def UploadCsv(event, context, ACCESS_KEY, SECRET_KEY, BUCKET_NAME):
try:
content_type_header = event['headers']['content-type']
body = base64.b64decode(event['body'])
username = decoder.MultipartDecoder(
body, content_type_header).parts[1].content.decode('utf-8')
print('username----', username)
csv_file_name = 'dataset.csv'
csv_file = decoder.MultipartDecoder(
body, content_type_header).parts[2].content.decode('utf-8')
csv_file_path = f'/tmp/{csv_file_name}'
print("csv_File------ ", csv_file)
f = open(csv_file_path, "w")
f.write(csv_file)
f.close()
print('created csv file')
df = pd.read_csv(csv_file_path, names=['data', 'categories'])
df.fillna('No value', inplace=True)
codes, index = pd.factorize(df['categories'])
df['labels'] = codes
df = df.drop(['categories'], axis=1)
df.to_csv(csv_file_path, index=False, header=False)
print('created clean csv')
S3_FILE_NAME = f'{username}/{csv_file_name}'
upload_to_s3(csv_file_path, BUCKET_NAME, S3_FILE_NAME, ACCESS_KEY,
SECRET_KEY)
print('uploaded to s3')
os.remove(csv_file_path)
print('removed file')
return tuple(index)
except Exception as e:
print(repr(e))
def face_swap(event, context):
try:
content_type_header = event['headers']['content-type']
# print(event['body'])
body = base64.b64decode(event["body"])
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
picture1 = decoder.MultipartDecoder(body, content_type_header).parts[1]
image_bytes = picture.content
image_bytes1 = picture1.content
print("Start Swapping the faces")
image = Image.open(io.BytesIO(image_bytes))
image1 = Image.open(io.BytesIO(image_bytes1))
print("Image Shape:", image.size)
np_image, np_image1, = np.array(image), np.array(image1)
swapped_image = face_swap_func(np_image, np_image1)
pil_image = Image.fromarray(swapped_image)
res_image = get_response_image(pil_image)
img = get_response_image(image)
img1 = get_response_image(image1)
# res_image = "data:image/jpeg;base64,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"
message = 'here is my message'
response = {
'Status': 'Success',
'message': message,
'img': img,
'img1': img1,
'swapped_image': res_image
}
# img_bytes = image.tobytes()
# bytes_encoded = base64.b64encode(img_bytes)
return {
"statusCode": 200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
# "body": response, "isBase64Encoded":"true"
"body": json.dumps(response)
}
except Exception as e:
traceback.print_exc()
return {
"statusCode": 500,
"headers": {
'Content-Type': 'multipart/form-data',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({"error": repr(e)})
}
def get_images_from_event(event, max_files=1):
try:
pic_details = []
content_type_key = "Content-Type"
if event["headers"].get(content_type_key.lower()):
content_type_key = content_type_key.lower()
content_type_header = event["headers"][content_type_key]
body = base64.b64decode(event["body"])
if type(event["body"]) is str:
event["body"] = bytes(event["body"], "utf-8")
pictures = decoder.MultipartDecoder(body, content_type_header)
for part in pictures.parts:
filename = get_picture_filename(part).replace('"', "")
pic_details.append((part, filename))
logger.info(f"len: {len(pic_details)}")
return pic_details[0:max_files]
except Exception as e:
logger.exception(e)
raise e
def post_cb(self, request, context):
from requests_toolbelt.multipart import decoder
multipart_data = decoder.MultipartDecoder(
request.body, request.headers["content-type"])
part = multipart_data.parts[0]
self.content[self.relpath(request.path)] = part.content
def handler(event, context):
body = event['body-json']
# 람다 생성 확인용 코드
if body == "test":
return {'statusCode': 200, 'body': "함수가 정상적으로 배포 되었습니다."}
body = base64.b64decode(body)
boundary = body.split(b'\r\n')[0]
boundary = boundary.decode('utf-8')
content_type = f"multipart/form-data; boundary={boundary}"
multipart_data = decoder.MultipartDecoder(body, content_type)
img = multipart_to_input(multipart_data)
result = inference_model(img)
return {
'statusCode':
200,
'body':
json.dumps(
f"{result[0][1]}&{result[0][2]}&{result[1][1]}&{result[1][2]}&{result[2][1]}&{result[2][2]}&{result[3][1]}&{result[3][2]}&{result[4][1]}&{result[4][2]}"
)
}
def get_multipart_filename(self, content_type, request):
"""Return filename for given multipart request."""
