Response = Rekognition_1.detect_text(Image={'Bytes': ObjectBytes})

all_lines = []

# / home / abhay / Downloads / Hyperverge_Aadhar_Front / test.txt

with open("/home/abhay/Downloads/Hyperverge_Aadhar_Front/test.txt", "r") as f:

all_lines = f.read().splitlines()

Response = {}

Detector = cv2.QRCodeDetector() #####detect barcode using detectqrcode

if Detector is not None:

for barcode in decode(image_bytes):

Barcode_decoded_data = barcode.data.decode('utf-8') ####decoding data from detected barcode data

## frontaadharcard of qrcode name and all data are available

Response_Barcode_data = {}

if "name" in Barcode_decoded_data:

Xml_to_orderdict_data = xmltodict.parse(Barcode_decoded_data,

encoding='utf-8') ###convert xml data to orderdict

Orderdict_to_regDict = dict(OrderedDict(Xml_to_orderdict_data)) ## convert orderdict to regular dict

######parse all data into an array#####

for Insert_data in Orderdict_to_regDict.values():

Response_Barcode_data['aadhar_number'] = Insert_data['@uid']

Response_Barcode_data['full_name'] = Insert_data['@name']

Response_Barcode_data['birth_of_year'] = Insert_data['@yob']

# Response_Barcode_data['gender']= Insert_data['@gender']

Response_Barcode_data['co '] = Insert_data['@co']

Response_Barcode_data['city'] = Insert_data['@vtc']

Response_Barcode_data['state'] = Insert_data['@state']

Response_Barcode_data['district'] = Insert_data['@dist']

Response_Barcode_data['pincode'] = Insert_data['@pc']

if Insert_data['@gender'] == 'M':

Response_Barcode_data['gender'] = 'male'

else:

Response_Barcode_data['gender'] = 'female'

Response['barcode_data'] = Response_Barcode_data

return Response

else:

####for backside barcode aadharnumber detection######

if re.match(r"(.*)", Barcode_decoded_data,

re.MULTILINE): ####only aadhar number is available in back barcode aadharcard

barcode_aadhar_front = re.findall(r"(.*)", Barcode_decoded_data, re.MULTILINE)

barcode_aadhar_front = barcode_aadhar_front[0].strip()

Response_Barcode_data['Barcode_aadhar_number'] = barcode_aadhar_front

Response['Barcode_data'] = Response_Barcode_data

return Response

else:

# for imgpath in all_lines:

image = cv2.imread(imgpath)

assert not isinstance(image, type(None)), 'Image not found'

net = cv2.dnn.readNet(

'/home/abhay/Downloads/Model-Object-Detection/Trained_models/Aadhar_Front/yolov3_custom_v1_final.weights',

'/home/abhay/Downloads/Model-Object-Detection/Trained_models/Aadhar_Front/yolov3_custom_v1.cfg')

classes = []

with open("/home/abhay/Downloads/Model-Object-Detection/Trained_models/Aadhar_Front/classes.names", "r") as f:

classes = f.read().splitlines()

boxes = []

confidences = []

class_ids = []

layers_names = net.getLayerNames()

output_layers = [layers_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

result = {}

height, width, channel = image.shape

blob = cv2.dnn.blobFromImage(image, 0.0039, (416, 416), (0, 0, 0), True, crop=False)

net.setInput(blob)

outs = net.forward(output_layers)

for out in outs:

res = {}

temp = []

for detection in out:

scores = detection[5:]

class_id = np.argmax(scores)

confidence = scores[class_id]

if confidence > 0.5:

centre_x = int(detection[0] * width)

centre_y = int(detection[1] * height)

w = int(detection[2] * width)

h = int(detection[3] * height)

x = int(centre_x - w / 2)

y = int(centre_y - h / 2)

try:

crop_img = image[y:y + h, x:x + w]

name_dict = {0:'AadharName', 1:'AadharDateofBirth', 2:'AadharGender',

3:'AadharNumber', 4:'AadharQRCode', 5:'AadharProfile',

6:'AadharFatherName', 7:'AadharSecondaryName', 8:'AadharBarcode',

9:'AadharFatherNameSecondary', 10:'AadharContact'}

imname = imgpath.split('/')

newpath = imname[len(imname) - 1]

directory = '/home/abhay/Downloads/Model-Object-Detection/Trained_models/Aadhar_Front/Output/'

if not os.path.isdir(directory):

os.mkdir(directory)

cv2.imwrite(os.path.join(directory, "cropped_{}_{}.jpg".format(newpath, name_dict[class_id])),

crop_img)

# print("Cropped")

if class_id in [0, 1, 2, 3, 6]:

if not class_id in temp:

success, encoded_image = cv2.imencode('.png', crop_img)

content = encoded_image.tobytes()

# text = Detect_Text_From_Image_Textract(content)

# try:

# blocks=text['Blocks'][1]['Text']

# except:

# blocks = "Not Readable"

text = Detect_Text_From_Image(content)

try:

blocks = text.get('TextDetections')[0].get('DetectedText')

except:

blocks = "Not Readable"

label = name_dict[class_id]

res[label] = blocks

temp.append(class_id)

elif class_id in [4]:

if not class_id in temp:

response = Barcode_Qrcode_detection(image)

label = name_dict[class_id]

res[label] = "Not Readable" if response is None else response

temp.append(class_id)

except:

label = newpath

res[label] = "Not Detected"

# cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

if res:

result.update(res)