Merge pull request #1249 from serengil/feat-task-0306-docker-hub

Feat task 0306 docker hub

README.md

@@ -319,11 +319,13 @@ Face recognition, facial attribute analysis and vector representation functions
 
 **Dockerized Service** - [`Demo`](https://youtu.be/9Tk9lRQareA)
 
-You can deploy the deepface api on a kubernetes cluster with docker. The following [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh) will serve deepface on `localhost:5005`. You may need to re-configure the [Dockerfile](https://github.com/serengil/deepface/blob/master/Dockerfile) if you want to apply some customization. Then, even if you do not have a development environment, you will be able to consume deepface services such as verify and analyze. You can also access the inside of the docker image to run deepface related commands. Please follow the instructions in the [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh).
+[![Docker Pulls](https://img.shields.io/docker/pulls/serengil/deepface?logo=docker)](https://hub.docker.com/r/serengil/deepface)
+
+The following command set will serve deepface on `localhost:5005` via docker. Then, you will be able to consume deepface services such as verify, analyze and represent. Also, if you want to build the image by your own instead of pre-built image from docker hub, [Dockerfile](https://github.com/serengil/deepface/blob/master/Dockerfile) is available in the root folder of the project.
 
 ```shell
-cd scripts
-./dockerize.sh
+docker pull serengil/deepface
+docker run -p 5005:5000 serengil/deepface
 ```
 
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-dockerized-v2.jpg" width="50%" height="50%"></p>

deepface/DeepFace.py

@@ -300,7 +300,7 @@ def find(
         silent (boolean): Suppress or allow some log messages for a quieter analysis process
             (default is False).
 
-        refresh_database (boolean): Synchronizes the images representation (pkl) file with the 
+        refresh_database (boolean): Synchronizes the images representation (pkl) file with the
         directory/db files, if set to false, it will ignore any file changes inside the db_path
         (default is True).
 

deepface/modules/verification.py

@@ -64,7 +64,7 @@ def verify(
 
         silent (boolean): Suppress or allow some log messages for a quieter analysis process
             (default is False).
-        
+
         threshold (float): Specify a threshold to determine whether a pair represents the same
             person or different individuals. This threshold is used for comparing distances.
             If left unset, default pre-tuned threshold values will be applied based on the specified

scripts/dockerize.sh

@@ -16,10 +16,14 @@ docker build -t deepface .
 # copy weights from your local
 # docker cp ~/.deepface/weights/. <CONTAINER_ID>:/root/.deepface/weights/
 
-# run image
+# run the built image
 # docker run --net="host" deepface
 docker run -p 5005:5000 deepface
 
+# or pull the pre-built image from docker hub and run it
+# docker pull serengil/deepface
+# docker run -p 5005:5000 serengil/deepface
+
 # to access the inside of docker image when it is in running status
 # docker exec -it <CONTAINER_ID> /bin/sh
 

tests/test_extract_faces.py

@@ -2,6 +2,7 @@
 import base64
 
 # 3rd party dependencies
+import cv2
 import numpy as np
 import pytest
 
@@ -16,8 +17,12 @@ detectors = ["opencv", "mtcnn"]
 
 
 def test_different_detectors():
+    img_path = "dataset/img11.jpg"
+    img = cv2.imread(img_path)
+    height, width, _ = img.shape
+
     for detector in detectors:
-        img_objs = DeepFace.extract_faces(img_path="dataset/img11.jpg", detector_backend=detector)
+        img_objs = DeepFace.extract_faces(img_path=img_path, detector_backend=detector)
         for img_obj in img_objs:
             assert "face" in img_obj.keys()
             assert "facial_area" in img_obj.keys()
@@ -34,6 +39,23 @@ def test_different_detectors():
             assert left_eye[0] > right_eye[0]
             assert "confidence" in img_obj.keys()
 
+            # we added black pixeled borders to image because if faces are close to border,
+            # then alignment moves them to outside of the image. adding this borders may
+            # cause to miscalculate the facial area. check it is restored correctly.
+            x = img_obj["facial_area"]["x"]
+            y = img_obj["facial_area"]["y"]
+            w = img_obj["facial_area"]["w"]
+            h = img_obj["facial_area"]["h"]
+
+            assert x < width
+            assert x + w < width
+            assert y < height
+            assert y + h < height
+            assert left_eye[0] < height
+            assert right_eye[0] < height
+            assert left_eye[1] < width
+            assert right_eye[1] < width
+
             img = img_obj["face"]
             assert img.shape[0] > 0 and img.shape[1] > 0
         logger.info(f"✅ extract_faces for {detector} backend test is done")