init

.gitignore

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+assets/*
+*.jpg
+beerds.json

beerds.py

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+
+import os
+import random
+
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.utils import image_dataset_from_directory, split_dataset
+
+
+img_size = (200, 200)
+
+
+# обогащение выборки
+data_augmentation = keras.Sequential(
+[
+    layers.RandomFlip("horizontal"),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.2),
+]
+)
+
+
+
+input_dir = "assets/dog"
+
+labels_dict = {}
+for fname in os.listdir(input_dir):
+    if fname in labels_dict:
+        continue
+    labels_dict[fname] = len(labels_dict)
+
+model_name = "beerd_25_04_2023.keras"
+train_dataset, val_ds = image_dataset_from_directory(
+    input_dir,
+    labels="inferred",
+    label_mode="categorical",
+    class_names=None,
+    color_mode="rgb",
+    batch_size=32,
+    seed=12,
+    image_size=img_size,
+    shuffle=True,
+    validation_split=0.1,
+    subset="both",
+    interpolation="bilinear",
+    follow_links=False,
+    crop_to_aspect_ratio=False
+)
+
+validation_dataset, test_dataset = split_dataset(val_ds, left_size=0.8)
+
+inputs = keras.Input(shape=img_size + (3,))
+x = data_augmentation(inputs)
+x = layers.Rescaling(1./255)(x)
+x = layers.Conv2D(filters=32, kernel_size=5, use_bias=False)(x)
+for size in [32, 64, 128, 256, 512, 1024]:
+    residual = x
+    x = layers.BatchNormalization()(x)
+    x = layers.Activation("relu")(x)
+    x = layers.SeparableConv2D(size, 3, padding="same", use_bias=False)(x)
+    x = layers.BatchNormalization()(x)
+    x = layers.Activation("relu")(x)
+    x = layers.SeparableConv2D(size, 3, padding="same", use_bias=False)(x)
+    x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
+    residual = layers.Conv2D(
+    size, 1, strides=2, padding="same", use_bias=False)(residual)
+    x = layers.add([x, residual])
+
+x = layers.GlobalAveragePooling2D()(x)
+x = layers.Dropout(0.5)(x)
+outputs = layers.Dense(len(labels_dict), activation="softmax")(x)
+model = keras.Model(inputs, outputs)
+
+model.compile(optimizer="rmsprop",
+              loss="categorical_crossentropy", metrics=['accuracy'])
+callbacks = [
+    keras.callbacks.ModelCheckpoint(model_name,
+                                    save_best_only=True)
+]
+history = model.fit(train_dataset,
+                    epochs=200,
+                    callbacks=callbacks,
+                    validation_data=validation_dataset,)
+
+epochs = range(1, len(history.history["loss"]) + 1)
+loss = history.history["loss"]
+val_loss = history.history["val_loss"]
+acc = history.history["accuracy"]
+val_acc = history.history["val_accuracy"]
+plt.plot(epochs, acc, "bo", label="Точность на этапе обучения")
+plt.plot(epochs, val_acc, "b", label="Точность на этапе проверки")
+plt.title("Точность на этапах обучения и проверки")
+plt.legend()
+plt.figure()
+plt.plot(epochs, loss, "bo", label="Потери на этапе обучения")
+plt.plot(epochs, val_loss, "b", label="Потери на этапе проверки")
+plt.title("Потери на этапах обучения и проверки")
+plt.legend()
+plt.show()
+
+
+test_model = keras.models.load_model(model_name)
+test_loss, test_acc = test_model.evaluate(test_dataset)
+print(f"Test accuracy: {test_acc:.3f}")