body = request.body
multipart_data = decoder.MultipartDecoder(body, content_type)
multipart_dict = {}
filename_array = [self.filename, request.method]
for part in multipart_data.parts:
content_disposition = part.headers.get(
'Content-Disposition'.encode(),
None
)
decoded_cd = content_disposition.decode("utf-8")
multipart_name = self.get_multipart_name(decoded_cd)
multipart_dict[multipart_name] = part.text.replace(' ', '-')
ordered_dict = collections.OrderedDict(
sorted(multipart_dict.items())
)
for key, value in ordered_dict.items():
filename_array.append(key + '=' + value)
filename = '--'.join(filename_array)
return filename
def classify_image(event, context):
try:
content_type_header = event['headers']['content-type']
print(event['body'])
body = base64.b64decode(event['body'])
print('BODY LOADED')
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
prediction = get_prediction(image_bytes=picture.content)
print("The prediction is {}".format(prediction))
#filename = picture.headers[b'Content-Disposition'].decoder().split(';')[2].split('=')[1]
return {
"statusCode": 200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True
},
"body": json.dumps({"predicted": prediction})
}
except Exception as e:
print(repr(e))
return {
"statusCode": 500,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True
},
"body": json.dumps({"predicted": prediction})
}
def parse_multipart_req(body, content_type):
"""
Parse multipart-encoded form data
"""
req = {}
# convert to bytes if needed
if isinstance(body, str):
body = bytes(body, "utf-8")
multipart_data = decoder.MultipartDecoder(body, content_type)
for part in multipart_data.parts:
content_disposition = part.headers.get(b"Content-Disposition", b"")
content_disposition = content_disposition.decode("utf-8")
search_field = PATTERN.search(content_disposition)
if search_field:
if search_field.group(0) == "image":
img_io = io.BytesIO(part.content)
img_io.seek(0)
req["image"] = img_io
elif search_field.group(0) == "url":
url = part.content.decode("utf-8")
img_io = io.BytesIO(urlopen(url).read())
req["url"] = img_io
elif search_field.group(0) == "bbox":
req["bbox"] = json.loads(part.content.decode("utf-8"))
else:
logger.debug("Bad field name in form-data")
return req
def parse_email(content_type, body):
multipart_string = base64.b64decode(body)
parts = decoder.MultipartDecoder(multipart_string, content_type).parts
for part in parts:
if 'name="email"' in str(part.headers[b'Content-Disposition']):
return part.text
return None
def classify_image(event, context):
"""Take input image from API and classify it."""
print("Executing classify_image")
try:
# Get image from the request
print("requesting")
print(event)
content_type_header = event['headers']['Content-Type']
print("content_type_header loaded")
body = base64.b64decode(event['body'])
print('Body loaded')
# Obtain the final picture that will be used by the model
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
print('Picture obtained')
# print("picture",dir(picture))
# Get predictions
prediction = get_prediction(image_bytes=picture.content)
prediction_name = class_name[prediction]
print(f'Prediction: {prediction}\tPrediction Name: {prediction_name}')
return {
'statusCode':
200,
'body':
json.dumps({
'predicted': prediction,
'predicted name': prediction_name
})
}
except Exception as e:
print(repr(e))
return {'statusCode': 500, 'body': json.dumps({'error': repr(e)})}
def main_handler(event, context):
try:
content_type_header = event['headers']['content-type']
print('content_type header: ' + content_type_header)
#print('Event Body: ' + event["body"])
body = base64.b64decode(event["body"])
#print('Image content Loaded')
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
print(f'MultipartDecoder processed')
img = Image.open(io.BytesIO(picture.content))
print(f'Input image data loaded')
lr, hr_restore, hr = get_sample_images(real_image=img,
