Merge pull request oarriaga#16 from ekholabs/master

Dockerize the project and add a RESTful service to process images

Dockerfile

@@ -0,0 +1,17 @@
+FROM debian:latest
+
+RUN apt-get -y update && apt-get install -y git python3-pip python3-dev python3-tk vim procps curl
+
+#Face classificarion dependencies & web application
+RUN pip3 install numpy scipy scikit-learn pillow tensorflow pandas h5py opencv-python==3.2.0.8 keras statistics pyyaml pyparsing cycler matplotlib Flask
+
+ADD . /ekholabs/face-classifier
+
+WORKDIR ekholabs/face-classifier
+
+ENV PYTHONPATH=$PYTHONPATH:src
+ENV FACE_CLASSIFIER_PORT=8084
+EXPOSE $FACE_CLASSIFIER_PORT
+
+ENTRYPOINT ["python3"]
+CMD ["src/web/faces.py"]

README.md

@@ -5,7 +5,16 @@ Real-time face detection and emotion/gender classification using fer2013/IMDB da
 
 For more information please consult the [publication](https://github.com/oarriaga/face_classification/blob/master/report.pdf)
 
-Emotion/gender examples:
+# Running with Docker
+
+With a few steps one can get its own face classification and detection running. Follow the commands below:
+
+* ```docker pull ekholabs/face-classifier```
+* ```docker run -d -p 8084:8084 --name=face-classifier ekholabs/face-classifier```
+* ```curl -v -F image=@[path_to_image]  http://localhost:8084/classifyImage > image.png```
+
+# Emotion/gender examples:
+
 ![alt tag](images/demo_results.png)
 
 Guided back-prop
@@ -19,7 +28,6 @@ Real-time demo:
 [B-IT-BOTS](https://mas-group.inf.h-brs.de/?page_id=622) robotics team :)
 ![alt tag](images/robocup_team.png)
 
-
 ## Instructions
 
 ### To train previous/new models for emotion classification:

src/web/__init__.py

@@ -0,0 +1 @@
+

src/web/emotion_gender_processor.py

@@ -0,0 +1,94 @@
+import os
+import sys
+import logging
+
+import cv2
+from keras.models import load_model
+import numpy as np
+
+from utils.datasets import get_labels
+from utils.inference import detect_faces
+from utils.inference import draw_text
+from utils.inference import draw_bounding_box
+from utils.inference import apply_offsets
+from utils.inference import load_detection_model
+from utils.inference import load_image
+from utils.preprocessor import preprocess_input
+
+def process_image(image):
+
+    try:
+        # parameters for loading data and images
+        detection_model_path = './trained_models/detection_models/haarcascade_frontalface_default.xml'
+        emotion_model_path = './trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
+        gender_model_path = './trained_models/gender_models/simple_CNN.81-0.96.hdf5'
+        emotion_labels = get_labels('fer2013')
+        gender_labels = get_labels('imdb')
+        font = cv2.FONT_HERSHEY_SIMPLEX
+
+        # hyper-parameters for bounding boxes shape
+        gender_offsets = (30, 60)
+        gender_offsets = (10, 10)
+        emotion_offsets = (20, 40)
+        emotion_offsets = (0, 0)
+
+        # loading models
+        face_detection = load_detection_model(detection_model_path)
+        emotion_classifier = load_model(emotion_model_path, compile=False)
+        gender_classifier = load_model(gender_model_path, compile=False)
+
+        # getting input model shapes for inference
+        emotion_target_size = emotion_classifier.input_shape[1:3]
+        gender_target_size = gender_classifier.input_shape[1:3]
+
+        # loading images
+        image_array = np.fromstring(image, np.uint8)
+        unchanged_image = cv2.imdecode(image_array, cv2.IMREAD_UNCHANGED)
+
+        rgb_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2RGB)
+        gray_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2GRAY)
+
+        faces = detect_faces(face_detection, gray_image)
+        for face_coordinates in faces:
+            x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
+            rgb_face = rgb_image[y1:y2, x1:x2]
+
+            x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
+            gray_face = gray_image[y1:y2, x1:x2]
+
+            try:
+                rgb_face = cv2.resize(rgb_face, (gender_target_size))
+                gray_face = cv2.resize(gray_face, (emotion_target_size))
+            except:
+                continue
+
+            rgb_face = preprocess_input(rgb_face, False)
+            rgb_face = np.expand_dims(rgb_face, 0)
+            gender_prediction = gender_classifier.predict(rgb_face)
+            gender_label_arg = np.argmax(gender_prediction)
+            gender_text = gender_labels[gender_label_arg]
+
+            gray_face = preprocess_input(gray_face, True)
+            gray_face = np.expand_dims(gray_face, 0)
+            gray_face = np.expand_dims(gray_face, -1)
+            emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
+            emotion_text = emotion_labels[emotion_label_arg]
+
+            if gender_text == gender_labels[0]:
+                color = (0, 0, 255)
+            else:
+                color = (255, 0, 0)
+
+            draw_bounding_box(face_coordinates, rgb_image, color)
+            draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
+            draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)
+    except Exception as err:
+        logging.error('Error in emotion gender processor: "{0}"'.format(err))
+
+    bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
+
+    dirname = 'result'
+    if not os.path.exists(dirname):
+        os.mkdir(dirname)
+
+    cv2.imwrite(os.path.join(dirname, 'predicted_image.png'), bgr_image)

src/web/faces.py

@@ -0,0 +1,31 @@
+from flask import Flask, jsonify, make_response, request, abort, redirect, send_file
+import logging
+
+import emotion_gender_processor as eg_processor
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return redirect("https://ekholabs.ai", code=302)
+
+@app.route('/classifyImage', methods=['POST'])
+def upload():
+    try:
+        image = request.files['image'].read()
+        eg_processor.process_image(image)
+        return send_file('/ekholabs/face-classifier/result/predicted_image.png', mimetype='image/png')
+    except Exception as err:
+        logging.error('An error has occurred whilst processing the file: "{0}"'.format(err))
+        abort(400)
+
+@app.errorhandler(400)
+def bad_request(erro):
+    return make_response(jsonify({'error': 'We cannot process the file sent in the request.'}), 400)
+
+@app.errorhandler(404)
+def not_found(error):
+    return make_response(jsonify({'error': 'Resource no found.'}), 404)
+
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=8084)