Loading autocnet/matcher/naive_template.py +1 −1 Original line number Diff line number Diff line Loading @@ -177,4 +177,4 @@ def pattern_match(template, image, upsampling=8, metric=cv2.TM_CCOEFF_NORMED, er shift_x = (matched_x - ((corrmap.shape[1]-1)/2)) / upsampling shift_y = (matched_y - ((corrmap.shape[0]-1)/2)) / upsampling return shift_x, shift_y , max_corr, corrmap return -shift_x, -shift_y , max_corr, corrmap autocnet/transformation/roi.py +21 −52 Original line number Diff line number Diff line from math import modf, floor from math import modf, floor, ceil import numpy as np from plio.io.io_gdal import GeoDataset import scipy.ndimage as ndimage Loading Loading @@ -35,6 +35,11 @@ class Roi(): bottom_y : int The bottom image coordinate in imge space clip_center : tuple on instantiation, set to (). When clip is called and the clipped_array variable is set, the clip_center is set to the center of the, potentially affine transformed, cliped_array. """ def __init__(self, data, x, y, size_x=200, size_y=200, ndv=None, ndv_threshold=0.5, buffer=5): if not isinstance(data, GeoDataset): Loading @@ -48,15 +53,12 @@ class Roi(): self._ndv_threshold = ndv_threshold self.buffer = buffer self.clipped_array = None self.clip_center = () @property def center(self): return (self.x, self.y) @property def clip_center(self): return (self.size_x + 0.5, self.size_y + 0.5) @property def x(self): return self._whole_x + self._remainder_x Loading Loading @@ -149,7 +151,6 @@ class Roi(): no null pixels (as defined by the no data value (ndv)) are present. """ print(self.ndv) if self.ndv == None: return True return np.isclose(self.ndv,self.array).all() Loading Loading @@ -193,7 +194,7 @@ class Roi(): self.size_x = size_x if size_y: self.size_y = size_y print('BUFFER: ', self.buffer) min_x = self._whole_x - self.size_x - self.buffer min_y = self._whole_y - self.size_y - self.buffer x_read_length = (self.size_x * 2) + 1 + (self.buffer * 2) Loading @@ -203,9 +204,9 @@ class Roi(): if (np.asarray(pixels) < 0).any(): raise IndexError('Image coordinates plus read buffer are outside of the available data. Please select a smaller ROI and/or a smaller read buffer.') # This data is an nd array that is larger than originally requested, because it may be affine warped. # This data is an nd-array that is larger than originally requested, because it may be affine warped. data = self.data.read_array(pixels=pixels, dtype=dtype) print('data shape: ', data.shape) # Now that the whole pixel array has been read, interpolate the array to align pixel edges xi = np.linspace(self._remainder_x, ((self.buffer*2) + self._remainder_x + (self.size_x*2)), Loading @@ -215,64 +216,32 @@ class Roi(): (self.size_y*2+1)+(self.buffer*2)) # the xi, yi are intentionally handed in backward, because the map_coordinates indexes column major # Maybe this operates in place? pixel_locked = ndimage.map_coordinates(data, np.meshgrid(yi, xi, indexing='ij'), mode=mode, order=3) print('pixel locked shape', pixel_locked.shape) #pixel_locked = data if affine: # The cval is being set to the mean of the array, pixel_locked = tf.warp(pixel_locked, affine, #.inverse, order=3, mode='constant', cval=0.1) mode='reflect') array_center = (np.array(pixel_locked.shape)[::-1] - 1) / 2.0 self.original_array_center = (np.array(pixel_locked.shape)[::-1] - 1) / 2.0 # Return order is x,y new_center = affine.inverse(array_center)[0] self.clip_center = affine.inverse(self.original_array_center)[0] # +1 handles non-inclusive end indexing on the nd-array; indexing order is y, x pixel_locked = pixel_locked[floor(new_center[1])-self.size_y:floor(new_center[1])+self.size_y+1, floor(new_center[0])-self.size_x:floor(new_center[0])+self.size_x+1] pixel_locked = pixel_locked[floor(self.clip_center[1])-self.size_y:ceil(self.clip_center[1])+self.size_y, floor(self.clip_center[0])-self.size_x:ceil(self.clip_center[0])+self.size_x] print('post affine pixel locked shape', pixel_locked.shape) self.clipped_array = img_as_float32(pixel_locked) return """if affine: # The cval is being set to the mean of the array, d2 = tf.warp(data, affine,# .inverse, order=3, mode=mode) if self.buffer != 0: pixel_locked = d2[self.buffer:-self.buffer, self.buffer:-self.buffer] return img_as_float32(pixel_locked) return d2 else: return data""" """if affine: # The cval is being set to the mean of the array, pixel_locked = tf.warp(data, affine.inverse, order=3, mode=mode)""" # UL, LR, C and then compute if self.buffer != 0: pixel_locked = pixel_locked[self.buffer:-self.buffer, self.buffer:-self.buffer] # Ohh is buffer doing something here? Yeah, post clip, so we should be safe. # Buffers don't matter - wtf self.clip_center = tuple(np.array(pixel_locked.shape)[::-1] / 2.) self.original_array_center = self.clip_center self.clipped_array = img_as_float32(pixel_locked) Loading