upscale_factor=2)
print(
f'lr shape: {lr.shape}, hr_restore shape: {hr_restore.shape}, hr shape: {hr.shape}'
)
lrSize = lr.shape[1]
hrSize = hr.shape[1]
lr = lr.unsqueeze(0)
sr = g_model(lr) # create SR image
sr, lr = sr.squeeze(0), lr.squeeze(0)
lr_np = tensor2np(lr)
hr_restore_np = tensor2np(hr_restore)
sr_np = tensor2np(sr)
sr_np_norm = np.uint8(sr_np * 255)
print(f'SR image created')
return {
"statusCode":
200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body":
json.dumps({
'lrSize': lrSize,
'hrSize': hrSize,
'lrImg': img_to_base64(lr_np),
'hrRestoredImg': img_to_base64(hr_restore_np),
'srImg': img_to_base64(sr_np_norm)
})
}
except Exception as e:
print(repr(e))
return {
"statusCode": 500,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({"error": repr(e)})
}
def get_image_from_event(event):
content_type_header = event["headers"]["content-type"]
body = base64.b64decode(event["body"])
logger.info("Body Loaded")
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
filename = get_picture_filename(picture).replace('"', "")
return picture, filename
def lambdaHandler(event, context):
headers = event['headers']
content_type = headers['Content-Type'] if 'Content-Type' in headers else headers['content-type']
# This code can only handle base64 encoded
# Need to work on Non-base64 encoded case
isBase64 = event['isBase64Encoded']
# In the CDK, if the binaryMediaTypes: ['multipart/form-data'] specified on the API level, it is base64 encoded, otherwise, it is not
body = base64.b64decode(event['body'])
form_data = decoder.MultipartDecoder(body, content_type).parts
part = form_data[0]
# print(part.headers)
# print(part.content)
key = f'F-{int(time.time())}.jpg'
client = boto3.client('s3')
def get_presigned_url():
client.put_object(Body = part.content, Bucket = bucket, Key = key)
# ExpiresIn - time in second
return client.generate_presigned_url('get_object', Params = { 'Bucket': bucket, 'Key': key }, ExpiresIn = 60 * 60 * 24)
presigned_url = get_presigned_url()
return {
'statusCode': 200,
'headers': { 'Access-Control-Allow-Origin': '*', 'content-type': 'application/json' },
'body': json.dumps(presigned_url)
}
async def format_files(r, encode=False):
if encode:
pass
else:
decoded = decoder.MultipartDecoder(await r.content, r.mimetype)
dump = {}
for part in decoded.parts:
header = part.headers[b"Content-Disposition"].decode("utf-8")
mimetype = part.headers.get(b"Content-Type", None)
filename = None
for section in [h.strip() for h in header.split(";")]:
split = section.split("=")
if len(split) > 1:
key = split[0]
value = split[1]
value = value[1:-1]
if key == "filename":
filename = value
elif key == "name":
formname = value
if mimetype is None:
dump[formname] = part.content
else:
dump[formname] = {
"filename": filename,
"content": part.content,
"content-type": mimetype.decode("utf-8"),
}
return dump
def WadoRs(self, wado_url: Text, accept_header: Text = None) -> bytes:
"""Performs the request, parses the multipart response, and returns content.
Args:
wado_url: URL for the WADO request.
accept_header: Value of the Accept header to use. If set to None, no
Accept header will be used.
Returns:
The content of the first (and only) part as a string.
Raises:
UnexpectedResponseError: If the response status was not success or the
number of parts in the multipart response is different from 1.
"""
resp, content = self._InvokeHttpRequest(
wado_url,
'GET',
headers={'Accept': accept_header}
if accept_header is not None else {})
if 200 <= resp.status >= 299:
raise UnexpectedResponseError(
'WadoRs error. Response Status: %d,\nURL: %s,\nContent: %s.' %
(resp.status, wado_url, content))
multipart_data = decoder.MultipartDecoder(content,
resp['content-type'])
num_parts = len(multipart_data.parts)
if num_parts != 1:
raise UnexpectedResponseError(
'WadoRs multipart response expected to have a single part.'
