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adfi_api.py
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adfi_api.py
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import base64
import datetime
import json
import os
from io import BytesIO
import cv2
import numpy as np
import requests
from PIL import Image
class AdfiApi:
def __init__(
self, apikey, aimodel_id, model_type, url, ok_dir, not_clear_dir, ng_dir
):
self.apikey = apikey
self.aimodel_id = aimodel_id
self.model_type = model_type
if url[-1] != "/":
self.url = url + "/"
else:
self.url = url
self.height = 800
self.width = 800
current_day = datetime.datetime.now().strftime("%Y%m%d")
if ok_dir[-1] != "/":
self.ok_dir = ok_dir + "/" + current_day + "/"
else:
self.ok_dir = ok_dir + current_day + "/"
if not_clear_dir[-1] != "/":
self.not_clear_dir = not_clear_dir + "/" + current_day + "/"
else:
self.not_clear_dir = not_clear_dir + current_day + "/"
if ng_dir[-1] != "/":
self.ng_dir = ng_dir + "/" + current_day + "/"
else:
self.ng_dir = ng_dir + current_day + "/"
def inspect_image(self, img_filepath, result_image=True):
img = Image.open(img_filepath).convert("RGB")
# Notice: Please make the image size smaller than 800 pix.
MAX_SIZE = 800
self.height = img.height
self.width = img.width
if img.height > MAX_SIZE:
self.height = MAX_SIZE
if img.width > MAX_SIZE:
self.width = MAX_SIZE
img = img.resize((self.width, self.height), Image.ANTIALIAS)
img_bytes = BytesIO()
img.save(img_bytes, format="PNG")
img_bytes = img_bytes.getvalue()
files = {"image_data": (img_filepath, img_bytes, "image/png")}
data = {
"apikey": self.apikey,
"aimodel_id": self.aimodel_id,
"model_type": self.model_type,
"result_image": result_image,
}
# Send a request.
response = requests.post(self.url, files=files, data=data)
# if the request fails.
if response.status_code != 200:
return None, None
result_json = response.json()
result_image_save_path = None
if result_image:
# Convert a string to a result image
img_binary = base64.b64decode(
result_json["result_image_base64_data"].encode("utf-8")
)
img_array = np.frombuffer(img_binary, dtype=np.uint8)
result_image_np = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
result_image_save_path = os.path.basename(img_filepath)
if "Anomaly" in result_json["result"]:
if not os.path.exists(self.ng_dir):
os.makedirs(self.ng_dir)
result_image_save_path = self.ng_dir + result_image_save_path
elif "Not-clear" in result_json["result"]:
if not os.path.exists(self.not_clear_dir):
os.makedirs(self.not_clear_dir)
result_image_save_path = self.not_clear_dir + result_image_save_path
else:
if not os.path.exists(self.ok_dir):
os.makedirs(self.ok_dir)
result_image_save_path = self.ok_dir + result_image_save_path
# Save the result image
result_image_np = cv2.resize(result_image_np, (img.width, img.height))
cv2.imwrite(result_image_save_path, result_image_np)
return result_json, result_image_save_path
class AdfiLocalModelApi:
model = None
info_dict = None
model_path = None
def __init__(self, model_path, ok_dir, not_clear_dir, ng_dir):
if os.path.isfile("./adfi_local/adfi.py"):
self.model_path = model_path
from adfi_local import adfi
self.model = adfi.AI_Model()
self.model.load(self.model_path)
self.info_dict = self.model.get_info()
print("License expiration date: ", self.info_dict["expiration_date"])
current_day = datetime.datetime.now().strftime("%Y%m%d")
if ok_dir[-1] != "/":
self.ok_dir = ok_dir + "/" + current_day + "/"
else:
self.ok_dir = ok_dir + current_day + "/"
if not_clear_dir[-1] != "/":
self.not_clear_dir = not_clear_dir + "/" + current_day + "/"
else:
self.not_clear_dir = not_clear_dir + current_day + "/"
if ng_dir[-1] != "/":
self.ng_dir = ng_dir + "/" + current_day + "/"
else:
self.ng_dir = ng_dir + current_day + "/"
def inspect_image(self, img_filepath, result_image=True):
prediction_dict = self.model.predict(
img_filepath, get_result_image=result_image
)
result_dict = {
"image_name": os.path.basename(img_filepath),
"result": str(prediction_dict["result"]),
"time": str(datetime.datetime.now()),
"anomaly_score": str(prediction_dict["score"]),
"main_prediction_result": str(prediction_dict["main"]),
"sub_prediction_result": str(prediction_dict["sub"]),
}
result_image_save_path = None
if result_image:
result_image_save_path = os.path.basename(img_filepath)
if "Anomaly" in prediction_dict["result"]:
if not os.path.exists(self.ng_dir):
os.makedirs(self.ng_dir)
result_image_save_path = self.ng_dir + result_image_save_path
elif "Not-clear" in prediction_dict["result"]:
if not os.path.exists(self.not_clear_dir):
os.makedirs(self.not_clear_dir)
result_image_save_path = self.not_clear_dir + result_image_save_path
else:
if not os.path.exists(self.ok_dir):
os.makedirs(self.ok_dir)
result_image_save_path = self.ok_dir + result_image_save_path
# Save the result image
prediction_dict["result_image"].save(result_image_save_path)
return result_dict, result_image_save_path