# -*- coding: utf-8; -*- """ Copyright (c) 2018 Rolf Hempel, rolf6419@gmx.de This file is part of the PlanetarySystemStacker tool (PSS). https://github.com/Rolf-Hempel/PlanetarySystemStacker PSS is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with PSS. If not, see . """ from glob import glob from itertools import chain from time import time import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from math import ceil from numpy import float32, zeros, empty, int32, uint8, uint16, clip, histogram from cv2 import imwrite, moments, threshold, THRESH_BINARY, Laplacian, CV_32F, minMaxLoc from configuration import Configuration from exceptions import WrongOrderingError, NotSupportedError, InternalError, ArgumentError, Error from frames import Frames from miscellaneous import Miscellaneous from rank_frames import RankFrames class AlignFrames(object): """ Based on a list of frames and a (parallel) list of frame quality values, an averaged reference frame is created and all frames are aligned with this frame. The alignment is performed on a small rectangular area where structure is optimal in both x and y directions. """ def __init__(self, frames, rank_frames, configuration, progress_signal=None): """ Initialize the AlignFrames object with info from the objects "frames" and "rank_frames". :param frames: Frames object with all video frames :param rank_frames: RankFrames object with global quality ranks (between 0. and 1., 1. being optimal) for all frames :param configuration: Configuration object with parameters :param progress_signal: Either None (no progress signalling), or a signal with the signature (str, int) with the current activity (str) and the progress in percent (int). """ self.frames = frames self.rank_frames = rank_frames self.shape = frames.shape self.alignment_rect_qualities = None self.alignment_rect_bounds = None self.frame_shifts = None self.intersection_shape = None self.intersection_shape_original = None self.mean_frame = None self.mean_frame_original = None self.quality_loss_percent = None self.cog_mean_frame = None self.configuration = configuration self.progress_signal = progress_signal self.signal_step_size = max(int(self.frames.number / 10), 1) self.x_low_opt = self.x_high_opt = self.y_low_opt = self.y_high_opt = None self.dy = self.dx = None self.y_low_opt = self.y_high_opt = self.x_low_opt = self.x_high_opt = None self.dy_original = self.dx_original = None self.ROI_set = False self.dev_table = empty((2 * self.configuration.align_frames_search_width, 2 * self.configuration.align_frames_search_width), dtype=float32) def compute_alignment_rect(self, scale_factor): """ Using the frame with the highest rank (sharpest image), compute the rectangular patch where structure is best in both x and y directions. The size of the patch is the size of the frame divided by "scale_factor" in both coordinate directions. :param scale_factor: Ratio of the size of the frame and the alignment patch in both coordinate directions :return: A four tuple (y_low, y_high, x_low, x_high) with pixel coordinates of the optimal patch """ dim_y, dim_x = self.shape[0:2] # Compute the extensions of the alignment rectangle in y and x directions. Take into account # a border and the search radius for frame shifts. border_width = self.configuration.align_frames_border_width + \ self.configuration.align_frames_search_width rect_y = int((dim_y - 2 * border_width) / scale_factor) rect_y_2 = int(rect_y / 2) rect_x = int((dim_x - 2 * border_width) / scale_factor) rect_x_2 = int(rect_x / 2) # Initialize lists which for each location store the quality measure and the index bounds. self.alignment_rect_qualities = [] self.alignment_rect_bounds = [] # Compute for all locations in the frame the "quality measure" and store the location # and quality measure in above lists. best_frame_mono_blurred = self.frames.frames_mono_blurred(self.rank_frames.frame_ranks_max_index) x_low = border_width x_high = x_low + rect_x while x_high <= dim_x - border_width: y_low = border_width y_high = y_low + rect_y while y_high <= dim_y - border_width: # new_quality = Miscellaneous.local_contrast( # self.frames_mono_blurred[self.frame_ranks_max_index][y_low:y_high, # x_low:x_high], self.configuration.quality_area_pixel_stride) self.alignment_rect_qualities.append( Miscellaneous.quality_measure_threshold_weighted( best_frame_mono_blurred[y_low:y_high, x_low:x_high], stride=self.configuration.align_frames_rectangle_stride, black_threshold=self.configuration.align_frames_rectangle_black_threshold, min_fraction=self.configuration.align_frames_rectangle_min_fraction)) self.alignment_rect_bounds.append((y_low, y_high, x_low, x_high)) y_low += rect_y_2 y_high += rect_y_2 x_low += rect_x_2 x_high += rect_x_2 try: # Sort lists by quality. arq, arb = zip(*sorted(zip(self.alignment_rect_qualities, self.alignment_rect_bounds), reverse=True)) self.alignment_rect_qualities = list(arq) self.alignment_rect_bounds = list(arb) # Set the optimal coordinates and return them as a tuple. (self.y_low_opt, self.y_high_opt, self.x_low_opt, self.x_high_opt) = \ self.alignment_rect_bounds[0] return self.alignment_rect_bounds[0] except: raise ArgumentError("The frame is too small for alignment parameters chosen") def select_alignment_rect(self, index): """ Select an alignment patch from the list computed in "compute_aligment_rect" to be used for frame aligment. :param index: index of the alignment patch in the list in decreasing quality order. :return: True, if successful. Otherwise return False. """ if index < 0 or index > len(self.alignment_rect_bounds): return False else: self.y_low_opt, self.y_high_opt, self.x_low_opt, self.x_high_opt = \ self.alignment_rect_bounds[index] return True def set_alignment_rect(self, y_low_opt, y_high_opt, x_low_opt, x_high_opt): """ As an alternative to "select_alignment_rect", the rectangular patch can be set explicitly :param y_low_opt: Lower y pixel coordinate of patch :param y_high_opt: Upper y pixel coordinate of patch :param x_low_opt: Lower x pixel coordinate of patch :param x_high_opt: Upper x pixel coordinate of patch :return: - """ self.y_low_opt = y_low_opt self.y_high_opt = y_high_opt self.x_low_opt = x_low_opt self.x_high_opt = x_high_opt # If the user has set the patch manually, set the lists to this single element. self.alignment_rect_qualities = [1.] self.alignment_rect_bounds = [(self.y_low_opt, self.y_high_opt, self.x_low_opt, self.x_high_opt)] def align_frames(self): """ Compute the displacement of all frames relative to the sharpest frame using the alignment rectangle. :return: - """ if self.configuration.align_frames_mode == "Surface": # For "Surface" mode the alignment rectangle has to be selected first. if self.x_low_opt is None: raise WrongOrderingError( "Method 'align_frames' is called before 'select_alignment_rect'") # From the sharpest frame cut out the alignment rectangle. The shifts of all other frames # will be computed relativ to this patch. if self.configuration.align_frames_method == "MultiLevelCorrelation": # MultiLevelCorrelation uses two reference windows with different resolution. Also, # please note that the data type is float32 in this case. reference_frame = self.frames.frames_mono_blurred( self.rank_frames.frame_ranks_max_index).astype(float32) self.reference_window = reference_frame[self.y_low_opt:self.y_high_opt, self.x_low_opt:self.x_high_opt] # For the first phase a box with half the resolution is constructed. self.reference_window_first_phase = self.reference_window[::2, ::2] else: # For all other methods, the reference window is of type int32. reference_frame = self.frames.frames_mono_blurred( self.rank_frames.frame_ranks_max_index).astype(int32) self.reference_window = reference_frame[ self.y_low_opt:self.y_high_opt, self.x_low_opt:self.x_high_opt] self.reference_window_shape = self.reference_window.shape elif self.configuration.align_frames_mode == "Planet": # For "Planetary" mode compute the center of gravity for the reference image. cog_reference_y, cog_reference_x = AlignFrames.center_of_gravity( self.frames.frames_mono_blurred(self.rank_frames.frame_ranks_max_index)) else: raise NotSupportedError( "Frame alignment mode '" + self.configuration.align_frames_mode + "' not supported") # Initialize a list which for each frame contains the shifts