kopia lustrzana https://github.com/OpenDroneMap/ODM
644 wiersze
23 KiB
Python
644 wiersze
23 KiB
Python
import math
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import re
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import cv2
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import os
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from opendm import dls
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import numpy as np
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from opendm import log
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from opendm.concurrency import parallel_map
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from opensfm.io import imread
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from skimage import exposure
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from skimage.morphology import disk
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from skimage.filters import rank, gaussian
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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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"""
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Convert Digital Number values to Radiance values
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:param photo ODM_Photo
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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("float32")
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if len(image.shape) != 3:
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raise ValueError("Image should have shape length of 3 (got: %s)" % len(image.shape))
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# Thermal (this should never happen, but just in case..)
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if photo.is_thermal():
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return image
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# All others
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a1, a2, a3 = photo.get_radiometric_calibration()
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dark_level = photo.get_dark_level()
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exposure_time = photo.exposure_time
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gain = photo.get_gain()
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gain_adjustment = photo.gain_adjustment
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V, x, y = vignette_map(photo)
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if x is None:
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x, y = np.meshgrid(np.arange(photo.width), np.arange(photo.height))
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if dark_level is not None:
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image -= dark_level
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# Normalize DN to 0 - 1.0
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bit_depth_max = photo.get_bit_depth_max()
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if bit_depth_max:
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image /= bit_depth_max
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else:
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log.ODM_WARNING("Cannot normalize DN for %s, bit depth is missing" % photo.filename)
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if V is not None:
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# vignette correction
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V = np.repeat(V[:, :, np.newaxis], image.shape[2], axis=2)
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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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R = np.repeat(R[:, :, np.newaxis], image.shape[2], axis=2)
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image *= R
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# Floor any negative radiances to zero (can happen 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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if a1 is not None:
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# multiply with the radiometric calibration coefficient
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image *= a1
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if gain_adjustment is not None:
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image *= gain_adjustment
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return image
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def vignette_map(photo):
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x_vc, y_vc = photo.get_vignetting_center()
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polynomial = photo.get_vignetting_polynomial()
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if x_vc and polynomial:
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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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# perform vignette correction
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# get coordinate grid across image
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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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# compute matrix of distances from image center
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r = np.hypot((x - x_vc), (y - y_vc))
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# compute the vignette polynomial for each distance - we divide by the polynomial so that the
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# corrected image is image_corrected = image_original * vignetteCorrection
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vignette = np.polyval(vignette_poly, r)
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# DJI is special apparently
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if photo.camera_make != "DJI":
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vignette = 1.0 / vignette
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return vignette, x, y
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return None, None, None
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def dn_to_reflectance(photo, image, use_sun_sensor=True):
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radiance = dn_to_radiance(photo, image)
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irradiance = compute_irradiance(photo, use_sun_sensor=use_sun_sensor)
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reflectance = radiance * math.pi / irradiance
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reflectance[reflectance < 0.0] = 0.0
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reflectance[reflectance > 1.0] = 1.0
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return reflectance
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def compute_irradiance(photo, use_sun_sensor=True):
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# Thermal (this should never happen, but just in case..)
