def face_rec(fpath, names, known_faces, model='hog'):
unknown_images = get_file_path(fpath)
count = 0
total = len(unknown_images)
for i in unknown_images:
count += 1
try:
im = Image(i)
except:
print('Pass ' + i)
continue
# im_xmp = im.read_xmp()
# if 'Xmp.xmp.Keywords' in im_xmp.keys():
# continue
final_labels = ''
xmp_labels = dict()
xmp_labels.setdefault('Xmp.xmp.Keywords', '')
unknown = face_recognition.load_image_file(i, pixel=1000)
face_loc = face_recognition.face_locations(unknown, model=model)
unknown_face_encoding = face_recognition.face_encodings(
unknown, known_face_locations=face_loc)
for f in unknown_face_encoding:
dis = face_recognition.face_distance(known_faces, f)
min_dis = min(dis)
if min_dis <= 0.5:
idx = dis.tolist().index(min_dis)
label = names[idx] + ','
xmp_labels['Xmp.xmp.Keywords'] = xmp_labels[
'Xmp.xmp.Keywords'] + label
im.modify_xmp(xmp_labels)
final_labels = im.read_xmp()['Xmp.xmp.Keywords']
print(
str(count) + '/' + str(total) + ' ' + i + ' added labels: ' +
str(final_labels))
im.close()
def writ_gps(image_path, coo):
# 转换并写入图像地理范围，存储于Xmp.dc字段下(x1、y1)(x2,y2)下
# {'Xmp.dc.x1': 'E120.25',
# 'Xmp.dc.y1': 'N31.05000000000001',
# 'Xmp.dc.x2': 'E121.29999999999994',
# 'Xmp.dc.y2': 'N30.25'}
if coo[0][0] > 0:
long_location_lef = "E"
else:
long_location_lef = "W"
if coo[0][1] > 0:
lati_location_lef = "N"
else:
lati_location_lef = "S"
if coo[1][0] > 0:
long_location_rig = "E"
else:
long_location_rig = "W"
if coo[1][1] > 0:
lati_location_rig = "N"
else:
lati_location_rig = "S"
longituade_lef = abs(coo[0][0])
latituade_lef = abs(coo[0][1])
longituade_rig = abs(coo[1][0])
latituade_rig = abs(coo[1][1])
point1_x1 = str(long_location_lef) + str(longituade_lef) + "°"
point1_y1 = str(lati_location_lef) + str(latituade_lef) + "°"
point2_x1 = str(long_location_rig) + str(longituade_rig) + "°"
point2_y2 = str(lati_location_rig) + str(latituade_rig) + "°"
img = Image(image_path)
img.modify_xmp({"Xmp.dc.x1": point1_x1})
img.modify_xmp({"Xmp.dc.y1": point1_y1})
img.modify_xmp({"Xmp.dc.x2": point2_x1})
img.modify_xmp({"Xmp.dc.y2": point2_y2})
img.close()
img.read_iptc()
img.read_xmp()
img.read_raw_xmp()
img.clear_exif()
img.read_exif()
img.modify_exif({'Exif.Image.Make': 'test-中文-', 'Exif.Image.Rating': ''})
img.read_exif()
dict1 = {
"Xmp.xmp.CreateDate": "2019-06-23T19:45:17.834",
"Xmp.xmp.Rating": "",
"Xmp.dc.subject": ["tag1", "tag2", "tag3"]
}
img.modify_xmp(dict1)
img.close()
from pyexiv2 import ImageData
with open(r'.\pyexiv2\tests\1.jpg', 'rb') as f:
with ImageData(f.read()) as img:
data = img.read_exif()
with open(r'.\pyexiv2\tests\1.jpg', 'rb+') as f:
with ImageData(f.read()) as img:
changes = {'Iptc.Application2.ObjectName': 'test'}
img.modify_iptc(changes)
f.seek(0)
f.write(img.get_bytes())
indexFile = open(path + "\\index.txt", mode="r", encoding="utf-8")
os.mkdir(path + "\\to_delete\\")
next_date_for_pictures = ''
num_photos_with_date = 0
while True:
fileName = indexFile.readline().strip()
description = indexFile.readline().strip()
dateTaken = indexFile.readline().strip()
if not dateTaken: break # EOF
if description != 'delete':
metadata = Image(path + "\\" + fileName)
xmp = {'Xmp.dc.description': description}
metadata.modify_xmp(xmp)
if dateTaken == next_date_for_pictures:
# add a second to make the next picture think that it comes next
split_date = dateTaken.split(":")
minutes = split_date[3]
split_date[3] = str(int(minutes) + num_photos_with_date).zfill(2)
num_photos_with_date += 1
dateTaken = ":".join(split_date)
else:
num_photos_with_date = 1
next_date_for_pictures = dateTaken
exif = {
'Exif.Image.ImageDescription': description,
'Exif.Photo.DateTimeOriginal': dateTaken
def write_metadata(path, tags):
i = Image(path)
i.modify_xmp({'Xmp.dc.subject': tags})
