Docker support

Dockerfile

@@ -0,0 +1,41 @@
+## === Stage 1 --- Build dlib wheel ===
+FROM python:3.8
+
+## Build 'dlib' separately - it takes a long time and needs a lot of space
+RUN apt-get update && apt-get install -y build-essential cmake
+RUN mkdir -p /tmp/dlib
+RUN pip wheel --use-pep517 --wheel-dir /tmp/dlib dlib
+RUN ln /tmp/dlib/dlib-*.whl /tmp/dlib.whl
+
+
+## === Stage 2 --- Build the actual container ===
+FROM python:3.8
+
+WORKDIR /app
+EXPOSE 5000
+
+# Use 'dlib.whl' from the above container
+COPY --from=0 /tmp/dlib /tmp/dlib
+RUN pip install /tmp/dlib/dlib-*.whl
+RUN rm -rf /tmp/dlib
+
+# libGL1 is required for opencv-python
+RUN apt-get update && apt-get install -y libgl1 && apt-get clean
+
+# Download YOLOv4 model files (also change mlapiconfig.docker.ini
+# if you need some other models)
+COPY get_models.sh .
+RUN INSTALL_YOLOV3=no INSTALL_TINYYOLOV3=no INSTALL_YOLOV4=yes INSTALL_TINYYOLOV4=no ./get_models.sh
+
+# Prevent re-installing if source has changed but not requirements.txt
+COPY requirements.txt .
+RUN pip install -r requirements.txt && rm -rf /root/.cache
+
+# Copy all the files
+COPY . .
+RUN rm -rf .git
+
+# Use Docker config customised for Docker
+RUN cp mlapiconfig.docker.ini mlapiconfig.ini
+
+CMD [ "python3", "./mlapi.py", "-c", "mlapiconfig.ini" ]

README.md

@@ -20,9 +20,31 @@ Tip of the Hat
 ===============
 A tip of the hat to [Adrian Rosebrock](https://www.pyimagesearch.com/about/) to get me started. His articles are great.
 
+Running in a Docker container
+=============================
+A simple `Dockerfile` is included with this project. It is preconfigured for *YOLOv4* model but can easily be changed to use any other available models.
+
+To build it run:
+
+```
+git clone https://github.com/pliablepixels/mlapi.git
+cd mlapi
+docker build -t mlapi:local .
+```
+
+Once built you can run the container:
+
+```
+docker run --rm -it -e MLAPI_USER="myuser" -e MLAPI_PASSWORD="Passw0rd" -p 5000:5000 mlapi:local
+```
+
+To quickly test it run `curl http://localhost:5000/api/v1/health`, then follow the instructions below under the *Running* section.
+
 Containerized Fork
-==================
-themoosman maintains a containerized fork of this [repo](https://github.com/themoosman/mlapi).  This fork runs as a container and has been refactored to a WSGI (NGINX + Gunicorn + Flask) application. Please **do not** post questions about his containerized fork here. Please post issues in his fork.
+------------------
+[@themoosman](https://github.com/themoosman) maintains a [containerized fork of this mlapi](https://github.com/themoosman/mlapi).
+
+This fork has been refactored to a WSGI (NGINX + Gunicorn + Flask) application. Please **do not** post questions about his containerized fork here. Please post issues in his fork.
 
 Install
 =======
@@ -37,7 +59,7 @@ Note that this package also needs OpenCV which is not installed by the above ste
 
 Then:
 ```
- git clone https://github.com/pliablepixels/mlapi
+ git clone https://github.com/pliablepixels/mlapi.git
  cd mlapi
  sudo -H pip3 install -r requirements.txt
  ```

get_models.sh

@@ -18,7 +18,7 @@ INSTALL_TINYYOLOV4=${INSTALL_TINYYOLOV4:-yes}
 INSTALL_CORAL_EDGETPU=${INSTALL_CORAL_EDGETPU:-no}
 
 TARGET_DIR='./models'
-WGET=$(which wget)
+WGET="$(which wget) --progress=bar:force:noscroll"
 
