upload model to cdn and api

switch to yolov11

README.md

@@ -25,16 +25,9 @@ Linux
     pip install virtualenv
     python -m venv env
     env\Scripts\activate
+    pip install -r requirements.txt
     ```
 
-```
-    python -m pip install --upgrade pip
-    pip install --upgrade huggingface_hub
-    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
-    pip install git+https://github.com/airockchip/ultralytics_yolov8.git
-    pip install albumentations
-```
-
 **3. Fix possible problems**
   * cv2.error: OpenCV(4.10.0) ...\window.cpp:1301: error: (-2:Unspecified error)
       ```

azaion_api.py

@@ -0,0 +1,37 @@
+import io
+import requests
+
+
+class ApiCredentials:
+    def __init__(self, url, email, password, folder):
+        self.url = url
+        self.email = email
+        self.password = password
+        self.folder = folder
+
+
+class Api:
+    def __init__(self, credentials):
+        self.token = None
+        self.credentials = credentials
+
+    def login(self):
+        response = requests.post(f'{self.credentials.url}/login',
+                                 json={"email": self.credentials.email, "password": self.credentials.password})
+        response.raise_for_status()
+        token = response.json()["token"]
+        self.token = token
+
+    def upload_file(self, filename: str, file_bytes: bytearray):
+        folder = self.credentials.folder
+        if self.token is None:
+            self.login()
+        url = f"{self.credentials.url}/resources/{folder}"
+        headers = {"Authorization": f"Bearer {self.token}"}
+        files = {'data': (filename, io.BytesIO(file_bytes))}
+        try:
+            r = requests.post(url, headers=headers, files=files, allow_redirects=True)
+            r.raise_for_status()
+            print(f"Upload {len(file_bytes)} bytes ({filename}) to {self.credentials.url}. Result: {r.status_code}")
+        except Exception as e:
+            print(f"Upload fail: {e}")

cdn_manager.py

@@ -0,0 +1,34 @@
+import io
+import boto3
+
+
+class CDNCredentials:
+    def __init__(self, host, access_key, secret_key):
+        self.host = host
+        self.access_key = access_key
+        self.secret_key = secret_key
+
+
+class CDNManager:
+    def __init__(self, credentials: CDNCredentials):
+        self.creds = credentials
+        self.minio_client = boto3.client('s3', endpoint_url=self.creds.host,
+                                         aws_access_key_id=self.creds.access_key,
+                                         aws_secret_access_key=self.creds.secret_key)
+
+    def upload(self, bucket: str, filename: str, file_bytes: bytearray):
+        try:
+            self.minio_client.upload_fileobj(io.BytesIO(file_bytes), bucket, filename)
+            print(f'Uploaded {len(file_bytes)} bytes to {self.creds.host}/{bucket}/{filename}')
+            return True
+        except Exception as e:
+            print(e)
+            return False
+
+    def download(self, bucket: str, filename: str):
+        try:
+            self.minio_client.download_file(bucket, filename, filename)
+            return True
+        except Exception as e:
+            print(e)
+            return False

config.yaml

@@ -0,0 +1,11 @@
+cdn:
+  host: 'https://cdnapi.azaion.com'
+  access_key: '8gx5HWhLXD1sEZgQjxam'
+  secret_key: 'KHjr6xmveqgKa7UibY9kEQUQ7VhjT8yfmG1fP0tV'
+  bucket: 'models'
+
+api:
+  url: 'https://api.azaion.com'
+  user: 'admin@azaion.com'
+  pw: 'Az@1on1000Odm$n'
+  folder: ''

constants.py

@@ -27,3 +27,5 @@ annotation_classes = AnnotationClass.read_json()
 date_format = '%Y-%m-%d'
 checkpoint_file = 'checkpoint.txt'
 checkpoint_date_format = '%Y-%m-%d %H:%M:%S'
+
+CONFIG_FILE = "config.yaml"