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if photo.is_thermal():
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return 1.0
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# Some cameras (Micasense, DJI) store the value (nice! just return)
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hirradiance = photo.get_horizontal_irradiance()
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if hirradiance is not None:
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return hirradiance
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# TODO: support for calibration panels
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if use_sun_sensor and photo.get_sun_sensor():
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# Estimate it
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dls_orientation_vector = np.array([0,0,-1])
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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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photo.get_dls_pose(),
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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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# TODO: support for direct and scattered irradiance
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direct_to_diffuse_ratio = 6.0 # Assumption, clear skies
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spectral_irradiance = photo.get_sun_sensor()
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percent_diffuse = 1.0 / direct_to_diffuse_ratio
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sensor_irradiance = spectral_irradiance / angular_correction
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# Find direct irradiance in the plane normal to the sun
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untilted_direct_irr = sensor_irradiance / (percent_diffuse + np.cos(sun_sensor_angle))
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direct_irradiance = untilted_direct_irr
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scattered_irradiance = untilted_direct_irr * percent_diffuse
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# compute irradiance on the ground using the solar altitude angle
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horizontal_irradiance = direct_irradiance * np.sin(solar_elevation) + scattered_irradiance
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return horizontal_irradiance
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elif use_sun_sensor:
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log.ODM_WARNING("No sun sensor values found for %s" % photo.filename)
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return 1.0
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def get_photos_by_band(multi_camera, user_band_name):
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band_name = get_primary_band_name(multi_camera, user_band_name)
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for band in multi_camera:
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if band['name'] == band_name:
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return band['photos']
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def get_primary_band_name(multi_camera, user_band_name):
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if len(multi_camera) < 1:
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raise Exception("Invalid multi_camera list")
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# Pick RGB, or Green, or Blue, in this order, if available, otherwise first band
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if user_band_name == "auto":
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for aliases in [['rgb', 'redgreenblue'], ['green', 'g'], ['blue', 'b']]:
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for band in multi_camera:
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if band['name'].lower() in aliases:
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return band['name']
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return multi_camera[0]['name']
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for band in multi_camera:
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if band['name'].lower() == user_band_name.lower():
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return band['name']
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band_name_fallback = multi_camera[0]['name']
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log.ODM_WARNING("Cannot find band name \"%s\", will use \"%s\" instead" % (user_band_name, band_name_fallback))
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return band_name_fallback
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def compute_band_maps(multi_camera, primary_band):
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"""
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Computes maps of:
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- { photo filename --> associated primary band photo } (s2p)
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- { primary band filename --> list of associated secondary band photos } (p2s)
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by looking at capture UUID, capture time or filenames as a fallback
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"""
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band_name = get_primary_band_name(multi_camera, primary_band)
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primary_band_photos = None
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for band in multi_camera:
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if band['name'] == band_name:
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primary_band_photos = band['photos']
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break
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# Try using capture time as the grouping factor
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try:
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unique_id_map = {}
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s2p = {}
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p2s = {}
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for p in primary_band_photos:
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uuid = p.get_capture_id()
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if uuid is None:
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raise Exception("Cannot use capture time (no information in %s)" % p.filename)
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# Should be unique across primary band
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if unique_id_map.get(uuid) is not None:
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raise Exception("Unreliable UUID/capture time detected (duplicate)")
