()
else:
self.horizontal_irradiance = self.compute_horizontal_irradiance_dls2()
else:
self.direct_to_diffuse_ratio = 6.0 # assumption
self.horizontal_irradiance = self.compute_horizontal_irradiance_dls1()
self.spectral_irradiance = self.meta.spectral_irradiance()
else: # no dls present or LWIR band: compute what we can, set the rest to 0
self.dls_orientation_vector = np.array([0, 0, -1])
self.sun_vector_ned, \
self.sensor_vector_ned, \
self.sun_sensor_angle, \
self.solar_elevation, \
self.solar_azimuth = dls.compute_sun_angle(self.location,
(0, 0, 0),
self.utc_time,
self.dls_orientation_vector)
self.angular_correction = dls.fresnel(self.sun_sensor_angle)
self.horizontal_irradiance = 0
self.scattered_irradiance = 0
self.direct_irradiance = 0
self.direct_to_diffuse_ratio = 0
# Internal image containers; these can use a lot of memory, clear with Image.clear_images
self.__raw_image = None # pure raw pixels
self.__intensity_image = None # black level and gain-exposure/radiometric compensated
self.__radiance_image = None # calibrated to radiance
self.__reflectance_image = None # calibrated to reflectance (0-1)
self.__reflectance_irradiance = None
self.__undistorted_source = None # can be any of raw, intensity, radiance
self.__undistorted_image = None # current undistorted image, depdining on source
sbr_G = 0
sbr_R = 0
sbr_E = 0
sbr_N = 0
# Num of each band's radiance
nbr_B = 0
nbr_G = 0
nbr_R = 0
nbr_E = 0
nbr_N = 0
for im in imageFiles:
# Read raw image DN values
imageName = filePath + os.sep + "low_altitude" + os.sep + im
imageRaw = plt.imread(imageName)
print("Processing %s" % imageName)
meta = metadata.Metadata(imageName, exiftoolPath=exiftoolPath)
bandName = meta.get_item('XMP:BandName')
radianceImage, L, V, R = msutils.raw_image_to_radiance(meta, imageRaw)
panel_coords = panelDetect(imageName, black_th, cont_th)
print('Panel Coords', panel_coords[0][0][0])
# Extract coordinates
if panel_coords[0][0][0]:
nw_x = int(panel_coords[0][0][0])
nw_y = int(panel_coords[0][0][1])
sw_x = int(panel_coords[1][0][0])
sw_y = int(panel_coords[1][0][1])
se_x = int(panel_coords[2][0][0])
se_y = int(panel_coords[2][0][1])
ne_x = int(panel_coords[3][0][0])
ne_y = int(panel_coords[3][0][1])
x_min = numpy.min([nw_x, sw_x, ne_x, se_x])
def get_band(image):
meta = metadata.Metadata(image, exiftoolPath=exiftoolPath)
band = meta.get_item('XMP:BandName')
return band
#PREFLIGHT CALIBRATION IMAGES
##############
#Band 1 (Blue)
#Plotting
imageName = os.path.join(CalibrationFolder_preflight, 'IMG_0002_1.tif')
imageRaw = plt.imread(
imageName).T # Read raw image DN values - 16 bit tif only
plt.imshow(imageRaw.T, cmap='gray')
plotutils.colormap('viridis')
# Optional: pick a color map: 'gray, viridis, plasma, inferno, magma, nipy_spectral'
fig = plotutils.plotwithcolorbar(imageRaw.T,
title='Raw image values with colorbar')
#Image metadata
meta = metadata.Metadata(imageName, exiftoolPath=exiftoolPath)
bandName = meta.get_item('XMP:BandName')
#Converting raw images to Radiance
radianceImage, L, V, R = msutils.raw_image_to_radiance(meta, imageRaw.T)
plotutils.plotwithcolorbar(V, 'Vignette Factor')
plotutils.plotwithcolorbar(R, 'Row Gradient Factor')
plotutils.plotwithcolorbar(V * R, 'Combined Corrections')
plotutils.plotwithcolorbar(L, 'Vignette and row gradient corrected raw values')
plotutils.plotwithcolorbar(radianceImage,
'All factors applied and scaled to radiance')
#Mask to panel and calculate radiance
markedImg = radianceImage.copy()
ulx = 510 # upper left column (x coordinate) of panel area
uly = 350 # upper left row (y coordinate) of panel area
panelSize = 0
panelSize = panelSizeEval(imageName, black_th)
if panelSize > 0:
pSizeList.append(panelSize)
pSizeArray = numpy.asarray(pSizeList)
bn = int((pSizeArray.max() - pSizeArray.min()) / 5)
hist, bin_edges = numpy.histogram(pSizeList, bins=bn, density=False)
cont_th = bin_edges[numpy.argmax(hist) + 1]
print("Panel contour size is close to %s" % (int)(cont_th))
#
for im in imageFiles:
# Read raw image DN values
imageName = filePath + "\\low_altitude\\" + im
imageRaw = plt.imread(imageName)
print("Processing %s" % imageName)
meta = metadata.Metadata(imageName, exiftoolPath=exiftoolPath)