requirements.txt

@@ -0,0 +1,14 @@
+huggingface_hub
+torch
+torchvision
+torchaudio
+ultralytics
+albumentations~=2.0.4
+
+opencv-python~=4.11.0.86
+matplotlib~=3.10.0
+PyYAML~=6.0.2
+cryptography~=44.0.1
+numpy~=2.1.1
+requests~=2.32.3
+pyyaml

security.py

@@ -0,0 +1,58 @@
+import base64
+import hashlib
+import os
+
+from cryptography.hazmat.backends import default_backend
+from cryptography.hazmat.primitives import padding
+from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+
+BUFFER_SIZE = 64 * 1024  # 64 KB
+
+
+class Security:
+    @staticmethod
+    def encrypt_to(input_bytes, key):
+        aes_key = hashlib.sha256(key.encode('utf-8')).digest()
+        iv = os.urandom(16)
+
+        cipher = Cipher(algorithms.AES(aes_key), modes.CBC(iv), backend=default_backend())
+        encryptor = cipher.encryptor()
+        padder = padding.PKCS7(128).padder()
+
+        padded_plaintext = padder.update(input_bytes) + padder.finalize()
+        ciphertext = encryptor.update(padded_plaintext) + encryptor.finalize()
+
+        return iv + ciphertext
+
+    @staticmethod
+    def decrypt_to(ciphertext_with_iv_bytes, key):
+        aes_key = hashlib.sha256(key.encode('utf-8')).digest()
+        iv = ciphertext_with_iv_bytes[:16]
+        ciphertext_bytes = ciphertext_with_iv_bytes[16:]
+
+        cipher = Cipher(algorithms.AES(aes_key), modes.CBC(iv), backend=default_backend())
+        decryptor = cipher.decryptor()
+
+        decrypted_padded_bytes = decryptor.update(ciphertext_bytes) + decryptor.finalize()
+
+        # Manual PKCS7 unpadding check and removal
+        padding_value = decrypted_padded_bytes[-1]  # Get the last byte, which indicates padding length
+        if 1 <= padding_value <= 16: # Valid PKCS7 padding value range for AES-128
+            padding_length = padding_value
+            plaintext_bytes = decrypted_padded_bytes[:-padding_length] # Remove padding bytes
+        else:
+            plaintext_bytes = decrypted_padded_bytes
+
+        return bytes(plaintext_bytes)
+
+    @staticmethod
+    def get_model_encryption_key():
+        key = '-#%@AzaionKey@%#---234sdfklgvhjbnn'
+        return Security.calc_hash(key)
+
+    @staticmethod
+    def calc_hash(key):
+        str_bytes = key.encode('utf-8')
+        hash_bytes = hashlib.sha384(str_bytes).digest()
+        h = base64.b64encode(hash_bytes).decode('utf-8')
+        return h

tests/predictor.py

@@ -1,6 +1,33 @@
 from abc import ABC, abstractmethod
+from ultralytics import YOLO
+import yaml
+
 
 class Predictor(ABC):
     @abstractmethod
     def predict(self, frame):
         pass
+
+
+class OnnxPredictor(Predictor):
+    def __init__(self):
+        self.model = YOLO('azaion.onnx')
+        self.model.task = 'detect'
+        with open('data.yaml', 'r') as f:
+            data_yaml = yaml.safe_load(f)
+        class_names = data_yaml['names']
+
+        names = self.model.names
+
+    def predict(self, frame):
+        results = self.model.track(frame, persist=True, tracker='bytetrack.yaml')
+        return results[0].plot()
+
+
+class YoloPredictor(Predictor):
+    def __init__(self):
+        self.model = YOLO('azaion.pt')
+
+    def predict(self, frame):
+        results = self.model.track(frame, persist=True, tracker='bytetrack.yaml')
+        return results[0].plot()

tests/security_test.py

@@ -0,0 +1,10 @@
+import io
+from security import Security
+
+key = Security.get_model_encryption_key()
+test_str = 'test test test 123'
+
+test_encrypted = Security.encrypt_to(io.BytesIO(test_str.encode('utf-8')), key)
+test_res = Security.decrypt_to(io.BytesIO(test_encrypted), key)
+print(f'Initial: {test_str}')
+print(f'Result : {test_res}')

tests/test-prediction.py

@@ -5,20 +5,23 @@ from ultralytics import YOLO
 import cv2
 from time import sleep
 
