init

.gitattributes

@@ -32,3 +32,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pechpoints_weights/checkpoint.ckpt.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+saved_models/FerNetEfficientNetB2/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+saved_models/TsinghuaFerNet_EfficientNetB2/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

App/Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3
+RUN pip install flask
+RUN pip install requests
+RUN pip install gcloud
+RUN pip install --upgrade google-cloud-translate
+EXPOSE 5003/tcp
+COPY app.py .
+COPY fmartinezlopezGTranslateKey.json .
+COPY templates/index.html templates/
+ENTRYPOINT [ "python3", "app.py" ]

App/GDownload.py

@@ -0,0 +1,30 @@
+import requests
+
+def download_file_from_google_drive(id, destination):
+    URL = "https://docs.google.com/uc?export=download"
+
+    session = requests.Session()
+
+    response = session.get(URL, params = { 'id' : id }, stream = True)
+    token = get_confirm_token(response)
+
+    if token:
+        params = { 'id' : id, 'confirm' : token }
+        response = session.get(URL, params = params, stream = True)
+
+    save_response_content(response, destination)    
+
+def get_confirm_token(response):
+    for key, value in response.cookies.items():
+        if key.startswith('download_warning'):
+            return value
+
+    return None
+
+def save_response_content(response, destination):
+    CHUNK_SIZE = 32768
+
+    with open(destination, "wb") as f:
+        for chunk in response.iter_content(CHUNK_SIZE):
+            if chunk: # filter out keep-alive new chunks
+                f.write(chunk)

App/app.py

@@ -0,0 +1,96 @@
+#!streamlit/bin/python
+import streamlit as st
+from pathlib import Path
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from io import BytesIO
+import json
+#from GDownload import download_file_from_google_drive
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+#    if selected_model == 'PVAN-Stanford':
+#        model_location = '1-q1R5dLfIFW7BbzKuYTjolAoqpjVClsb'
+#        save_dest = Path('saved_model')
+#        save_dest.mkdir(exist_ok=True)
+#        saved_model = Path("saved_model/FerNet_EfficientNet.h5")
+  
+#    elif selected_model == 'PVAN-Tsinghua':
+  # model_location = '1-q1R5dLfIFW7BbzKuYTjolAoqpjVClsb'
+  # save_dest = Path('saved_model')
+  # save_dest.mkdir(exist_ok=True)
+  # saved_model = Path("saved_model/FerNet_EfficientNet.h5")
+  
+  # if not saved_model.exists():
+  #     download_file_from_google_drive(model_location, saved_model)
+  saved_model = str(Path().parent.absolute())+"/saved_models/FerNetEfficientNetB2"
+  saved_model = tf.keras.models.load_model(saved_model)
+  return saved_model
+
+@st.cache
+def load_classes():
+    with open(str(Path().parent.absolute())+'/App/classes_dict.json') as classes:
+        class_names = json.load(classes)
+    return class_names
+
+def load_and_prep_image(filename, img_shape=260):
+  #img = tf.io.read_file(filename)
+  img = np.array(filename)#tf.io.decode_image(filename, channels=3)
+  # Resize our image
+  img = tf.image.resize(img, [img_shape,img_shape])
+  # Scale
+  return img # don't need to resclae images for EfficientNet models in Tensorflow
+
+if __name__ == '__main__':
+  
+  hide_st_style = """
+            <style>
+            footer {visibility: hidden;}
+            header {visibility: hidden;}
+            </style>
+            """
+  st.markdown(hide_st_style, unsafe_allow_html=True)
+
+  st.title("Dog Breeds Detector")
+  
+  options = ['PVAN-Stanford', 'PVAN-Tsinghua']
+  selected_model = st.selectbox('Select a model to use (Default: PVAN-Stanford):', options)
+  
+  saved_model = load_model()
+  class_names = load_classes()
+  
+  st.write("Choose any dog image and get the corresponding breed:")
+
+  uploaded_image = st.file_uploader("Choose an image...")
+    
+  if uploaded_image:
+    uploaded_image = Image.open(uploaded_image)
+    # try:
+    uploaded_image = uploaded_image.convert("RGB")
+    membuf = BytesIO()
+    uploaded_image.save(membuf, format="jpeg")
+    uploaded_image = Image.open(membuf)
+    # finally:
+
+
+    image_for_the_model = load_and_prep_image(uploaded_image)
+    prediction = saved_model.predict(tf.expand_dims(image_for_the_model, axis=0), verbose=0)
+    
+    top_k_proba, top_k_indices = tf.nn.top_k(prediction,k=5)
+    top_5_classes = {top_n+1:class_names[str(top_k)] for top_n, top_k in enumerate(list(tf.squeeze(top_k_indices).numpy()))}
+    top_k_proba = tf.squeeze(top_k_proba).numpy()
+    top_5_classes = pd.DataFrame({"Top-k":top_5_classes.keys(), "Dog Breed": top_5_classes.values(), "Probability": top_k_proba})
+    #top_5_classes.set_index("Top-k", inplace=True)
+    
+    print(tf.argmax(prediction, axis=1).numpy())
+    predicted_breed = class_names[str(tf.argmax(prediction, axis=1).numpy()[0])]
+    predicted_breed = ' '.join(predicted_breed.split('_'))
+    predicted_breed = predicted_breed.title()
+    st.header(f'This dog looks like a {predicted_breed}')
+    
+    col1, col2 = st.columns([1,2])
+    
+    col1.image(uploaded_image,use_column_width=True)
+    col2.bar_chart(top_5_classes, x="Dog Breed", y="Probability")