' Actual: %d.\nURL: %s' % (num_parts, wado_url))
return multipart_data.parts[0].content
def text_classification(event,context):
try:
print('event-------',event)
content_type_header = event['headers']['content-type']
body = base64.b64decode(event['body'])
print('body-------',body)
print('body loaded')
print(content_type_header)
decoded = decoder.MultipartDecoder(body,content_type_header)
function_usage =( decoded.parts[0].content).decode('utf-8')
if function_usage == 'text_classify_test':
prediction = predict_class(S3_BUCKET,decoded,ACCESS_KEY,SECRET_KEY,min_len=5)
return {
'statusCode': 200,
'headers':{
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True
},
'body': json.dumps({'status': '1', 'result': str(prediction) })
}
except Exception as e:
print(repr(e))
return {
'statusCode': 500,
'headers':{
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True
},
'body': json.dumps({ 'error': repr(e) })
}
def parse_multipart_data(body_dec, content_type):
"""Parser for transforming multipart form data into readable key value pairs
Args:
body_dec (byte),
content_type (str)
Returns:
Parsed Form Data (list): List of key value pairs describing each form data
"""
items = []
for part in decoder.MultipartDecoder(body_dec, content_type).parts:
disposition = part.headers[b'Content-Disposition']
params = {}
for dispPart in str(disposition).split(';'):
kv = dispPart.split('=', 2)
params[str(kv[0]).strip()] = str(kv[1]).strip(
'\"\'\t \r\n') if len(kv) > 1 else str(kv[0]).strip()
type = part.headers[b'Content-Type'].decode(
"utf-8") if b'Content-Type' in part.headers else None
items.append({"content": part.content, "type": type, "params": params})
return items
def classify_image (event, context):
try:
content_type_header = event['headers']['content-type']
print(event['body'])
body = base64.b64decode(event["body"])
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
prediction = get_prediction(image_bytes=picture.content)
print (prediction)
filename = picture.headers[b'Content-Disposition'].decode().split(';')[1].split('=')[1]
if len(filename) < 4:
filename = picture.headers[b'Content-Disposition'].decode().split(';')[2].split('=')[1]
return {
"statusCode": 200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({'file': filename.replace('"', ''), 'predicted': [prediction, classes[prediction] ]})
}
except Exception as e:
print(repr(e))
return {
"statusCode": 500,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({"error": repr(e)})
}
def classifyHandler(event, context):
content_type_header = event['headers']['Content-Type']
body = event['body'].encode()
rawBytes = decoder.MultipartDecoder(body,
content_type_header).parts[0].content
# # TODO: why do rawBytes look off? why are they unable to be opened as image?
# rawIO = BytesIO(rawBytes)
# image = Image.open(rawBytes)
# # image = np.array(image)
# # image = tf.image.convert_image_dtype(image, tf.float32)
# # image = tf.image.resize(image, (224,224))
# # image = np.expand_dims(image, axis=0)
# # prediction = model.predict_classes(image)[0]
# # prediction = class_dict['prediction']
response = {
'statusCode':
200,
'body':
json.dumps({
'sample_bytes': str(rawBytes)[:10000],
'request_body': str(event['body'][:10000])
})
}
return response
def deserialize(self, data, headers, response_type):
"""Deserializes response into an object.
:param response: RESTResponse object to be deserialized.
:param response_type: class literal for
deserialized object, or string of class name.
:return: deserialized object.
"""
# handle file downloading
# save response body into a tmp file and return the instance
if response_type == "file":
return self.__deserialize_file(data, headers)
if response_type == "multipart":
return decoder.MultipartDecoder(data, headers['Content-Type'],
'UTF-8')
# fetch data from response object
if six.PY3:
data = data.decode('utf8')
try:
data = json.loads(data)
except:
pass
return self.__deserialize(data, response_type)
def _postData(cls, mediaType, body):
if mediaType and mediaType.mimeType() == 'multipart/form-data':
try:
multipart_data = decoder.MultipartDecoder(body, str(mediaType))
except ImproperBodyPartContentException:
return {}
params = []
default_type = None
for part in multipart_data.parts:
if 'Content-Disposition' not in part.headers:
continue
if default_type is None:
default_type = part.headers.get('Content-Type')
_, pdict = cgi.parse_header(
part.headers['Content-Disposition'])
p = dict(name=pdict.get('name'),
contentType=part.headers.get('Content-Type',
default_type))
if pdict.get('filename'):
p['fileName'] = pdict.get('filename')
if 'text/' in p['contentType']:
p['value'] = part.content
else:
p['value'] = b64encode(part.content)
params.append(p)
return dict(mimeType=str(mediaType), params=params, comment='')
return {}
def classify_image(event, context):
"""Classify image using api.