-from yolo_predictor import YOLOPredictor
+from predictor import OnnxPredictor, YoloPredictor
 
 # video_url = 'https://www.youtube.com/watch?v=d1n2fDOSo8c'
 # stream = CamGear(source=video_url, stream_mode=True, logging=True).start()
+write_output = False
 
-predictor = YOLOPredictor()
+predictor = YoloPredictor()
 
 fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
 
-input_name = 'ForAI.mp4'
+input_name = 'ForAI_test.mp4'
 output_name = Path(input_name).stem + '_recognised.mp4'
 
 v_input = cv2.VideoCapture(input_name)
-v_output = cv2.VideoWriter(output_name, fourcc, 20.0, (640, 480))
+
+if write_output:
+    v_output = cv2.VideoWriter(output_name, fourcc, 20.0, (640, 480))
 
 while v_input.isOpened():
     ret, frame = v_input.read()
@@ -30,10 +33,12 @@ while v_input.isOpened():
     cv2.imshow('Video', frame_detected)
     sleep(0.01)
 
+    if write_output:
         v_output.write(frame_detected)
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break
 
 v_input.release()
-v_output.release()
+if write_output:
+    v_output.release()
 cv2.destroyAllWindows()

tests/yolo_predictor.py

@@ -1,20 +0,0 @@
-import cv2
-import numpy as np
-import yaml
-
-from predictor import Predictor
-from ultralytics import YOLO
-
-class YOLOPredictor(Predictor):
-    def __init__(self):
-        self.model = YOLO('/azaion/models/azaion.onnx')
-        self.model.task = 'detect'
-        with open('data.yaml', 'r') as f:
-            data_yaml = yaml.safe_load(f)
-        class_names = data_yaml['names']
-
-        names = self.model.names
-
-    def predict(self, frame):
-        results = self.model.track(frame, persist=True, tracker='bytetrack.yaml')
-        return results[0].plot()

train.py

@@ -1,3 +1,4 @@
+import io
 import os
 import random
 import shutil
@@ -6,7 +7,15 @@ from datetime import datetime
 from os import path, replace, listdir, makedirs, scandir
 from os.path import abspath
 from pathlib import Path
+from utils import Dotdict
+
+import yaml
 from ultralytics import YOLO
+
+import constants
+from azaion_api import ApiCredentials, Api
+from cdn_manager import CDNCredentials, CDNManager
+from security import Security
 from constants import (processed_images_dir,
                        processed_labels_dir,
                        annotation_classes,
@@ -145,6 +154,7 @@ def get_latest_model():
     last_model = sorted_dates[-1]
     return last_model['date'], last_model['path']
 
+
 def train_dataset(existing_date=None, from_scratch=False):
     latest_date, latest_model = get_latest_model()
 
@@ -156,7 +166,7 @@ def train_dataset(existing_date=None, from_scratch=False):
         cur_folder = today_folder
         cur_dataset = today_dataset
 
-    model_name = latest_model if latest_model is not None and path.isfile(latest_model) and not from_scratch else 'yolov8m.yaml'
+    model_name = latest_model if latest_model is not None and path.isfile(latest_model) and not from_scratch else 'yolo11m.yaml'
     print(f'Initial model: {model_name}')
     model = YOLO(model_name)
 
@@ -171,8 +181,10 @@ def train_dataset(existing_date=None, from_scratch=False):
     model_dir = path.join(models_dir, cur_folder)
     shutil.copytree(results.save_dir, model_dir)
 