 # utility functions for color coded pretty printing
 print_error() {

mlapiconfig.docker.ini

@@ -0,0 +1,135 @@
+[general]
+# This is an optional file
+# If specified, you can specify tokens with secret values in that file
+# and onlt refer to the tokens in your main config file
+
+secrets=./secrets.ini
+#secrets=./secrets.mine
+
+# port that mlapi will listen on. Default 5000
+port=5000
+
+# Maximum # of processes that will be forked
+# to handle requests. Note that each process will
+# have its own copy of the model, so memory can 
+# build up very quickly
+# This number also dictates how many requests will be executed in parallel
+# The rest will be queued
+
+# For now, keep this to 1 if you are on a GPU
+processes=1
+
+# the secret key that will be used to sign
+# JWT tokens. Make sure you change the value
+# in your secrets.ini
+mlapi_secret_key=!MLAPI_SECRET_KEY
+
+# folder where images will be uploaded
+# default ./images
+images_path=./images
+
+# folder where the user DB will be stored
+db_path=./db
+
+# If specified, will limit detected object size to this amount of
+# the total image size passed. Can help avoiding weird detections
+# You can specify as % or px. Default is px
+# Remember the image is resized to 416x416 internally. better
+# to keep in %
+max_detection_size=100%
+
+# You can now limit the # of detection process
+# per target processor. If not specified, default is 1
+# Other detection processes will wait to acquire lock
+
+cpu_max_processes=3
+tpu_max_processes=1
+gpu_max_processes=1
+
+# NEW: Time to wait in seconds per processor to be free, before
+# erroring out. Default is 120 (2 mins)
+cpu_max_lock_wait=120
+tpu_max_lock_wait=120
+gpu_max_lock_wait=120
+
+
+[object]
+
+# for Yolov3
+#object_framework=opencv
+#object_processor=cpu
+#object_config=./models/yolov3/yolov3.cfg
+#object_weights=./models/yolov3/yolov3.weights
+#object_labels=./models/yolov3/coco.names
+
+# for Tiny Yolov3
+#object_framework=opencv
+#object_processor=cpu
+#object_config=./models/tinyyolov3/yolov3-tiny.cfg
+#object_weights=./models/tinyyolov3/yolov3-tiny.weights
+#object_labels=./models/tinyyolov3/coco.names
+
+# for Yolov4
+object_framework=opencv
+object_processor=cpu
+object_config=./models/yolov4/yolov4.cfg
+object_weights=./models/yolov4/yolov4.weights
+object_labels=./models/yolov4/coco.names
+
+# for Tiny Yolov4
+#object_framework=opencv
+#object_processor=cpu
+#object_config=./models/tinyyolov4/yolov4-tiny.cfg
+#object_weights=./models/tinyyolov4/yolov4-tiny.weights
+#object_labels=./models/tinyyolov4/coco.names
+
+# for Google Coral Edge TPU
+#object_framework=coral_edgetpu
+#object_processor=tpu
+#object_weights=./models/coral_edgetpu/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite
+#object_labels=./models/coral_edgetpu/coco_indexed.names
+
+
+[face]
+face_detection_framework=dlib
+face_recognition_framework=dlib
+face_num_jitters=0
+face_upsample_times=1
+face_model=cnn
+face_train_model=hog
+face_recog_dist_threshold=0.6
+face_recog_knn_algo=ball_tree
+
+known_images_path=./known_faces
+unknown_images_path=./unknown_faces
+
+unknown_face_name=unknown face
+save_unknown_faces=yes
+save_unknown_faces_leeway_pixels=50
+
+[alpr]
+
+alpr_use_after_detection_only=yes
+alpr_api_type=cloud
+
+# -----| If you are using plate recognizer | ------
+alpr_service=plate_recognizer
+alpr_key=!PLATEREC_ALPR_KEY
+platerec_stats=yes
+#platerec_regions=['us','cn','kr']
+platerec_min_dscore=0.1
+platerec_min_score=0.2
+
+# ----| If you are using openALPR |-----
+#alpr_service=open_alpr
+#alpr_key=!OPENALPR_ALPR_KEY
+#openalpr_recognize_vehicle=1
+#openalpr_country=us
+#openalpr_state=ca
+# openalpr returns percents, but we convert to between 0 and 1
+#openalpr_min_confidence=0.3
+
+# ----| If you are using openALPR command line |-----
+openalpr_cmdline_binary=alpr
+openalpr_cmdline_params=-j -d
+openalpr_cmdline_min_confidence=0.3

modules/common_params.py

@@ -1,3 +1,4 @@
+import os
 import modules.log as g_log
 
 
@@ -71,7 +72,7 @@
         },
         'mlapi_secret_key':{
             'section': 'general',
-            'default': None,
+            'default': os.getenv('MLAPI_SECRET_KEY'),
             'type': 'string',
         },
 
@@ -195,7 +196,7 @@
         },
         'alpr_key': {
             'section': 'alpr',
-            'default': '',
+            'default': os.getenv('MLAPI_ALPR_KEY'),
             'type': 'string',
         },
         'alpr_use_after_detection_only': {

modules/db.py

@@ -1,3 +1,4 @@
+import os
 from tinydb import TinyDB, Query, where
 from passlib.hash import bcrypt
 import modules.common_params as g
@@ -16,24 +17,27 @@ def __init__(self):
         self.query = Query()
         g.log.debug ('DB engine ready')
         if not len(self.users):
-            g.log.debug ('Initializing default users')
-
             print ('--------------- User Creation ------------')
-            print ('Please configure atleast one user:')
-            while True:
-                name = input ('user name:')
-                if not name:
-                    print ('Error: username needed')
-                    continue
-                p1 = getpass.getpass('Please enter password:')
-                if not p1:
-                    print ('Error: password cannot be empty')
-                    continue
-                p2 = getpass.getpass('Please re-enter password:')
-                if  p1 != p2:
-                    print ('Passwords do not match, please re-try')
-                    continue
-                break  
+            if os.getenv('MLAPI_USER') and os.getenv('MLAPI_PASSWORD'):
+                name = os.getenv('MLAPI_USER')
+                p1 = os.getenv('MLAPI_PASSWORD')
+                g.log.debug('Creating user from $MLAPI_USER: {}'.format(name))
+            else:
+                print ('Please configure at least one user:')
+                while True:
+                    name = input ('User name:')
+                    if not name:
+                        print ('Error: username needed')
+                        continue
+                    p1 = getpass.getpass('Please enter password:')
+                    if not p1:
+                        print ('Error: password cannot be empty')
+                        continue
+                    p2 = getpass.getpass('Please re-enter password:')
+                    if  p1 != p2:
+                        print ('Passwords do not match, please re-try')
+                        continue
+                    break
             self.users.insert({'name':name, 'password':self._get_hash(p1)})
             print ('------- User: {} created ----------------'.format(name))
 

requirements.txt

@@ -13,3 +13,4 @@ scikit_learn
 face_recognition
 pyzm>=0.1.27
 portalocker
+opencv-python>=4.3