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unique_id_map[uuid] = p
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for band in multi_camera:
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photos = band['photos']
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for p in photos:
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uuid = p.get_capture_id()
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if uuid is None:
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raise Exception("Cannot use UUID/capture time (no information in %s)" % p.filename)
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# Should match the primary band
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if unique_id_map.get(uuid) is None:
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raise Exception("Unreliable UUID/capture time detected (no primary band match)")
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s2p[p.filename] = unique_id_map[uuid]
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if band['name'] != band_name:
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p2s.setdefault(unique_id_map[uuid].filename, []).append(p)
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return s2p, p2s
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except Exception as e:
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# Fallback on filename conventions
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log.ODM_WARNING("%s, will use filenames instead" % str(e))
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filename_map = {}
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s2p = {}
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p2s = {}
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file_regex = re.compile(r"^(.+)[-_]\w+(\.[A-Za-z]{3,4})$")
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for p in primary_band_photos:
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filename_without_band = re.sub(file_regex, "\\1\\2", p.filename)
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# Quick check
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if filename_without_band == p.filename:
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raise Exception("Cannot match bands by filename on %s, make sure to name your files [filename]_band[.ext] uniformly." % p.filename)
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filename_map[filename_without_band] = p
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for band in multi_camera:
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photos = band['photos']
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for p in photos:
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filename_without_band = re.sub(file_regex, "\\1\\2", p.filename)
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# Quick check
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if filename_without_band == p.filename:
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raise Exception("Cannot match bands by filename on %s, make sure to name your files [filename]_band[.ext] uniformly." % p.filename)
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s2p[p.filename] = filename_map[filename_without_band]
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if band['name'] != band_name:
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p2s.setdefault(filename_map[filename_without_band].filename, []).append(p)
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return s2p, p2s
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def compute_alignment_matrices(multi_camera, primary_band_name, images_path, s2p, p2s, max_concurrency=1, max_samples=30):
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log.ODM_INFO("Computing band alignment")
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alignment_info = {}
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# For each secondary band
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for band in multi_camera:
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if band['name'] != primary_band_name:
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matrices = []
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def parallel_compute_homography(p):
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try:
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if len(matrices) >= max_samples:
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# log.ODM_INFO("Got enough samples for %s (%s)" % (band['name'], max_samples))
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return
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# Find good matrix candidates for alignment
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primary_band_photo = s2p.get(p['filename'])
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if primary_band_photo is None:
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log.ODM_WARNING("Cannot find primary band photo for %s" % p['filename'])
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return
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warp_matrix, dimension, algo = compute_homography(os.path.join(images_path, p['filename']),
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os.path.join(images_path, primary_band_photo.filename))
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if warp_matrix is not None:
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log.ODM_INFO("%s --> %s good match" % (p['filename'], primary_band_photo.filename))
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matrices.append({
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'warp_matrix': warp_matrix,
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'eigvals': np.linalg.eigvals(warp_matrix),
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'dimension': dimension,
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'algo': algo
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})
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else:
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log.ODM_INFO("%s --> %s cannot be matched" % (p['filename'], primary_band_photo.filename))
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except Exception as e:
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log.ODM_WARNING("Failed to compute homography for %s: %s" % (p['filename'], str(e)))
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parallel_map(parallel_compute_homography, [{'filename': p.filename} for p in band['photos']], max_concurrency, single_thread_fallback=False)
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# Find the matrix that has the most common eigvals
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# among all matrices. That should be the "best" alignment.