-    shutil.copy(path.join(model_dir, 'weights', 'best.pt'), path.join(models_dir, f'{prefix[:-1]}.pt'))
+    model_path = path.join(models_dir, f'{prefix[:-1]}.pt')
+    shutil.copy(path.join(model_dir, 'weights', 'best.pt'), model_path)
     shutil.rmtree('runs')
+    return model_path
 
 
 def convert2rknn():
@@ -209,8 +221,38 @@ def validate(model_path):
     metrics = model.val()
     pass
 
+
+def upload_model(model_path: str):
+
+    # model = YOLO(model_path)
+    # model.export(format="onnx", imgsz=1280, nms=True, batch=4)
+    onnx_model = path.dirname(model_path) + Path(model_path).stem + '.onnx'
+
+    with open(onnx_model, 'rb') as f_in:
+        onnx_bytes = f_in.read()
+
+    key = Security.get_model_encryption_key()
+    onnx_encrypted = Security.encrypt_to(onnx_bytes, key)
+
+    part1_size = min(10 * 1024, int(0.9 * len(onnx_encrypted)))
+    onnx_part_small = onnx_encrypted[:part1_size]  # slice bytes for part1
+    onnx_part_big = onnx_encrypted[part1_size:]
+
+    with open(constants.CONFIG_FILE, "r") as f:
+        config_dict = yaml.safe_load(f)
+        d_config = Dotdict(config_dict)
+        cdn_c = Dotdict(d_config.cdn)
+        api_c = Dotdict(d_config.api)
+    cdn_manager = CDNManager(CDNCredentials(cdn_c.host, cdn_c.access_key, cdn_c.secret_key))
+    cdn_manager.upload(cdn_c.bucket, 'azaion.onnx.big', onnx_part_big)
+
+    api = Api(ApiCredentials(api_c.url, api_c.user, api_c.pw, api_c.folder))
+    api.upload_file('azaion.onnx.small', onnx_part_small)
+
 if __name__ == '__main__':
-    train_dataset('2024-10-26', from_scratch=True)
-    validate(path.join('runs', 'detect', 'train7', 'weights', 'best.pt'))
-    form_data_sample(500)
-    convert2rknn()
+    # model_path = train_dataset('2024-10-26', from_scratch=True)
+    # validate(path.join('runs', 'detect', 'train7', 'weights', 'best.pt'))
+    # form_data_sample(500)
+    # convert2rknn()
+    model_path = 'azaion.pt'
+    upload_model(model_path)

utils.py

@@ -0,0 +1,5 @@
+class Dotdict(dict):
+    """dot.notation access to dictionary attributes"""
+    __getattr__ = dict.get
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__

yolov8.yaml

@@ -1,46 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
-
-# Parameters
-nc: 80  # number of classes
-scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
-  # [depth, width, max_channels]
-  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
-  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
-  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
-  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
-  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
-
-# YOLOv8.0n backbone
-backbone:
-  # [from, repeats, module, args]
-  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
-  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
-  - [-1, 3, C2f, [128, True]]
-  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
-  - [-1, 6, C2f, [256, True]]
-  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
-  - [-1, 6, C2f, [512, True]]
-  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
-  - [-1, 3, C2f, [1024, True]]
-  - [-1, 1, SPPF, [1024, 5]]  # 9
-
-# YOLOv8.0n head
-head:
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
-  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
-  - [-1, 3, C2f, [512]]  # 12
-
-  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
-  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
-  - [-1, 3, C2f, [256]]  # 15 (P3/8-small)
-
-  - [-1, 1, Conv, [256, 3, 2]]
-  - [[-1, 12], 1, Concat, [1]]  # cat head P4
-  - [-1, 3, C2f, [512]]  # 18 (P4/16-medium)
-
-  - [-1, 1, Conv, [512, 3, 2]]
-  - [[-1, 9], 1, Concat, [1]]  # cat head P5
-  - [-1, 3, C2f, [1024]]  # 21 (P5/32-large)
-
-  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)