in y and x directions. self.frame_shifts = [None] * self.frames.number # Initialize a counter of processed frames for progress bar signalling. It is set to one # because in the loop below the optimal frame is not counted. number_processed = 1 # Loop over all frames. Begin with the sharpest (reference) frame for idx in chain(reversed(range(self.rank_frames.frame_ranks_max_index + 1)), range(self.rank_frames.frame_ranks_max_index, self.frames.number)): if idx == self.rank_frames.frame_ranks_max_index: # For the sharpest frame the displacement is 0 because it is used as the reference. self.frame_shifts[idx] = [0, 0] # Initialize two variables which keep the shift values of the previous step as # the starting point for the next step. This reduces the search radius if frames are # drifting. dy_min_cum = dx_min_cum = 0 # For all other frames: Compute the global shift, using the "blurred" monochrome image. else: # After every "signal_step_size"th frame, send a progress signal to the main GUI. if self.progress_signal is not None and number_processed % self.signal_step_size == 1: self.progress_signal.emit("Align all frames", int(round(10*number_processed / self.frames.number) * 10)) frame = self.frames.frames_mono_blurred(idx) # In Planetary mode the shift of the "center of gravity" of the image is computed. # This algorithm cannot fail. if self.configuration.align_frames_mode == "Planet": cog_frame = AlignFrames.center_of_gravity(frame) self.frame_shifts[idx] = [cog_reference_y - cog_frame[0], cog_reference_x - cog_frame[1]] # In Surface mode various methods can be used to measure the shift from one frame # to the next. The method "Translation" is special: Using phase correlation it is # the only method not based on a local search algorithm. It is treated differently # here because it does not require a re-shifting of the alignment patch. elif self.configuration.align_frames_method == "Translation": # The shift is computed with cross-correlation. Cut out the alignment patch and # compute its translation relative to the reference. frame_window = self.frames.frames_mono_blurred(idx)[ self.y_low_opt:self.y_high_opt, self.x_low_opt:self.x_high_opt] self.frame_shifts[idx] = Miscellaneous.translation(self.reference_window, frame_window, self.reference_window_shape) # Now treat all "Surface" mode cases using local search algorithms. In each case # the result is the shift vector [dy_min, dx_min]. The search can fail (if within # the search radius no optimum is found). If that happens for at least one frame, # an exception is raised. The workflow thread then tries again using another # alignment patch. else: if self.configuration.align_frames_method == "MultiLevelCorrelation": # The shift is computed in two phases: First on a coarse pixel grid, # and then on the original grid in a small neighborhood around the optimum # found in the first phase. shift_y_local_first_phase, shift_x_local_first_phase, \ success_first_phase, shift_y_local_second_phase, \ shift_x_local_second_phase, success_second_phase = \ Miscellaneous.multilevel_correlation( self.reference_window_first_phase, frame, self.configuration.frames_gauss_width, self.reference_window, self.y_low_opt - dy_min_cum, self.y_high_opt - dy_min_cum, self.x_low_opt - dx_min_cum, self.x_high_opt - dx_min_cum, self.configuration.align_frames_search_width, weight_matrix_first_phase=None) success = success_first_phase and success_second_phase if success: [dy_min, dx_min] = [ shift_y_local_first_phase + shift_y_local_second_phase, shift_x_local_first_phase + shift_x_local_second_phase] elif self.configuration.align_frames_method == "RadialSearch": # Spiral out from the shift position of the previous frame and search for the # local optimum. [dy_min, dx_min], dev_r = Miscellaneous.search_local_match( self.reference_window, frame, self.y_low_opt - dy_min_cum, self.y_high_opt - dy_min_cum, self.x_low_opt - dx_min_cum, self.x_high_opt - dx_min_cum, self.configuration.align_frames_search_width, self.configuration.align_frames_sampling_stride, sub_pixel=False) # The search was not successful if a zero shift was reported after more # than two search cycles. success = len(dev_r) <= 2 or dy_min != 0 or dx_min != 0 elif self.configuration.align_frames_method == "SteepestDescent": # Spiral out from the shift position of the previous frame and search for the # local optimum. [dy_min, dx_min], dev_r = Miscellaneous.search_local_match_gradient( self.reference_window, frame, self.y_low_opt - dy_min_cum, self.y_high_opt - dy_min_cum, self.x_low_opt - dx_min_cum, self.x_high_opt - dx_min_cum, self.configuration.align_frames_search_width, self.configuration.align_frames_sampling_stride, self.dev_table) # The search was not successful if a zero shift was reported after more # than two search cycles. success = len(dev_r) <= 2 or dy_min != 0 or dx_min != 0 else: raise NotSupportedError( "Frame alignment method " + configuration.align_frames_method + " not supported") # If the local search was unsuccessful, quit the frame loop with an error. if not success: raise InternalError("frame " + str(idx)) # Update the cumulative shift values to be used as starting point for the # next frame. dy_min_cum += dy_min dx_min_cum += dx_min self.frame_shifts[idx] = [dy_min_cum, dx_min_cum] # If the alignment window gets too close to a frame edge, move it away from # that edge by half the border width. First check if the reference window still # fits into the shifted frame. if self.shape[0] - abs( dy_min_cum) - 2 * self.configuration.align_frames_search_width - \ self.configuration.align_frames_border_width < \ self.reference_window_shape[0] or self.shape[1] - abs( dx_min_cum) - 2 * self.configuration.align_frames_search_width - \ self.configuration.align_frames_border_width < \ self.reference_window_shape[1]: raise ArgumentError("Frame stabilization window does not fit into" " intersection") new_reference_window = False # Start with the lower y edge. while self.y_low_opt - dy_min_cum < \ self.configuration.align_frames_search_width + \ self.configuration.align_frames_border_width / 2: self.y_low_opt += ceil(self.configuration.align_frames_border_width / 2.) self.y_high_opt += ceil(self.configuration.align_frames_border_width / 2.) new_reference_window = True # Now the upper y edge. while self.y_high_opt - dy_min_cum > self.shape[ 0] - self.configuration.align_frames_search_width - \ self.configuration.align_frames_border_width / 2: self.y_low_opt -= ceil(self.configuration.align_frames_border_width / 2.) self.y_high_opt -= ceil(self.configuration.align_frames_border_width / 2.) new_reference_window = True # Now the lower x edge. while self.x_low_opt - dx_min_cum < \ self.configuration.align_frames_search_width + \ self.configuration.align_frames_border_width / 2: self.x_low_opt += ceil(self.configuration.align_frames_border_width / 2.) self.x_high_opt += ceil(self.configuration.align_frames_border_width / 2.) new_reference_window = True # Now the upper x edge. while self.x_high_opt - dx_min_cum > self.shape[ 1] - self.configuration.align_frames_search_width - \ self.configuration.align_frames_border_width / 2: self.x_low_opt -= ceil(self.configuration.align_frames_border_width / 2.) self.x_high_opt -= ceil(self.configuration.align_frames_border_width / 2.) new_reference_window = True # If the window was moved, update the "reference window(s)". if new_reference_window: if self.configuration.align_frames_method == "MultiLevelCorrelation": self.reference_window = reference_frame[self.y_low_opt:self.y_high_opt, self.x_low_opt:self.x_high_opt] # For the first phase a box with half the resolution is constructed. self.reference_window_first_phase = self.reference_window[::2, ::2] else: self.reference_window = reference_frame[self.y_low_opt:self.y_high_opt, self.x_low_opt:self.x_high_opt] # This frame is processed, go to next one. number_processed += 1 if self.progress_signal is not None: self.progress_signal.emit("Align all frames", 100) # Compute the shape of the area contained in all frames in the form [[y_low, y_high], # [x_low, x_high]] self.intersection_shape = [[max(b[0] for b in self.frame_shifts), min(b[0] for b in self.frame_shifts) + self.shape[0]], [max(b[1] for b in self.frame_shifts), min(b[1] for b in self.frame_shifts) + self.shape[1]]] @staticmethod def