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v2.0-alpha
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v2.0-alpha
| Author | SHA1 | Date | |
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| 7ead19a651 |
@@ -8,7 +8,7 @@ Designed for contribution to street-level imagery projects like Mapillary or Pan
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__author__ = "Lucas MATHIEU (@campanu)"
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__license__ = "AGPL-3.0-or-later"
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__version__ = "2.0-alpha1"
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__version__ = "2.0-alpha2"
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__maintainer__ = "Lucas MATHIEU (@campanu)"
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__email__ = "campanu@luc-geo.fr"
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@@ -262,7 +262,7 @@ else:
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model = input(locale_toml['ui']['metadatas']['model'])
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author = input(locale_toml['ui']['metadatas']['author'])
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# Getting video metadatas
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# Video metadatas extraction
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print('\n{}'.format(locale_toml['processing']['reading_metadatas']))
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video = cv2.VideoCapture(video_path)
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@@ -279,14 +279,14 @@ video_start_datetime_obj = video_start_datetime_obj + timedelta(seconds=time_off
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video_start_datetime = video_start_datetime_obj.strftime('%Y:%m:%d %H:%M:%S')
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video_start_subsectime = video_start_datetime_obj.strftime('%f')
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# Displaying metadata
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# Metadata recap
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print('\n{}'.format(locale_toml['ui']['info']['metadatas'].format(video_file_name,
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round(video_file_size[0], 3), video_file_size[1],
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video_duration, video_start_datetime,
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int(int(video_start_subsectime) / 1000),
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video_rec_timezone)))
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# Creating output folder
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# Output folder creation
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output_folder = '{}/{}'.format(output_folder, video_file_name)
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existing_path(output_folder)
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@@ -295,22 +295,23 @@ existing_path(output_folder)
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i = 0
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if timelapse == user_agree:
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frame_interval = (1000 * frame_sampling) / video_fps
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frame_interval = frame_sampling / video_fps
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else:
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frame_interval = 1000 * frame_sampling
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frame_interval = frame_sampling
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cv2_tqdm_unit = " {}".format(locale_toml['ui']['units']['cv2_tqdm'])
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cv2_tqdm_range = int(video_duration / frame_interval)
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for i in tqdm(range(video_total_frames - 1), unit=cv2_tqdm_unit):
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t = frame_interval * i
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for i in tqdm(range(cv2_tqdm_range), unit=cv2_tqdm_unit):
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t = frame_interval * i * 1000
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video.set(cv2.CAP_PROP_POS_MSEC, t)
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ret, frame = video.read()
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frame_name = '{:05d}'.format(i)
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image_name = "{}_f{}.jpg".format(video_file_name.split('.')[0], frame_name)
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image_path = "{}/{}".format(output_folder, image_name)
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cv2.imwrite(image_name, frame, [cv2.IMWRITE_JPEG_QUALITY, 88, cv2.IMWRITE_JPEG_PROGRESSIVE, 1,
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cv2.IMWRITE_JPEG_SAMPLING_FACTOR, 0x411111])
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cv2.imwrite(image_path, frame, [cv2.IMWRITE_JPEG_QUALITY, 88, cv2.IMWRITE_JPEG_PROGRESSIVE, 1, cv2.IMWRITE_JPEG_SAMPLING_FACTOR, 0x411111])
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## Time tags preparation
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time_shift = i * frame_sampling
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@@ -318,17 +319,19 @@ for i in tqdm(range(video_total_frames - 1), unit=cv2_tqdm_unit):
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current_datetime = current_datetime_obj.strftime('%Y:%m:%d %H:%M:%S')
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current_subsec_time = int(int(current_datetime_obj.strftime('%f')) / 1000)
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with open(image_name, 'rb') as image_file:
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with open(image_path, 'rb') as image_file:
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image = Image(image_file)
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image.make = make
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image.model = model
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image.author = author
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image.copyright = "{}, {}".format(author, video_start_datetime_obj.strftime('%Y'))
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image.datetime_original = current_datetime
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image.subsec_time_original = current_subsec_time
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image.offset_time_original = video_rec_timezone
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#image.offset_time_original = video_rec_timezone
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with open('{}'.format(image_name), 'wb') as tagged_image_file:
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if current_subsec_time > 0 :
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image.subsec_time_original = str(current_subsec_time)
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with open(image_path, 'wb') as tagged_image_file:
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tagged_image_file.write(image.get_file())
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i += 1
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@@ -336,7 +339,7 @@ for i in tqdm(range(video_total_frames - 1), unit=cv2_tqdm_unit):
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# Geo-tagging (ExifTool)
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print('\n{}'.format(locale_toml['processing']['geotagging']))
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geotagging_cmd = '{} -P -geotag "{}" "-geotime<SubSecDateTimeOriginal" -overwrite_original "{}/{}_f*.jpg"'\
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.format(exiftool_path, gps_track_path, output_folder, video_file_name)
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.format(exiftool_path, gps_track_path, output_folder, video_file_name.split('.')[0])
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geotagging = os.system(geotagging_cmd)
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# End
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