def classify_upload():
try:
# We will save the file to disk for possible data collection.
imagefile = flask.request.files['imagefile']
filename_ = str(datetime.datetime.now()).replace(' ', '_') + \
werkzeug.secure_filename(imagefile.filename)
filename = os.path.join(UPLOAD_FOLDER, filename_)
imagefile.save(filename)
path, extension = os.path.splitext(filename)
if extension == '.png':
im = Image.open(filename)
filename = "%s.jpg" % path
im.save(filename)
logging.info('Saving to %s.', filename)
image = exifutil.open_oriented_im(filename)
except Exception as err:
logging.info('Uploaded image open error: %s', err)
return flask.render_template('classify.html',
has_result=True,
result=(False,
'Cannot open uploaded image.'))
names, time_cost, probs = app.clf.classify_image(filename)
return flask.render_template(
'classify.html',
has_result=True,
result=[True, zip(names, probs),
'%.3f' % time_cost],
imagesrc=embed_image_html(image))
def classify_url():
imageurl = flask.request.args.get('imageurl', '')
localfile = '/tmp/samplefile.png'
try:
bytes = request.urlopen(imageurl).read()
#save to tmp,just for temp use for my classify api.
tmpfile = open(localfile, 'wb')
tmpfile.write(bytes)
tmpfile.close()
if sys.version_info.major == 2:
string_buffer = StringIO.StringIO(bytes)
else:
string_buffer = BytesIO(bytes)
image = exifutil.open_oriented_im(string_buffer)
except Exception as err:
# For any exception we encounter in reading the image, we will just
# not continue.
logging.info('URL Image open error: %s', err)
return flask.render_template('detection.html',
has_result=True,
result=(False,
'Cannot open image from URL.'))
app.logger.info('Image: %s', imageurl)
image_np, time_cost = app.clf.detect_image(localfile)
os.remove(localfile)
return flask.render_template('detection.html',
has_result=True,
result=[True, '%.3f' % time_cost],
imagesrc=embed_image_html(image))
def detection_url():
imageurl = flask.request.args.get('image_url', '')
filename_ = str(datetime.datetime.now()).replace(' ',
'_') + 'samplefile.png'
localfile = os.path.join(UPLOAD_FOLDER, filename_)
try:
bytes = request.urlopen(imageurl).read()
# save to tmp,just for temp use for my classify api.
tmpfile = open(localfile, 'wb')
tmpfile.write(bytes)
tmpfile.close()
if sys.version_info.major == 2:
string_buffer = StringIO.StringIO(bytes)
else:
string_buffer = BytesIO(bytes)
image = exifutil.open_oriented_im(localfile)
except Exception as err:
# For any exception we encounter in reading the image, we will just
# not continue.
logging.info('URL Image open error: %s', err)
return flask.render_template('detection.html',
imagesrc="",
has_result=False,
jsonstr='Cannot open image from URL.')
app.logger.info('Image: %s', imageurl)
# names, time_cost, probs = app.clf.classify_image(localfile)
chn_idx = my_detector.detect_image(localfile)
json_file = "/tmp/" + str(chn_idx) + ".json"
image_shape = image.shape
ids, cls, bboxs, scores, json_str = osd.parse_json_file(
json_file, image_shape, label)
osd.draw_labels(image, ids, cls, bboxs, scores, colors)
os.remove(localfile)
return flask.render_template('detection.html',
imagesrc=embed_image_html(image),
has_result=True,
jsonstr=json_str)
def classify_upload():
try:
# We will save the file to disk for possible data collection.
imagefile = flask.request.files['image_file']
filename_ = str(datetime.datetime.now()).replace(' ', '_') + \
werkzeug.secure_filename(imagefile.filename)
filename = os.path.join(UPLOAD_FOLDER, filename_)
imagefile.save(filename)
path, extension = os.path.splitext(filename)
if extension == '.png':
im = Image.open(filename)
im = im.convert("RGB")
os.remove(filename)
filename = "%s.jpg" % path
im.save(filename)
logging.info('Saving to %s.', filename)
image = exifutil.open_oriented_im(filename)
except Exception as err:
logging.info('Uploaded image open error: %s', err)
return flask.render_template('detection.html',
imagesrc="",
has_result=False,
jsonstr='Cannot open uploaded image.')