autocnet/matcher/naive_template.py +1 −1 Original line number Diff line number Diff line Loading @@ -177,4 +177,4 @@ def pattern_match(template, image, upsampling=8, metric=cv2.TM_CCOEFF_NORMED, er shift_x = (matched_x - ((corrmap.shape[1]-1)/2)) / upsampling shift_y = (matched_y - ((corrmap.shape[0]-1)/2)) / upsampling return shift_x, shift_y , max_corr, corrmap return -shift_x, -shift_y , max_corr, corrmap
autocnet/transformation/roi.py +21 −52 Original line number Diff line number Diff line from math import modf, floor from math import modf, floor, ceil import numpy as np from plio.io.io_gdal import GeoDataset import scipy.ndimage as ndimage Loading Loading @@ -35,6 +35,11 @@ class Roi(): bottom_y : int The bottom image coordinate in imge space clip_center : tuple on instantiation, set to (). When clip is called and the clipped_array variable is set, the clip_center is set to the center of the, potentially affine transformed, cliped_array. """ def __init__(self, data, x, y, size_x=200, size_y=200, ndv=None, ndv_threshold=0.5, buffer=5): if not isinstance(data, GeoDataset): Loading @@ -48,15 +53,12 @@ class Roi(): self._ndv_threshold = ndv_threshold self.buffer = buffer self.clipped_array = None self.clip_center = () @property def center(self): return (self.x, self.y) @property def clip_center(self): return (self.size_x + 0.5, self.size_y + 0.5) @property def x(self): return self._whole_x + self._remainder_x Loading Loading @@ -149,7 +151,6 @@ class Roi(): no null pixels (as defined by the no data value (ndv)) are present. """ print(self.ndv) if self.ndv == None: return True return np.isclose(self.ndv,self.array).all() Loading Loading @@ -193,7 +194,7 @@ class Roi(): self.size_x = size_x if size_y: self.size_y = size_y print('BUFFER: ', self.buffer) min_x = self._whole_x - self.size_x - self.buffer min_y = self._whole_y - self.size_y - self.buffer x_read_length = (self.size_x * 2) + 1 + (self.buffer * 2) Loading @@ -203,9 +204,9 @@ class Roi(): if (np.asarray(pixels) < 0).any(): raise IndexError('Image coordinates plus read buffer are outside of the available data. Please select a smaller ROI and/or a smaller read buffer.') # This data is an nd array that is larger than originally requested, because it may be affine warped. # This data is an nd-array that is larger than originally requested, because it may be affine warped. data = self.data.read_array(pixels=pixels, dtype=dtype) print('data shape: ', data.shape) # Now that the whole pixel array has been read, interpolate the array to align pixel edges xi = np.linspace(self._remainder_x, ((self.buffer*2) + self._remainder_x + (self.size_x*2)), Loading @@ -215,64 +216,32 @@ class Roi(): (self.size_y*2+1)+(self.buffer*2)) # the xi, yi are intentionally handed in backward, because the map_coordinates indexes column major # Maybe this operates in place? pixel_locked = ndimage.map_coordinates(data, np.meshgrid(yi, xi, indexing='ij'), mode=mode, order=3) print('pixel locked shape', pixel_locked.shape) #pixel_locked = data if affine: # The cval is being set to the mean of the array, pixel_locked = tf.warp(pixel_locked, affine, #.inverse, order=3, mode='constant', cval=0.1) mode='reflect') array_center = (np.array(pixel_locked.shape)[::-1] - 1) / 2.0 self.original_array_center = (np.array(pixel_locked.shape)[::-1] - 1) / 2.0 # Return order is x,y new_center = affine.inverse(array_center)[0] self.clip_center = affine.inverse(self.original_array_center)[0] # +1 handles non-inclusive end indexing on the nd-array; indexing order is y, x pixel_locked = pixel_locked[floor(new_center[1])-self.size_y:floor(new_center[1])+self.size_y+1, floor(new_center[0])-self.size_x:floor(new_center[0])+self.size_x+1] pixel_locked = pixel_locked[floor(self.clip_center[1])-self.size_y:ceil(self.clip_center[1])+self.size_y, floor(self.clip_center[0])-self.size_x:ceil(self.clip_center[0])+self.size_x] print('post affine pixel locked shape', pixel_locked.shape) self.clipped_array = img_as_float32(pixel_locked) return """if affine: # The cval is being set to the mean of the array, d2 = tf.warp(data, affine,# .inverse, order=3, mode=mode) if self.buffer != 0: pixel_locked = d2[self.buffer:-self.buffer, self.buffer:-self.buffer] return img_as_float32(pixel_locked) return d2 else: return data""" """if affine: # The cval is being set to the mean of the array, pixel_locked = tf.warp(data, affine.inverse, order=3, mode=mode)""" # UL, LR, C and then compute if self.buffer != 0: pixel_locked = pixel_locked[self.buffer:-self.buffer, self.buffer:-self.buffer] # Ohh is buffer doing something here? Yeah, post clip, so we should be safe. # Buffers don't matter - wtf self.clip_center = tuple(np.array(pixel_locked.shape)[::-1] / 2.) self.original_array_center = self.clip_center self.clipped_array = img_as_float32(pixel_locked)