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for m1 in matrices:
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acc = np.array([0.0,0.0,0.0])
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e = m1['eigvals']
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for m2 in matrices:
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acc += abs(e - m2['eigvals'])
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m1['score'] = acc.sum()
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# Sort
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matrices.sort(key=lambda x: x['score'], reverse=False)
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if len(matrices) > 0:
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alignment_info[band['name']] = matrices[0]
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log.ODM_INFO("%s band will be aligned using warp matrix %s (score: %s)" % (band['name'], matrices[0]['warp_matrix'], matrices[0]['score']))
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else:
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log.ODM_WARNING("Cannot find alignment matrix for band %s, The band might end up misaligned!" % band['name'])
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return alignment_info
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def compute_homography(image_filename, align_image_filename):
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try:
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# Convert images to grayscale if needed
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image = imread(image_filename, unchanged=True, anydepth=True)
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if image.shape[2] == 3:
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image_gray = to_8bit(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY))
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else:
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image_gray = to_8bit(image[:,:,0])
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max_dim = max(image_gray.shape)
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if max_dim <= 320:
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log.ODM_WARNING("Small image for band alignment (%sx%s), this might be tough to compute." % (image_gray.shape[1], image_gray.shape[0]))
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align_image = imread(align_image_filename, unchanged=True, anydepth=True)
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if align_image.shape[2] == 3:
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align_image_gray = to_8bit(cv2.cvtColor(align_image, cv2.COLOR_BGR2GRAY))
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else:
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align_image_gray = to_8bit(align_image[:,:,0])
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def compute_using(algorithm):
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try:
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h = algorithm(image_gray, align_image_gray)
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except Exception as e:
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log.ODM_WARNING("Cannot compute homography: %s" % str(e))
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return None, (None, None)
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if h is None:
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return None, (None, None)
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det = np.linalg.det(h)
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# Check #1 homography's determinant will not be close to zero
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if abs(det) < 0.25:
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return None, (None, None)
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# Check #2 the ratio of the first-to-last singular value is sane (not too high)
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svd = np.linalg.svd(h, compute_uv=False)
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if svd[-1] == 0:
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return None, (None, None)
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ratio = svd[0] / svd[-1]
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if ratio > 100000:
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return None, (None, None)
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return h, (align_image_gray.shape[1], align_image_gray.shape[0])
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warp_matrix = None
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dimension = None
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algo = None
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if max_dim > 320:
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algo = 'feat'
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result = compute_using(find_features_homography)
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if result[0] is None:
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algo = 'ecc'
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log.ODM_INFO("Can't use features matching, will use ECC (this might take a bit)")
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result = compute_using(find_ecc_homography)
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if result[0] is None:
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algo = None
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else: # ECC only for low resolution images
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algo = 'ecc'
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log.ODM_INFO("Using ECC (this might take a bit)")
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result = compute_using(find_ecc_homography)
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if result[0] is None:
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algo = None
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warp_matrix, dimension = result
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return warp_matrix, dimension, algo
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except Exception as e:
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log.ODM_WARNING("Compute homography: %s" % str(e))
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return None, (None, None), None
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def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000, termination_eps=1e-8, start_eps=1e-4):
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pyramid_levels = 0
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h,w = image_gray.shape
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max_dim = max(h, w)
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downscale = 0
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max_size = 2048
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while max_dim / (2**downscale) > max_size:
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downscale += 1
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if downscale > 0:
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f = 1 / (2**downscale)
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image_gray = cv2.resize(image_gray, None, fx=f, fy=f, interpolation=cv2.INTER_AREA)
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h,w = image_gray.shape
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min_dim = min(h, w)
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while min_dim > 300:
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min_dim /= 2.0
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pyramid_levels += 1
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log.ODM_INFO("Pyramid levels: %s" % pyramid_levels)
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# Quick check on size
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if align_image_gray.shape[0] != image_gray.shape[0]:
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align_image_gray = to_8bit(align_image_gray)
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image_gray = to_8bit(image_gray)
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fx = image_gray.shape[1]/align_image_gray.shape[1]
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fy = image_gray.shape[0]/align_image_gray.shape[0]
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align_image_gray = cv2.resize(align_image_gray, None,
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fx=fx,
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fy=fy,
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interpolation=(cv2.INTER_AREA if (fx < 1.0 and fy < 1.0) else cv2.INTER_LANCZOS4))
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# Build pyramids
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image_gray_pyr = [image_gray]
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align_image_pyr = [align_image_gray]
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for level in range(pyramid_levels):
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image_gray_pyr[0] = to_8bit(image_gray_pyr[0], force_normalize=True)
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image_gray_pyr.insert(0, cv2.resize(image_gray_pyr[0], None, fx=1/2, fy=1/2,
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interpolation=cv2.INTER_AREA))
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align_image_pyr[0] = to_8bit(align_image_pyr[0], force_normalize=True)
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align_image_pyr.insert(0, cv2.resize(align_image_pyr[0], None, fx=1/2, fy=1/2,
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interpolation=cv2.INTER_AREA))
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# Define the motion model, scale the initial warp matrix to smallest level
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warp_matrix = np.eye(3, 3, dtype=np.float32)
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for level in range(pyramid_levels+1):
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ig = gradient(gaussian(image_gray_pyr[level]))
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aig = gradient(gaussian(align_image_pyr[level]))
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if level == pyramid_levels and pyramid_levels == 0:
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eps = termination_eps
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else:
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eps = start_eps - ((start_eps - termination_eps) / (pyramid_levels)) * level
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# Define termination criteria
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criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
|
|
number_of_iterations, eps)
|
|
|
|
try:
|
|
log.ODM_INFO("Computing ECC pyramid level %s" % level)
|
|
_, warp_matrix = cv2.findTransformECC(ig, aig, warp_matrix, cv2.MOTION_HOMOGRAPHY, criteria, inputMask=None, gaussFiltSize=9)
|
|
except Exception as e:
|
|
if level != pyramid_levels:
|
|
log.ODM_INFO("Could not compute ECC warp_matrix at pyramid level %s, resetting matrix" % level)
|
|
warp_matrix = np.eye(3, 3, dtype=np.float32)
|
|
else:
|
|
raise e
|
|
|
|
if level != pyramid_levels:
|
|
warp_matrix = warp_matrix * np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32)
|
|
|
|
if downscale > 0:
|
|
return warp_matrix * (np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32) ** downscale)
|
|
else:
|
|
return warp_matrix
|
|
|
|
|
|
def find_features_homography(image_gray, align_image_gray, feature_retention=0.7, min_match_count=10):