center_of_gravity(frame): """ Comppute (y, x) pixel coordinates of the center of gravity for a given monochrome frame. Raise an error if the computed cog is outside the frame index bounds. :param frame: Monochrome frame (2D numpy array) :return: Integer pixel coordinates (center_y, center_x) of center of gravity """ # The following is the old algorithm (up to Version 0.8.5). It does not work well for # planets on a bright sky background. # # Convert the grayscale image to binary image, where all pixels # brighter than half the maximum image brightness are set to 1, # and all others are set to 0. # thresh = threshold(frame, frame.max()/2, 1, THRESH_BINARY)[1] # This new code sets the threshold between the minimal brightness (background) and # the maximal brightness (object). The hope is that this way the threshold is far away from # background noise. Also, no binary image is created, but brightness variations are allowed # to influence the center of gravity. This gives brighter parts of the image more weight, # which results in a slightly better precision. minVal, maxVal, minLoc, maxLoc = minMaxLoc(frame) brightness_threshold = int((minVal+maxVal)/2) thresh = clip(frame, brightness_threshold, None)[:,:]-brightness_threshold # Calculate moments of binary image M = moments(thresh) # Calculate coordinates for center of gravity and round pixel # coordinates to the nearest integers. cog_x = round(M["m10"] / M["m00"]) cog_y = round(M["m01"] / M["m00"]) # If the computed center of gravity is outside the frame bounds, raise an error (should be # impossible). if not 0 < cog_y < frame.shape[0] or not 0 < cog_x < frame.shape[1]: raise InternalError( "Center of gravity coordinates [" + str(cog_y) + ", " + str( cog_x) + "] of reference frame are out of bounds") return cog_y, cog_x def average_frame(self, average_frame_number=None, color=False): """ Compute an averaged frame from the best (monochrome) frames. :param average_frame_number: Number of best frames to be averaged. If None, the number is computed from the configuration parameter "align_frames_average_frame_percent" :param color: If True, compute an average of the original (color) images. Otherwise use the monochrome frame versions. :return: The averaged frame """ if self.intersection_shape is None: raise WrongOrderingError("Method 'average_frames' is called before 'align_frames'") # Compute global offsets of current frame relative to intersection frame. Start with # Initializing lists which for each frame give the dy and dx displacements between the # reference frame and current frame. self.dy = [self.intersection_shape[0][0] - self.frame_shifts[idx][0] for idx in range(self.frames.number)] self.dx = [self.intersection_shape[1][0] - self.frame_shifts[idx][1] for idx in range(self.frames.number)] # If the number of frames is not specified explicitly, compute it from configuration. if average_frame_number is not None: self.average_frame_number = average_frame_number else: self.average_frame_number = max( ceil(self.frames.number * self.configuration.align_frames_average_frame_percent / 100.), 1) # If the object is changing fast (Jupiter, Sun), restrict interval from which frames are # taken for computing the reference frame. if self.configuration.align_frames_fast_changing_object: window_size = min( self.average_frame_number * self.configuration.align_frames_best_frames_window_extension, self.frames.number) average_frame_indices, self.quality_loss_percent, self.cog_mean_frame = \ self.rank_frames.find_best_frames(self.average_frame_number, window_size) else: average_frame_indices = self.rank_frames.quality_sorted_indices[:self.average_frame_number] # Create an empty numpy buffer. The first and second dimensions are the y and x # coordinates. For color frames add a third dimension. Add all frames to the buffer. if color: self.mean_frame = zeros([self.intersection_shape[0][1] - self.intersection_shape[0][0], self.intersection_shape[1][1] - self.intersection_shape[1][0], 3], dtype=float32) for frame_index in average_frame_indices: shift = self.frame_shifts[frame_index] self.mean_frame += self.frames.frames(frame_index) \ [self.intersection_shape[0][0] - shift[0]: self.intersection_shape[0][1] - shift[0], self.intersection_shape[1][0] - shift[1]: self.intersection_shape[1][1] - shift[1], :] else: self.mean_frame = zeros([self.intersection_shape[0][1] - self.intersection_shape[0][0], self.intersection_shape[1][1] - self.intersection_shape[1][0]], dtype=float32) for frame_index in average_frame_indices: shift = self.frame_shifts[frame_index] self.mean_frame += self.frames.frames_mono(frame_index) \ [self.intersection_shape[0][0] - shift[0]: self.intersection_shape[0][1] - shift[0], self.intersection_shape[1][0] - shift[1]: self.intersection_shape[1][1] - shift[1]] # Compute the mean frame by dividing by the number of frames, and convert values to 16bit. if self.frames.dt0 == uint8: scaling = 256. / self.average_frame_number elif self.frames.dt0 == uint16: scaling = 1. / self.average_frame_number else: raise NotSupportedError("Attempt to compute the reference frame from images with type" " neither uint8 nor uint16") self.mean_frame = (self.mean_frame*scaling).astype(int32) return self.mean_frame def set_roi(self, y_min, y_max, x_min, x_max): """ Make the stacking region snmaller than the intersection size. Be careful: The pixel indices in this method refer to the shape of the intersection of all frames, i.e., the shape of the full mean frame. In general, the original frames are somewhat larger. If all four index bounds are zero, set the ROI to the full frame. :param y_min: Lower y pixel bound :param y_max: Upper y pixel bound :param x_min: Lower x pixel bound :param x_max: Upper x pixel bound :return: The new averaged frame, restricted to the ROI """ if self.intersection_shape is None: raise WrongOrderingError("Method 'set_roi' is called before 'align_frames'") # On the first call, keep a copy of the full mean frame and original intersection shape. if not self.ROI_set: self.mean_frame_original = self.mean_frame.copy() self.intersection_shape_original = self.intersection_shape.copy() if y_min == 0 and y_max == 0 and x_min == 0 and x_max == 0: y_min = 0 y_max = self.intersection_shape_original[0][1] - \ self.intersection_shape_original[0][0] x_min = 0 x_max = self.intersection_shape_original[1][1] - \ self.intersection_shape_original[1][0] elif y_min < 0 or y_max > self.intersection_shape_original[0][1] - \ self.intersection_shape_original[0][0] or \ x_min < 0 or x_max > self.intersection_shape_original[1][1] - \ self.intersection_shape_original[1][0] or \ y_min >= y_max or x_min >= x_max: raise ArgumentError("Invalid ROI index bounds specified") # Reduce the intersection shape and mean frame to the ROI. self.intersection_shape = [[y_min+self.intersection_shape_original[0][0], y_max+self.intersection_shape_original[0][0]], [x_min+self.intersection_shape_original[1][0], x_max+self.intersection_shape_original[1][0]]] # Re-compute global offsets of current frame relative to reference frame. self.dy = [self.intersection_shape[0][0] - self.frame_shifts[idx][0] for idx in range(self.frames.number)] self.dx = [self.intersection_shape[1][0] - self.frame_shifts[idx][1] for idx in range(self.frames.number)] self.ROI_set = True self.mean_frame = self.mean_frame_original[y_min:y_max, x_min:x_max] return self.mean_frame def reset_roi(self): """ After a ROI has been set, reset the ROI to the full frame. Restore the mean frame and the intersection shape to their original values. If no ROI has been set, do nothing. :return: - """ if self.ROI_set: self.mean_frame = self.mean_frame_original self.intersection_shape = self.intersection_shape_original def write_stabilized_video(self, name, fps, stabilized=True): """ Write out a stabilized videos. For all frames the part common to all frames is extracted and written into a video file. :param name: File name of the video output :param fps: Frames per second of video :param stabilized: if False, switch off image stabilization. Write original frames. :return: - """ # Initialize lists of stabilized frames and index strings. frames_mono_stabilized = [] frame_indices = [] if stabilized: # For each frame: cut out the shifted window with the intersection of all frames. # Append it to the list, and add