Function is called from this template python: handler.py
Args:
event: Dictionary containing API inputs
context: Dictionary
Returns:
dictionary: API response
Raises:
Exception: Returning API repsonse 500
"""
try:
content_type_header = event['headers']['content-type']
# print(event['body'])
body = base64.b64decode(event["body"])
print('BODY LOADED')
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
prediction = get_prediction(image_bytes=picture.content)
print(prediction)
prediction_label = imagenet1000_classidx_to_label(prediction)
filename = (picture.headers[b'Content-Disposition'].decode().split(';')
[1].split('=')[1])
if len(filename) < 4:
filename = (picture.headers[b'Content-Disposition'].decode().split(
';')[2].split('=')[1])
return {
"statusCode":
200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True
},
"body":
json.dumps({
'file': filename.replace('"', ''),
'predicted': f"{prediction}, {prediction_label}"
})
}
except Exception as e:
print(repr(e))
return {
"statusCode": 500,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True
},
"body": json.dumps({"error": repr(e)})
}
def _upload_artifacts_callback(self, request):
# TODO We should authorise the artifacts first
if (
"JOB-TOKEN" not in request.headers
or request.headers["JOB-TOKEN"] != self.token
):
return (403, {}, json.dumps({"message": "403 Forbidden"}))
if self.status != "running":
return (
403,
{},
json.dumps({"message": "403 Forbidden - Job is not running"}),
)
if self.file_data is not None:
return (
400,
{},
json.dumps(
{"message": '400 (Bad request) "Already uploaded" not given'}
),
)
from requests_toolbelt.multipart import decoder
payload = {}
for part in decoder.MultipartDecoder(
request.body, request.headers["Content-Type"]
).parts:
header = (
part.headers[b"Content-Disposition"].decode(part.encoding).split("; ")
)
assert header[0] == "form-data"
header = {h.split("=")[0]: h.split("=")[1][1:-1] for h in header[1:]}
assert header["name"] not in payload
if header["name"] == "file":
assert len(header) == 2
payload[header["name"]] = (header["filename"], part.content)
else:
assert len(header) == 1
payload[header["name"]] = part.text
file_name, file_data = payload["file"]
if "artifact_type" in payload or "artifact_format" in payload:
raise NotImplementedError("Not sure what this does")
if "expire_in" in payload:
pass
# TODO I think this should update the job's underlying job_info object
# https://gitlab.com/gitlab-org/gitlab-ce/blob/78b3eea7d248c6d3c48b615c9df24a95cb5fd1d8/lib/api/runner.rb#L292
self.upload_artifacts(file_name, file_data)
headers = {}
response = self.as_dict()
return (201, headers, json.dumps(response))
def version_4(event):
content_type_header = event['headers']['content-type']
postdata = base64.b64decode(event['body']).decode('iso-8859-1')
lst = []
for part in decoder.MultipartDecoder(postdata.encode('utf-8'),
content_type_header).parts:
lst.append(part.text)
upload_to_s3(lst, "image.jpeg")
def classify_image(event, context):
try:
if (event['httpMethod'] == 'OPTIONS'):
return {
"statusCode": 200,
"headers": {
'Access-control-Allow-origin': '*',
'Access-control-Allow-credentials': True,
'Access-Control-Allow-Methods':
'POST, GET, OPTIONS, DELETE',
'Access-Control-Allow-Headers': '*',
},
"body": json.dumps({'foo': 'bar'})
}
content_type_header = event['headers']['content-type']
#print(event['body'])
body = base64.b64decode(event["body"])
print('BODY LOADED')
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
face_tensor, probs = extract_align_faces(image_bytes=picture.content)
prediction = get_prediction(face_tensor)
print("Get Prediction returned:", prediction, type(prediction),
class_names[int(prediction)])
predicted_val = class_names[int(prediction)]
predicted_str = f'{predicted_val}'
content_disp = picture.headers[b'Content-Disposition'].decode().split(
';')
filename = content_disp[1].split('=')[1]
if len(filename) < 4:
filename = content_disp[2].split('=')[1]
return {
"statusCode":
200,
"headers": {
'Content-Type': 'application/json',
'Access-control-Allow-origin': '*',
'Access-control-Allow-credentials': True
},
"body":
json.dumps({
'file': filename.replace('"', ''),
'predicted': predicted_str,
'class': int(prediction)
})
}
except Exception as e:
print("classify_image Exception", repr(e))
return {
"statusCode": 500,
"headers": {
'Content-Type': 'application/json',
'Access-control-Allow-origin': '*',
"Access-control-Allow-credentials": True
},
"body": json.dumps({"error": repr(e)})
}
def unwrap_multipart_response(response: Response):
"""
:param response:
:return:
"""
content = response.content.decode("utf-8")
boundary = content.split("\r")[0][2:]
content_type = f"multipart/form-data; boundary={boundary}"