App/classes_dict.json

@@ -0,0 +1,120 @@
+{"0": "Chihuahua",
+ "1": "Japanese Spaniel",
+ "2": "Maltese Dog",
+ "3": "Pekinese",
+ "4": "Shih Tzu",
+ "5": "Blenheim Spaniel",
+ "6": "Papillon",
+ "7": "Toy Terrier",
+ "8": "Rhodesian Ridgeback",
+ "9": "Afghan Hound",
+ "10": "Basset",
+ "11": "Beagle",
+ "12": "Bloodhound",
+ "13": "Bluetick",
+ "14": "Black And Tan Coonhound",
+ "15": "Walker Hound",
+ "16": "English Foxhound",
+ "17": "Redbone",
+ "18": "Borzoi",
+ "19": "Irish Wolfhound",
+ "20": "Italian Greyhound",
+ "21": "Whippet",
+ "22": "Ibizan Hound",
+ "23": "Norwegian Elkhound",
+ "24": "Otterhound",
+ "25": "Saluki",
+ "26": "Scottish Deerhound",
+ "27": "Weimaraner",
+ "28": "Staffordshire Bullterrier",
+ "29": "American Staffordshire Terrier",
+ "30": "Bedlington Terrier",
+ "31": "Border Terrier",
+ "32": "Kerry Blue Terrier",
+ "33": "Irish Terrier",
+ "34": "Norfolk Terrier",
+ "35": "Norwich Terrier",
+ "36": "Yorkshire Terrier",
+ "37": "Wire Haired Fox Terrier",
+ "38": "Lakeland Terrier",
+ "39": "Sealyham Terrier",
+ "40": "Airedale",
+ "41": "Cairn",
+ "42": "Australian Terrier",
+ "43": "Dandie Dinmont",
+ "44": "Boston Bull",
+ "45": "Miniature Schnauzer",
+ "46": "Giant Schnauzer",
+ "47": "Standard Schnauzer",
+ "48": "Scotch Terrier",
+ "49": "Tibetan Terrier",
+ "50": "Silky Terrier",
+ "51": "Soft Coated Wheaten Terrier",
+ "52": "West Highland White Terrier",
+ "53": "Lhasa",
+ "54": "Flat Coated Retriever",
+ "55": "Curly Coated Retriever",
+ "56": "Golden Retriever",
+ "57": "Labrador Retriever",
+ "58": "Chesapeake Bay Retriever",
+ "59": "German Short Haired Pointer",
+ "60": "Vizsla",
+ "61": "English Setter",
+ "62": "Irish Setter",
+ "63": "Gordon Setter",
+ "64": "Brittany Spaniel",
+ "65": "Clumber",
+ "66": "English Springer",
+ "67": "Welsh Springer Spaniel",
+ "68": "Cocker Spaniel",
+ "69": "Sussex Spaniel",
+ "70": "Irish Water Spaniel",
+ "71": "Kuvasz",
+ "72": "Schipperke",
+ "73": "Groenendael",
+ "74": "Malinois",
+ "75": "Briard",
+ "76": "Kelpie",
+ "77": "Komondor",
+ "78": "Old English Sheepdog",
+ "79": "Shetland Sheepdog",
+ "80": "Collie",
+ "81": "Border Collie",
+ "82": "Bouvier Des Flandres",
+ "83": "Rottweiler",
+ "84": "German Shepherd",
+ "85": "Doberman",
+ "86": "Miniature Pinscher",
+ "87": "Greater Swiss Mountain Dog",
+ "88": "Bernese Mountain Dog",
+ "89": "Appenzeller",
+ "90": "Entlebucher",
+ "91": "Boxer",
+ "92": "Bull Mastiff",
+ "93": "Tibetan Mastiff",
+ "94": "French Bulldog",
+ "95": "Great Dane",
+ "96": "Saint Bernard",
+ "97": "Eskimo Dog",
+ "98": "Malamute",
+ "99": "Siberian Husky",
+ "100": "Affenpinscher",
+ "101": "Basenji",
+ "102": "Pug",
+ "103": "Leonberg",
+ "104": "Newfoundland",
+ "105": "Great Pyrenees",
+ "106": "Samoyed",
+ "107": "Pomeranian",
+ "108": "Chow",
+ "109": "Keeshond",
+ "110": "Brabancon Griffon",
+ "111": "Pembroke",
+ "112": "Cardigan",
+ "113": "Toy Poodle",
+ "114": "Miniature Poodle",
+ "115": "Standard Poodle",
+ "116": "Mexican Hairless",
+ "117": "Dingo",
+ "118": "Dhole",
+ "119": "African Hunting Dog"}

App/requirements.txt

@@ -0,0 +1,5 @@
+numpy
+pandas
+streamlit
+tensorflow
+

pechpoints_weights/checkpoint

@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint.ckpt"
+all_model_checkpoint_paths: "checkpoint.ckpt"

pechpoints_weights/checkpoint.ckpt.data-00000-of-00001

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requirements.txt

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+numpy
+streamlit
+tensorflow

saved_models/FerNetEfficientNetB2/keras_metadata.pb

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saved_models/FerNetEfficientNetB2/saved_model.pb

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saved_models/FerNetEfficientNetB2/variables/variables.data-00000-of-00001

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saved_models/TsinghuaFerNet_EfficientNetB2/saved_model.pb

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