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.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
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 *.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
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-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+.DS_Store
+.env
+.flaskenv
+*.pyc
+*.pyo
+env/
+venv/
+.venv/
+env*
+dist/
+build/
+*.egg
+*.egg-info/
+.tox/
+.cache/
+.pytest_cache/
+.idea/
+docs/_build/
+.vscode
+.tflite
+
+# Coverage reports
+htmlcov/
+.coverage
+.coverage.*
+*,cover

Dockerfile

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+# FROM python:3.8-slim-buster
+# COPY . /app
+# WORKDIR /app
+# RUN apt-get update
+# RUN pip3 install -r requirements.txt
+# EXPOSE 5000
+# ENTRYPOINT [ "python" ]
+# CMD [ "application.py" ]
+FROM tensorflow/tensorflow
+
+# Copy local code to the container image.
+COPY . /app
+WORKDIR /app
+
+# Install production dependencies.
+RUN pip3 install -r requirements.txt
+
+ENV PORT 5000
+EXPOSE 5000
+
+ENTRYPOINT [ "python" ]
+CMD [ "application.py" ]
+
+# webserver, with one worker process and 8 threads.
+# CMD exec gunicorn --bind 0.0.0.0:$PORT --workers 1 --threads 8 --timeout 0 main:app

application.py

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+from flask import Flask, request, jsonify
+import os
+from waitress import serve
+from model.predict import *
+
+app = Flask(__name__)
+
+@app.route('/api/create-texture', methods=['POST', 'GET', 'DELETE'])
+def create_texture():   
+    print(request.files)
+    front_image = request.files["front"].read()
+    back_image = request.files["back"].read()
+    primary_color = request.form["color"]
+
+    front_output = predict_h5(front_image)
+    back_output = predict_h5(back_image)
+
+    return jsonify({"front": front_output, "back" : back_output})
+
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 5000))
+    app.run(debug=True, host="0.0.0.0")
+    # serve(app, host="0.0.0.0", port=port)

model/model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8907e86ca825a4222ce652ef48fde473da62d6693dcedbc563f347a3837ce166
+size 215186872

model/predict.py

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+import base64
+import numpy as np
+import cv2
+import tensorflow as tf
+from tensorflow.keras.utils import CustomObjectScope
+
+def iou(y_true, y_pred):
+    def f(y_true, y_pred):
+        intersection = (y_true * y_pred).sum()
+        union = y_true.sum() + y_pred.sum() - intersection
+        x = (intersection + 1e-15) / (union + 1e-15)
+        x = x.astype(np.float32)
+        return x
+    return tf.numpy_function(f, [y_true, y_pred], tf.float32)
+
+smooth = 1e-15
+def dice_coef(y_true, y_pred):
+    y_true = tf.keras.layers.Flatten()(y_true)
+    y_pred = tf.keras.layers.Flatten()(y_pred)
+    intersection = tf.reduce_sum(y_true * y_pred)
+    return (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)
+
+def dice_loss(y_true, y_pred):
+    return 1.0 - dice_coef(y_true, y_pred)
+
+def predict():
+    #Load Model
+    interpreter = tf.lite.Interpreter(model_path='model/segmentation_model.tflite')
+    interpreter.allocate_tensors()
+
+    # Get input and output tensors.
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    # Global Parameters
+    H = 512
+    W = 384
+
+    # Set up your input data.
+    image = cv2.imread('model/test.jpg', cv2.IMREAD_COLOR)    
+
+    resized_image = cv2.resize(image, (W, H))
+    x = resized_image/255.0
+    x = x.astype(np.float32)
+    x = np.expand_dims(x,0)
+
+    # Input Data to Model
+    interpreter.set_tensor(input_details[0]['index'], x)
+    interpreter.invoke()
+
+    #Geting Output
+    prediction = interpreter.get_tensor(output_details[0]['index'])[0]
+
+    prediction = cv2.resize(prediction, (W, H))
+    prediction = np.expand_dims(prediction, axis=-1)
+    prediction = prediction > 0.5
+
+    mask = np.uint8(prediction) * 255
+    prediction = cv2.merge([mask, mask, mask])
+
+    mask_inv = cv2.cvtColor(prediction, cv2.COLOR_BGR2GRAY)
+
+    output_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2BGRA)
+    output_image[..., 3] = mask_inv
+
+    # cv2.imshow(output_image)
+
+    cv2.imwrite("model/output.png", output_image)
+
+    return "done"
+
+def predict_h5(file):
+    #Load Model
+    with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef, 'dice_loss': dice_loss}):
+        model = tf.keras.models.load_model("model/model.h5")
+
+    # Global Parameters
+    H = 512
+    W = 384
+
+    # Set up your input data.
+    # Convert File to np array
+    file_bytes = np.fromstring(file, np.uint8)
+    # convert numpy array to image
+    image = cv2.imdecode(file_bytes, cv2.IMREAD_UNCHANGED)
+
+    resized_image = cv2.resize(image, (W, H))
+    x = resized_image/255.0
+    x = x.astype(np.float32)
+    x = np.expand_dims(x,0)
+
+    # Input Data to Model
+
+    #Geting Output
+    prediction = model.predict(x)[0]
+
+    prediction = cv2.resize(prediction, (W, H))
+    prediction = np.expand_dims(prediction, axis=-1)
+    prediction = prediction > 0.5
+
+    mask = np.uint8(prediction) * 255
+    prediction = cv2.merge([mask, mask, mask])
+
+    mask_inv = cv2.cvtColor(prediction, cv2.COLOR_BGR2GRAY)
+
+    output_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2BGRA)
+    output_image[..., 3] = mask_inv
+
+    retval, buffer = cv2.imencode('.png', output_image)
+    encoded_image = base64.b64encode(buffer).decode('utf-8')
+
+    return encoded_image

requirements.txt

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+flask
+opencv-python-headless
+waitress