return decoder.MultipartDecoder(response.content, content_type)
def face_swap_handler(event, context):
try:
content_type_header = event['headers']['content-type']
body = base64.b64decode(event["body"])
print('content_type header: ' + content_type_header)
print('Event Body: ' + event["body"])
#json_body = json.loads(event["body"])
picture1 = decoder.MultipartDecoder(body, content_type_header).parts[0]
picture2 = decoder.MultipartDecoder(body, content_type_header).parts[1]
im1 = np.frombuffer(picture1.content, dtype=np.uint8)
im2 = np.frombuffer(picture2.content, dtype=np.uint8)
print('Body Loaded')
#picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
#print(f'MultipartDecoder processed')
swapped_face = fsp.face_swap(im1, im2, PREDICTOR_PATH_68)
return {
"statusCode":
200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body":
json.dumps({
'file': 'swap',
'swapface': img_to_base64(swapped_face)
})
}
except Exception as e:
print(repr(e))
return {
"statusCode": 500,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({"error": repr(e)})
}
def face_align(event, context):
try:
content_type_header = event["headers"]["content-type"]
body = base64.b64decode(event["body"])
# if type(event["body"]) is str:
# event["body"] = bytes(event["body"], "utf-8")
pictures = decoder.MultipartDecoder(body, content_type_header)
print(len(pictures.parts))
for picture in pictures.parts:
im_ndarray = cv2.imdecode(np.frombuffer(picture.content, np.uint8), -1)
print("image_decoded")
err, aligned = FD.faceAligner(im_ndarray)
print(aligned)
filename = picture.headers[b'content-Disposition'].decode().split(';')[1].split('=')[1]
if len(filename) < 4:
filename = picture.headers[b'content-Disposition'].decode().split(';')[2].split('=')[1]
fields = {"face-aligned": base64.b64encode(aligned).decode("utf-8")}
return {
"statusCode": 200,
"headers": {
'content-type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps(fields),
}
except ValueError as ve:
return {
"statusCode": 422,
"headers": {
'content-type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({"error": repr(ve)}),
}
except Exception as e:
print(repr(e))
return {
'statusCode': 500,
"headers": {
'content-type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({"error": repr(e)})
}
def classify_image(event, context):
"""Take input image from API and classify it."""
try:
# Get image from the request
if 'Content-Type' in event['headers']:
content_type_header = event['headers']['Content-Type']
else:
content_type_header = event['headers']['content-type']
body = base64.b64decode(event['body'])
print('Body loaded')
# Obtain the final picture that will be used by the model
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
print('Picture obtained')
# Get predictions
prediction = get_prediction(image_bytes=picture.content)
prediction_name = imagenet_class_names[prediction]
print(f'Prediction: {prediction}\tPrediction Name: {prediction_name}')
filename = picture.headers[b'Content-Disposition'].decode().split(
';')[1].split('=')[1]
if len(filename) < 4:
filename = picture.headers[b'Content-Disposition'].decode().split(
';')[2].split('=')[1]
return {
'statusCode':
200,
'headers': {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True
},
'body':
json.dumps({
'file': filename.replace('"', ''),
'predicted': prediction,
'predicted_name': prediction_name,
})
}
except Exception as e:
print(repr(e))
return {
'statusCode': 500,
'headers': {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Credentials': True
},
'body': json.dumps({'error': repr(e)})
}
def face_rec(event,context):
try:
print(event)
print(context)
content_type_header = event['headers']['content-type']
print(event['body'])
body = base64.b64decode(event["body"])
print("BODY LOADED...")
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
prediction = get_prediction(image_bytes = picture.content)
label = labels[str(prediction)]
print(prediction,label)
filename = picture.headers[b'Content-Disposition'].decode().split(';')[1].split('=')[1]
if len(filename) < 4:
filename = picture.headers[b'Content-Disposition'].decode().split(';')[2].split('=')[1]
print('filename: '+filename.replace('"',''))
return {
"statusCode": 200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({'status': 'Predicted' ,'predicted': prediction, 'label': label})
}
except ValueError as v:
print(v)
return {
"statusCode": 200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({'status': str(v) ,'predicted': '', 'label': ''})
}
except Exception as e:
print(repr(e))
return {
"statuscode" : 500,
"headers" : {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin' : '*',
'Access-Control-Allow-Credentials' : True
},
"body": json.dumps({"error": str(repr(e))})
}