its index to the list of index strings. for idx in range(self.frames.number): frame_mono = self.frames.frames_mono(idx) frames_mono_stabilized.append(frame_mono[ self.intersection_shape[0][0] - self.frame_shifts[idx][0]: self.intersection_shape[0][1] - self.frame_shifts[idx][0], self.intersection_shape[1][0] - self.frame_shifts[idx][1]: self.intersection_shape[1][1] - self.frame_shifts[idx][1]]) frame_indices.append(str(idx)) Miscellaneous.write_video(name, frames_mono_stabilized, frame_indices, 5) else: # Write the original frames (not stabilized) with index number insertions. frames_mono = [] for idx in range(self.frames.number): frame_indices.append(str(idx)) frames_mono.append(self.frames.frames_mono(idx)) Miscellaneous.write_video(name, frames_mono, frame_indices, 5) if __name__ == "__main__": # Images can either be extracted from a video file or a batch of single photographs. Select # the example for the test run. type = 'video' if type == 'image': names = glob( 'Images/2012*.tif') # names = glob.glob('Images/Moon_Tile-031*ap85_8b.tif') # names = glob.glob('Images/Example-3*.jpg') else: # file = 'Moon_Tile-013_205538_short' # file = 'another_short_video' # file = 'Moon_Tile-024_043939' # file = 'Moon_Tile-013_205538' file = "Venus_06_01" names = 'Videos/' + file + '.avi' print(names) # Get configuration parameters. configuration = Configuration() configuration.initialize_configuration() try: # In creating the Frames object the images are read from the specified file(s). frames = Frames(configuration, names, type=type) print("Number of images read: " + str(frames.number)) print("Image shape: " + str(frames.shape)) except Error as e: print("Error: " + str(e)) exit() # Rank the frames by their overall local contrast. rank_frames = RankFrames(frames, configuration) rank_frames.frame_score() print("Best frame index: " + str(rank_frames.frame_ranks_max_index)) output_file = 'Images/' + file + '.jpg' imwrite(output_file, frames.frames_mono(rank_frames.frame_ranks_max_index)) # Initialize the frame alignment object. align_frames = AlignFrames(frames, rank_frames, configuration) if configuration.align_frames_mode == "Surface": # Select the local rectangular patch in the image where the L gradient is highest in both x # and y direction. The scale factor specifies how much smaller the patch is compared to the # whole image frame. (y_low_opt, y_high_opt, x_low_opt, x_high_opt) = align_frames.compute_alignment_rect( configuration.align_frames_rectangle_scale_factor) # Alternative: Set the alignment rectangle by hand. # (align_frames.x_low_opt, align_frames.x_high_opt, align_frames.y_low_opt, # align_frames.y_high_opt) = (x_low_opt, x_high_opt, y_low_opt, y_high_opt) = ( # 650, 950, 550, 750) print("optimal alignment rectangle, x_low: " + str(x_low_opt) + ", x_high: " + str( x_high_opt) + ", y_low: " + str(y_low_opt) + ", y_high: " + str(y_high_opt)) frame = frames.frames_mono_blurred(rank_frames.frame_ranks_max_index).copy() frame[y_low_opt, x_low_opt:x_high_opt] = frame[y_high_opt - 1, x_low_opt:x_high_opt] = 255 frame[y_low_opt:y_high_opt, x_low_opt] = frame[y_low_opt:y_high_opt, x_high_opt - 1] = 255 plt.imshow(frame, cmap='Greys_r') plt.show() # Align all frames globally relative to the frame with the highest score. try: align_frames.align_frames() except NotSupportedError as e: print("Error: " + e.message) exit() except InternalError as e: print("Warning: " + e.message) print("Frame shifts: " + str(align_frames.frame_shifts)) print("Intersection: " + str(align_frames.intersection_shape)) # Compute the reference frame by averaging the best frames. average = align_frames.average_frame() plt.imshow(average, cmap='Greys_r') plt.show() # Write video with stabilized frames, annotated with their frame indices. stabilized = True if stabilized: name = 'Videos/stabilized_video_with_frame_numbers.avi' else: name = 'Videos/not_stabilized_video_with_frame_numbers.avi' align_frames.write_stabilized_video(name, 5, stabilized=stabilized) # print ("Setting ROI and computing new average frame") # average = align_frames.set_roi(300, 600, 500, 1000) # plt.imshow(average, cmap='Greys_r') # plt.show()