# names,time_cost, probs = app.clf.classify_image(filename)
chn_idx = my_detector.detect_image(filename)
json_file = "/tmp/" + str(chn_idx) + ".json"
image_shape = image.shape
ids, cls, bboxs, scores, json_str = osd.parse_json_file(
json_file, image_shape, label)
osd.draw_labels(image, ids, cls, bboxs, scores, colors)
os.remove(filename)
return flask.render_template('detection.html',
imagesrc=embed_image_html(image),
has_result=True,
jsonstr=json_str)
def detection_url():
imageurl = flask.request.args.get('image_url', '')
filename_ = str(datetime.datetime.now()).replace(' ', '_') + 'samplefile.png'
localfile = os.path.join(UPLOAD_FOLDER, filename_)
try:
bytes = request.urlopen(imageurl).read()
# save to tmp,just for temp use for my classify api.
tmpfile = open(localfile, 'wb')
tmpfile.write(bytes)
tmpfile.close();
if sys.version_info.major == 2:
string_buffer = StringIO.StringIO(bytes)
else:
string_buffer = BytesIO(bytes)
image = exifutil.open_oriented_im(localfile)
except Exception as err:
# For any exception we encounter in reading the image, we will just
# not continue.
logging.info('URL Image open error: %s', err)
return flask.render_template(
'detection.html', imagesrc="", has_result=False,
jsonstr='Cannot open image from URL.'
)
app.logger.info('Image: %s', imageurl)
if flask.request.args.get('web_type') == 'detection':
my_detector = detector.Detector()
config_path = "./detection_config.json"
my_detector.build_pipeline_by_JSONFile(config_path)
label = osd.parse_label(config_path)
colors = osd.generate_colors(len(label));
chn_idx = my_detector.detect_image(localfile)
json_file = "/tmp/" + str(chn_idx) + ".json";
image_shape = image.shape
ids, cls, bboxs, scores, json_str = osd.parse_json_file(json_file, image_shape, label)
osd.draw_labels(image, ids, cls, bboxs, scores, colors)
os.remove(localfile)
temp = flask.request.args.get('web_type')
return flask.render_template(
'detection.html',
imagesrc=embed_image_html(image, temp), has_result=True,
jsonstr=json_str
)
elif flask.request.args.get('web_type') == 'dehaze':
my_detector = detector.Detector()
config_path = "./dehaze_config.json"
my_detector.build_pipeline_by_JSONFile(config_path)
cchn_idx = my_detector.detect_image(localfile)
dehaze_path = os.path.abspath(
os.path.dirname(os.path.abspath(__file__)) + "/end.jpg")
dehaze_image = cv.imread(dehaze_path)
de_image = cv.cvtColor(dehaze_image,cv.COLOR_BGR2RGB)
image_shape = image.shape
temp = flask.request.args.get('web_type')
return flask.render_template(
'detection.html', imagesrc=embed_image_html(de_image, temp), has_result=True, jsonstr=""
)
elif flask.request.args.get('web_type') == 'style_transfer':
my_detector = detector.Detector()
config_path = "./style_config.json"
my_detector.build_pipeline_by_JSONFile(config_path)
cchn_idx = my_detector.detect_image(localfile)
style_path = os.path.abspath(
os.path.dirname(os.path.abspath(__file__)) + "/style.jpg")
style_image = cv.imread(style_path)
st_image = cv.cvtColor(style_image,cv.COLOR_BGR2RGB)
image_shape = image.shape
temp = flask.request.args.get('web_type')
return flask.render_template(
'detection.html', imagesrc=embed_image_html(st_image, temp), has_result=True, jsonstr=""
)
elif flask.request.args.get('web_type') == "SuperResolution":
my_detector = detector.Detector()
config_path = "./SuperResolution.json"
my_detector.build_pipeline_by_JSONFile(config_path)
cchn_idx = my_detector.detect_image(localfile)
image_shape = image.shape
result_path = os.path.abspath(os.path.dirname(os.path.abspath(__file__))+ "/output/result.png")
image = exifutil.open_oriented_im(result_path)
os.remove(localfile)
temp = flask.request.args.get('web_type')
return flask.render_template(
'detection.html',
imagesrc=embed_image_html(image,temp), has_result=True,
jsonstr=""
)
def classify_upload():
try:
# We will save the file to disk for possible data collection.
imagefile = flask.request.files['image_file']
filename_ = str(datetime.datetime.now()).replace(' ', '_') + \
werkzeug.secure_filename(imagefile.filename)
filename = os.path.join(UPLOAD_FOLDER, filename_)
imagefile.save(filename)
path, extension = os.path.splitext(filename)
if extension == '.png':
im = Image.open(filename)
im = im.convert("RGB")
os.remove(filename)
filename = "%s.jpg" % path
im.save(filename)
logging.info('Saving to %s.', filename)
image = exifutil.open_oriented_im(filename)
except Exception as err:
logging.info('Uploaded image open error: %s', err)
return flask.render_template(
'detection.html', imagesrc="", has_result=False,
jsonstr='Cannot open uploaded image.'
)
if flask.request.form["web_type"] == 'dehaze':
my_detector = detector.Detector()
config_path = "./dehaze_config.json"
my_detector.build_pipeline_by_JSONFile(config_path)
chn_idx = my_detector.detect_image(filename)
dehaze_path = os.path.abspath(
os.path.dirname(os.path.abspath(__file__)) + "/end.jpg")
dehaze_image = cv.imread(dehaze_path)
de_image = cv.cvtColor(dehaze_image,cv.COLOR_BGR2RGB)
image_shape = image.shape
os.remove(filename)
temp = flask.request.form["web_type"]
return flask.render_template(
'detection.html', imagesrc=embed_image_html(de_image, temp), has_result=True, jsonstr=""
)
elif flask.request.form["web_type"] == 'style_transfer':
my_detector = detector.Detector()
config_path = "./style_config.json"
my_detector.build_pipeline_by_JSONFile(config_path)
chn_idx = my_detector.detect_image(filename)
style_path = os.path.abspath(
os.path.dirname(os.path.abspath(__file__)) + "/style.jpg")
style_image = cv.imread(style_path)
st_image = cv.cvtColor(style_image,cv.COLOR_BGR2RGB)
image_shape = image.shape
os.remove(filename)
temp = flask.request.form["web_type"]
return flask.render_template(
'detection.html', imagesrc=embed_image_html(st_image, temp), has_result=True, jsonstr=""
)
elif flask.request.args.get('web_type') == "SuperResolution":
my_detector = detector.Detector()
config_path = "./SuperResolution.json"
my_detector.build_pipeline_by_JSONFile(config_path)
cchn_idx = my_detector.detect_image(localfile)
image_shape = image.shape
result_path = os.path.abspath(os.path.dirname(os.path.abspath(__file__))+ "/output/result.png")
image = exifutil.open_oriented_im(result_path)
os.remove(filename)
temp = flask.request.form["web_type"]
return flask.render_template(
'detection.html', imagesrc=embed_image_html(image, temp), has_result=True, jsonstr=""
)
elif flask.request.form["web_type"] == 'detection':
my_detector = detector.Detector()
config_path = "./detection_config.json"
my_detector.build_pipeline_by_JSONFile(config_path)
label = osd.parse_label(config_path)
colors = osd.generate_colors(len(label));
chn_idx = my_detector.detect_image(filename)
json_file = "/tmp/" + str(chn_idx) + ".json";
image_shape = image.shape
ids, cls, bboxs, scores, json_str = osd.parse_json_file(json_file, image_shape, label)
osd.draw_labels(image, ids, cls, bboxs, scores, colors)
os.remove(filename)
temp = flask.request.form["web_type"]
return flask.render_template(
'detection.html', imagesrc=embed_image_html(image, temp), has_result=True, jsonstr=json_str
)