def list_detail():
"""
Preview the picture of cow
:return: image base64
"""
company_id = request.json.get('companyid')
rfid_code = request.json.get('rfidcode')
# verify the existence of parameters
utils.verify_param(abort, logger, error_code=400, company_id=company_id, rfid_code=rfid_code,
method_name="list_detail")
pic_path = os.path.join(config.base_images_path, company_id, rfid_code) + os.sep
pre_files = glob.glob(pic_path + '*.jpg')
pic_num = len(pre_files)
if pic_num > 0:
step = pic_num // 10
if step == 0:
step = 1
# Get images and encode them to base64
pre_items = utils.read_image_to_base64(pre_files[0:pic_num:step])
return jsonify({
"items": pre_items
})
else:
logger.error("Folder {} not exist or No picture".format(pic_path))
abort(404)
def json_serilize(instance):
"""
change the returned data to dict
:param instance: data
:return: message dict
"""
# Get registered images and encode them to base64
pre_files = utils.get_files(
os.path.join(config.base_images_path, instance.company_id, instance.rfid_code) + os.sep, 1)
pre_items = utils.read_image_to_base64(pre_files)
return {
"userid": g.user.userid,
"aid": instance.aid,
"rfid_code": instance.rfid_code,
"age": instance.age,
"company_id": instance.company_id,
"gather_time": str(instance.gather_time),
"health_status": instance.health_status,
"extra_info": instance.extra_info,
"items": pre_items
}
def prospect():
"""
get the predicted results based on message
:return: predicted results and cow information
"""
user_id = g.user.userid
company_id = request.json.get('companyid')
gather_time1 = request.json.get('gathertime')
gather_time = utils.verify_time_param(abort, logger, gather_time1)
rfid_code = request.json.get('rfidcode')
ip = request.json.get('ip')
imei = request.json.get('imei')
image_array = request.json.get('items')
predict_array = []
cid_array = []
# verify the existence of parameters
utils.verify_param(abort, logger, error_code=400, user_id=user_id, company_id=company_id, gather_time=gather_time,
rfid_code=rfid_code, ip=ip, imei=imei, image_array=image_array, method_name="prospect")
for i, item in enumerate(image_array):
# get the base64 str and decode them to image
img_base64 = item.get('cvalue')
img_oriented = item.get('cid')
starter = img_base64.find(',')
img_base64 = img_base64[starter + 1:]
bytes_buffer = BytesIO(base64.b64decode(img_base64))
# get one for pillow and another for
img_pillow = Image.open(bytes_buffer)
img_pillow1 = img_pillow.resize(config.img_size, Image.NEAREST)
img_pillow2 = numpy.array(img_pillow1).astype(numpy.float32)
# img_cv2 = cv2.imdecode(numpy.frombuffer(bytes_buffer.getvalue(), numpy.uint8), cv2.IMREAD_COLOR)
# img_cv2 = cv2.resize(img_cv2, config.img_size)
predict_array.append(img_pillow2)
cid_array.append(img_oriented)
# get the predicted results and returned
result, predict_code = utils.get_predicted_result(predict_array, cid_array)
if rfid_code == predict_code and result >= config.min_predict:
resoult = 1
logger.info(
"From ip {} -> cow rfid_code = {},company_id = {} prediction success,result = {}%".format(ip, rfid_code,
company_id,
result))
else:
resoult = 0
logger.info(
"From ip {} -> cow rfid_code = {},company_id = {} prediction failure,result = {}%".format(ip, rfid_code,
company_id,
result))
# get the previous registered images top3 and encode them to base64
pre_files = utils.get_files(os.path.join(config.base_images_path, company_id, predict_code) + os.sep, 3)
pre_items = utils.read_image_to_base64(pre_files)
return jsonify({
'userid': user_id,
'companyid': company_id,
'resoult': resoult,
'gathertime': str(gather_time),
'percent': "predict_code:{},percent:{}%".format(predict_code, result),
'verinfo': 'test',
'ip': ip,
'imei': imei,
'items': pre_items
})