bandName = meta.get_item('XMP:BandName')
radianceImage, L, V, R = msutils.raw_image_to_radiance(meta, imageRaw)
panel_coords = panelDetect(imageName, black_th, (int)(cont_th))
# Extract coordinates
if panel_coords[0][0][0]:
nw_x = int(panel_coords[0][0][0])
nw_y = int(panel_coords[0][0][1])
sw_x = int(panel_coords[1][0][0])
sw_y = int(panel_coords[1][0][1])
se_x = int(panel_coords[2][0][0])
se_y = int(panel_coords[2][0][1])
ne_x = int(panel_coords[3][0][0])
ne_y = int(panel_coords[3][0][1])
x_min = numpy.min([nw_x, sw_x, ne_x, se_x])
x_max = numpy.max([nw_x, sw_x, ne_x, se_x])
import numpy as np
import cv2
import micasense.utils as msutils
import micasense.metadata as metadata
import exiftool
import matplotlib.pyplot as plt
import os,glob
import math
exiftoolPath = None
if os.name == 'nt':
exiftoolPath = 'C:/exiftool/exiftool.exe'
imageRaw = '/mnt/114e4710-77b7-4a37-a8f6-0177deea301b/Desktop/Link to Mine/MicaSense/imageprocessing-master/data/0000SET/000/IMG_0001_3.tif'
meta = metadata.Metadata(imageRaw)
radianceImage, _,_,_,_ = msutils.raw_image_to_radiance(meta, imageRaw)
plotutils.plotwithcolorbar(V,'Vignette Factor')
plotutils.plotwithcolorbar(R,'Row Gradient Factor')
plotutils.plotwithcolorbar(V*R,'Combined Corrections')
plotutils.plotwithcolorbar(L,'Vignette and row gradient corrected raw values')
plotutils.plotwithcolorbar(radianceImage,'All factors applied and scaled to radiance')
# Define DLS sensor orientation vector relative to dls pose frame
dls_orientation_vector = np.array([0, 0, -1])
# compute sun orientation and sun-sensor angles
(
sun_vector_ned, # Solar vector in North-East-Down coordinates
sensor_vector_ned, # DLS vector in North-East-Down coordinates
sun_sensor_angle, # Angle between DLS vector and sun vector
solar_elevation, # Elevation of the sun above the horizon
solar_azimuth, # Azimuth (heading) of the sun
) = dls.compute_sun_angle(cap.location(), cap.dls_pose(), cap.utc_time(),
dls_orientation_vector)
# Get Spectral Irradiance (= Sun Sensor Irradiance) for each image from its metadata
spectral_irradiances = []
meta = metadata.Metadata(raw_image, exiftoolPath=None)
spectral_irradiances.append(meta.get_item('XMP:Irradiance'))
# With Solar elements & Spectral Irradiance
# Now we can correct the raw sun sensor irradiance value (DLS)
# and compute the irradiance on level ground
dls_irradiances = []
fresnel_correction = dls.fresnel(sun_sensor_angle)
dir_dif_ratio = 6.0 # Default value from MicaSense
percent_diffuse = 1.0 / dir_dif_ratio
sensor_irradiance = spectral_irradiances / fresnel_correction
untilted_direct_irr = sensor_irradiance / (percent_diffuse +
np.cos(sun_sensor_angle))
def meta_bad_exposure():
image_path = os.path.join('data', '0001SET', '000')
return metadata.Metadata(os.path.join(image_path, 'IMG_0003_1.tif'))
def meta_altum_dls2(altum_flight_image_name):
return metadata.Metadata(altum_flight_image_name)
import micasense.plotutils as plotutils
import micasense.utils as msutils
import micasense.metadata as metadata
sys.path.append('C:/Users/Isaac Miller/Documents/GitHub/imageprocessing')
exiftoolPath = None
if os.name == 'nt':
exiftoolPath = 'C:/exiftool/exiftool.exe'
# get calibration
panelPath = os.path.join('.', 'data', '0000SET', '000')
panelName = glob.glob(os.path.join(panelPath, 'IMG_0000_1.tif'))[0]
panelRaw = plt.imread(panelName)
panelMeta = metadata.Metadata(panelName, exiftoolPath=exiftoolPath)
radianceImage, L, V, R = msutils.raw_image_to_radiance(panelMeta, panelRaw)
plotutils.plotwithcolorbar(V, 'Vignette Factor')
plotutils.plotwithcolorbar(R, 'Row Gradient Factor')
plotutils.plotwithcolorbar(V * R, 'Combined Corrections')
plotutils.plotwithcolorbar(L, 'Vignette and row gradient corrected raw values')
plotutils.plotwithcolorbar(radianceImage,
'All factors applied and scaled to radiance')
markedImg = radianceImage.copy()
ulx = 660 # upper left column (x coordinate) of panel area
uly = 490 # upper left row (y coordinate) of panel area
lrx = 840 # lower right column (x coordinate) of panel area
lry = 670 # lower right row (y coordinate) of panel area
cv2.rectangle(markedImg, (ulx, uly), (lrx, lry), (0, 255, 0), 3)
# Our panel calibration by band (from MicaSense for our specific panel)
