kopia lustrzana https://github.com/OpenDroneMap/ODM
Use log, tune down verbose output
Piero Toffanin
rodzic
270da0b757
commit
0ede11b788
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@ -8,6 +8,7 @@ import os
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import collections
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from PIL import Image
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import piexif
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from opendm import log
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from opendm.video.srtparser import SrtFileParser
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from opendm.video.parameters import Parameters
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from opendm.video.checkers import BlackFrameChecker, PercentageBlurChecker, SimilarityChecker, ThresholdBlurChecker
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@ -40,11 +41,11 @@ class Video2Dataset:
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# get file name
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file_name = os.path.basename(input_file)
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print("Processing video: {}".format(input_file))
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log.ODM_INFO("Processing video: {}".format(input_file))
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# get video info
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video_info = get_video_info(input_file)
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print(video_info)
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log.ODM_INFO(video_info)
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if self.parameters.use_srt:
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@ -55,38 +56,38 @@ class Video2Dataset:
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for srt_file in srt_files:
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if os.path.exists(srt_file):
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print("Loading SRT file: {}".format(srt_file))
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log.ODM_INFO("Loading SRT file: {}".format(srt_file))
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try:
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srt_parser = SrtFileParser(srt_file)
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srt_parser.parse()
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break
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except Exception as e:
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print("Error parsing SRT file: {}".format(e))
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log.ODM_INFO("Error parsing SRT file: {}".format(e))
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srt_parser = None
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else:
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srt_parser = None
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if (self.blur_checker is not None and self.blur_checker.NeedPreProcess()):
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print("Preprocessing for blur checker...")
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log.ODM_INFO("Preprocessing for blur checker...")
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self.blur_checker.PreProcess(input_file, self.parameters.start, self.parameters.end, self.parameters.internal_width, self.parameters.internal_height)
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end = time.time()
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print("Preprocessing time: {:.2f}s".format(end - start))
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print("Calculated threshold is {}".format(self.blur_checker.threshold))
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log.ODM_INFO("Preprocessing time: {:.2f}s".format(end - start))
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log.ODM_INFO("Calculated threshold is {}".format(self.blur_checker.threshold))
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if (self.black_checker is not None and self.black_checker.NeedPreProcess()):
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start2 = time.time()
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print("Preprocessing for black checker...")
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log.ODM_INFO("Preprocessing for black checker...")
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self.black_checker.PreProcess(input_file, self.parameters.start, self.parameters.end, self.parameters.internal_width, self.parameters.internal_height)
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end = time.time()
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print("Preprocessing time: {:.2f}s".format(end - start2))
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print("Calculated luminance_range_size is {}".format(self.black_checker.luminance_range_size))
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print("Calculated luminance_minimum_value is {}".format(self.black_checker.luminance_minimum_value))
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print("Calculated absolute_threshold is {}".format(self.black_checker.absolute_threshold))
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log.ODM_INFO("Preprocessing time: {:.2f}s".format(end - start2))
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log.ODM_INFO("Calculated luminance_range_size is {}".format(self.black_checker.luminance_range_size))
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log.ODM_INFO("Calculated luminance_minimum_value is {}".format(self.black_checker.luminance_minimum_value))
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log.ODM_INFO("Calculated absolute_threshold is {}".format(self.black_checker.absolute_threshold))
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# open video file
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cap = cv2.VideoCapture(input_file)
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if (not cap.isOpened()):
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print("Error opening video stream or file")
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log.ODM_INFO("Error opening video stream or file")
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return
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if (self.parameters.start is not None):
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@ -99,7 +100,7 @@ class Video2Dataset:
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frames_to_process = self.parameters.end - start_frame + 1 if (self.parameters.end is not None) else video_info.total_frames - start_frame
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output_file_paths = []
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progress = 0
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while (cap.isOpened()):
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ret, frame = cap.read()
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@ -110,8 +111,10 @@ class Video2Dataset:
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break
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# Calculate progress percentage
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prev_progress = progress
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progress = floor((self.frame_index - start_frame + 1) / frames_to_process * 100)
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print("[{}][{:3d}%] Processing frame {}/{}: ".format(file_name, progress, self.frame_index - start_frame + 1, frames_to_process), end="")
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if progress != prev_progress:
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print("[{}][{:3d}%] Processing frame {}/{}: ".format(file_name, progress, self.frame_index - start_frame + 1, frames_to_process), end="\r")
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stats = self.ProcessFrame(frame, video_info, srt_parser)
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@ -128,11 +131,12 @@ class Video2Dataset:
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self.f.close()
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if self.parameters.limit is not None and self.global_idx >= self.parameters.limit:
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print("Limit of {} frames reached, trimming dataset".format(self.parameters.limit))
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limit_files(output_file_paths, self.parameters.limit)
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log.ODM_INFO("Limit of {} frames reached, trimming dataset".format(self.parameters.limit))
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output_file_paths = limit_files(output_file_paths, self.parameters.limit)
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end = time.time()
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print("Total processing time: {:.2f}s".format(end - start))
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log.ODM_INFO("Total processing time: {:.2f}s".format(end - start))
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return output_file_paths
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def ProcessFrame(self, frame, video_info, srt_parser):
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@ -148,7 +152,7 @@ class Video2Dataset:
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res["is_blurry"] = is_blurry
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if is_blurry:
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print ("blurry, skipping")
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# print ("blurry, skipping")
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self.frame_index += 1
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return res
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@ -157,7 +161,7 @@ class Video2Dataset:
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res["is_black"] = is_black
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if is_black:
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print ("black, skipping")
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# print ("black, skipping")
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self.frame_index += 1
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return res
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@ -168,7 +172,7 @@ class Video2Dataset:
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res["last_frame_index"] = last_frame_index
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if is_similar:
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print ("similar to {}, skipping".format(self.similarity_checker.last_image_id))
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# print ("similar to {}, skipping".format(self.similarity_checker.last_image_id))
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self.frame_index += 1
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return res
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@ -178,8 +182,6 @@ class Video2Dataset:
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self.frame_index += 1
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self.global_idx += 1
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print ("saved")
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return res
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def SaveFrame(self, frame, video_info, srt_parser: SrtFileParser):
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@ -279,7 +281,7 @@ def limit_files(paths, limit):
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num_to_delete = cnt - limit
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if num_to_delete <= 0:
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return
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return paths
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skip = floor(num_to_delete / limit) if num_to_delete > cnt else ceil(cnt / num_to_delete)
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@ -291,7 +293,7 @@ def limit_files(paths, limit):
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else:
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to_keep.append(paths[i])
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limit_files(to_keep, limit)
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return limit_files(to_keep, limit)
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def to_deg(value, loc):
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"""convert decimal coordinates into degrees, munutes and seconds tuple
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