|
|
|
|
# Detect SIFT features and compute descriptors.
|
|
detector = cv2.SIFT_create(edgeThreshold=10, contrastThreshold=0.1)
|
|
|
|
h,w = image_gray.shape
|
|
max_dim = max(h, w)
|
|
downscale = 0
|
|
|
|
max_size = 4096
|
|
while max_dim / (2**downscale) > max_size:
|
|
downscale += 1
|
|
|
|
if downscale > 0:
|
|
f = 1 / (2**downscale)
|
|
image_gray = cv2.resize(image_gray, None, fx=f, fy=f, interpolation=cv2.INTER_AREA)
|
|
h,w = image_gray.shape
|
|
|
|
if align_image_gray.shape[0] != image_gray.shape[0]:
|
|
fx = image_gray.shape[1]/align_image_gray.shape[1]
|
|
fy = image_gray.shape[0]/align_image_gray.shape[0]
|
|
|
|
align_image_gray = cv2.resize(align_image_gray, None,
|
|
fx=fx,
|
|
fy=fy,
|
|
interpolation=(cv2.INTER_AREA if (fx < 1.0 and fy < 1.0) else cv2.INTER_LANCZOS4))
|
|
|
|
kp_image, desc_image = detector.detectAndCompute(image_gray, None)
|
|
kp_align_image, desc_align_image = detector.detectAndCompute(align_image_gray, None)
|
|
|
|
# Match
|
|
FLANN_INDEX_KDTREE = 1
|
|
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
|
|
search_params = dict(checks=50)
|
|
|
|
flann = cv2.FlannBasedMatcher(index_params, search_params)
|
|
try:
|
|
matches = flann.knnMatch(desc_image, desc_align_image, k=2)
|
|
except Exception as e:
|
|
return None
|
|
|
|
# Filter good matches following Lowe's ratio test
|
|
good_matches = []
|
|
for m, n in matches:
|
|
if m.distance < feature_retention * n.distance:
|
|
good_matches.append(m)
|
|
|
|
matches = good_matches
|
|
|
|
if len(matches) < min_match_count:
|
|
return None
|
|
|
|
# Debug
|
|
# imMatches = cv2.drawMatches(im1, kp_image, im2, kp_align_image, matches, None)
|
|
# cv2.imwrite("matches.jpg", imMatches)
|
|
|
|
# Extract location of good matches
|
|
points_image = np.zeros((len(matches), 2), dtype=np.float32)
|
|
points_align_image = np.zeros((len(matches), 2), dtype=np.float32)
|
|
|
|
for i, match in enumerate(matches):
|
|
points_image[i, :] = kp_image[match.queryIdx].pt
|
|
points_align_image[i, :] = kp_align_image[match.trainIdx].pt
|
|
|
|
# Find homography
|
|
h, _ = cv2.findHomography(points_image, points_align_image, cv2.RANSAC)
|
|
if h is None:
|
|
return None
|
|
|
|
if downscale > 0:
|
|
return h * (np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32) ** downscale)
|
|
else:
|
|
return h
|
|
|
|
def gradient(im, ksize=5):
|
|
im = local_normalize(im)
|
|
grad_x = cv2.Sobel(im,cv2.CV_32F,1,0,ksize=ksize)
|
|
grad_y = cv2.Sobel(im,cv2.CV_32F,0,1,ksize=ksize)
|
|
grad = cv2.addWeighted(np.absolute(grad_x), 0.5, np.absolute(grad_y), 0.5, 0)
|
|
return grad
|
|
|
|
def local_normalize(im):
|
|
width, _ = im.shape
|
|
disksize = int(width/5)
|
|
if disksize % 2 == 0:
|
|
disksize = disksize + 1
|
|
selem = disk(disksize)
|
|
im = rank.equalize(im, selem=selem)
|
|
return im
|
|
|
|
|
|
def align_image(image, warp_matrix, dimension):
|
|
image = resize_match(image, dimension)
|
|
|
|
if warp_matrix.shape == (3, 3):
|
|
return cv2.warpPerspective(image, warp_matrix, dimension)
|
|
else:
|
|
return cv2.warpAffine(image, warp_matrix, dimension)
|
|
|
|
|
|
def to_8bit(image, force_normalize=False):
|
|
if not force_normalize and image.dtype == np.uint8:
|
|
return image
|
|
|
|
# Convert to 8bit
|
|
try:
|
|
data_range = np.iinfo(image.dtype)
|
|
min_value = 0
|
|
value_range = float(data_range.max) - float(data_range.min)
|
|
except ValueError:
|
|
# For floats use the actual range of the image values
|
|
min_value = float(image.min())
|
|
value_range = float(image.max()) - min_value
|
|
|
|
image = image.astype(np.float32)
|
|
image -= min_value
|
|
image *= 255.0 / value_range
|
|
np.around(image, out=image)
|
|
image[image > 255] = 255
|
|
image[image < 0] = 0
|
|
image = image.astype(np.uint8)
|
|
|
|
return image
|
|
|
|
|
|
def resize_match(image, dimension):
|
|
h, w = image.shape[0], image.shape[1]
|
|
mw, mh = dimension
|
|
|
|
if w != mw or h != mh:
|
|
fx = mw/w
|
|
fy = mh/h
|
|
image = cv2.resize(image, None,
|
|
fx=fx,
|
|
fy=fx,
|
|
interpolation=(cv2.INTER_AREA if (fx < 1.0 and fy < 1.0) else cv2.INTER_LANCZOS4))
|
|
|
|
return image
|