beerds_imagenet.py

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+
+import os
+import random
+
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.utils import image_dataset_from_directory, split_dataset
+
+
+img_size = (180, 180)
+
+conv_base = keras.applications.vgg16.VGG16(
+    weights="imagenet",
+    include_top=False,
+    input_shape=(180, 180, 3))
+conv_base.trainable = False
+
+
+# обогащение выборки
+data_augmentation = keras.Sequential(
+[
+    layers.RandomFlip("horizontal"),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.2),
+])
+
+
+
+input_dir = "assets/dog"
+
+labels_dict = {}
+for fname in os.listdir(input_dir):
+    if fname in labels_dict:
+        continue
+    labels_dict[fname] = len(labels_dict)
+
+model_name = "beerd_imagenet_25_04_2023.keras"
+train_dataset, val_ds = image_dataset_from_directory(
+    input_dir,
+    labels="inferred",
+    label_mode="categorical",
+    class_names=None,
+    color_mode="rgb",
+    batch_size=32,
+    seed=12,
+    image_size=img_size,
+    shuffle=True,
+    validation_split=0.1,
+    subset="both",
+    interpolation="bilinear",
+    follow_links=False,
+    crop_to_aspect_ratio=False
+)
+
+validation_dataset, test_dataset = split_dataset(val_ds, left_size=0.8)
+
+inputs = keras.Input(shape=(180, 180, 3))
+x = data_augmentation(inputs)
+x = keras.applications.vgg16.preprocess_input(x)
+x = conv_base(x)
+x = layers.Flatten()(x)
+x = layers.Dense(512)(x)
+x = layers.Dropout(0.5)(x)
+outputs = layers.Dense(len(labels_dict), activation="softmax")(x)
+model = keras.Model(inputs, outputs)
+
+model.compile(optimizer="rmsprop",
+              loss="categorical_crossentropy", metrics=['accuracy'])
+callbacks = [
+    keras.callbacks.ModelCheckpoint(model_name,
+                                    save_best_only=True)
+]
+history = model.fit(train_dataset,
+                    epochs=100,
+                    callbacks=callbacks,
+                    validation_data=validation_dataset,)
+
+epochs = range(1, len(history.history["loss"]) + 1)
+loss = history.history["loss"]
+val_loss = history.history["val_loss"]
+acc = history.history["accuracy"]
+val_acc = history.history["val_accuracy"]
+plt.plot(epochs, acc, "bo", label="Точность на этапе обучения")
+plt.plot(epochs, val_acc, "b", label="Точность на этапе проверки")
+plt.title("Точность на этапах обучения и проверки")
+plt.legend()
+plt.figure()
+plt.plot(epochs, loss, "bo", label="Потери на этапе обучения")
+plt.plot(epochs, val_loss, "b", label="Потери на этапе проверки")
+plt.title("Потери на этапах обучения и проверки")
+plt.legend()
+plt.show()
+
+
+test_model = keras.models.load_model(model_name)
+test_loss, test_acc = test_model.evaluate(test_dataset)
+print(f"Test accuracy: {test_acc:.3f}")

beerds_val.py

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+
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+import json
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.utils import load_img, img_to_array
+from tensorflow.keras.utils import image_dataset_from_directory
+
+
+# model_name = "beerd_25_04_2023.keras"
+model_name = "beerd_imagenet_25_04_2023.keras"
+img = load_img("photo_2023-04-25_10-02-25.jpg", color_mode="rgb")
+img = tf.image.resize(img, (180, 180, ), "bilinear")
+img_array = img_to_array(img)
+
+test_model = keras.models.load_model(model_name)
+test_loss = test_model.predict(np.expand_dims(img_array, 0))
+
+
+list_labels = [fname for fname in os.listdir("assets/dog")]
+list_labels.sort()
+dict_names = {}
+for i, label in enumerate(list_labels):
+    dict_names[i] = label
+
+with open("beerds.json", "w") as  f:
+    f.write(json.dumps(dict_names))
+
+max_val = 0
+max_num = 0
+for i, val in enumerate(test_loss[0]):
+    if val < max_val:
+        continue
+    max_val = val
+    max_num = i
+
+print("-----------------------")
+print(list_labels)
+print(test_loss)
+print(max_num, max_val, dict_names[max_num])

server/Dockerfile

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+FROM python:3.10
+
+COPY requirements.txt /app/requirements.txt
+WORKDIR /app
+
+RUN pip3 install --upgrade pip
+RUN pip3 install -r requirements.txt
+
+COPY . /app/
+EXPOSE 4150
+WORKDIR /app/external_api
+
+ENTRYPOINT ["python", "main.py"]
+

server/index.html

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+<!DOCTYPE html>
+<html lang="ru">
+  <head>
+    <meta charset="UTF-8">
+    <title>Определение породы собаки по фото</title>
+  </head>
+  <body>
+    <form enctype="multipart/form-data" method="post" action="/beeds"  onsubmit="SavePhoto();return false">
+        <p><input type="file" name="f" id="file-input">
+        <input type="submit" value="Отправить"></p>
+       </form> 
+       <div id="result"></div>
+       <img id="image" />
+  </body>
+  <script src="static/scripts.js"></script>
+
+</html>

server/main.py

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+
+
+from PIL import Image
+from sanic import Sanic
+from sanic.response import json as json_answer
+import numpy as np
+from tensorflow import keras
+from tensorflow.keras.utils import img_to_array
+import io
+import os
+import json
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+
+app = Sanic("Ai")
+model_name = "../beerd_imagenet_25_04_2023.keras"
+test_model_imagenet = keras.models.load_model(model_name)
+
+model_name = "../beerd_25_04_2023.keras"
+test_model = keras.models.load_model(model_name)
+
+list_labels = [fname for fname in os.listdir("../assets/dog")]
+list_labels.sort()
+dict_names = {}
+with open("beerds.json", "r") as f:
+    dict_names = json.loads(f.read())
+app.static("/", "index.html", name="main")
+app.static("/static/", "static/", name="static")
+
+
+@app.post("/beeds")
+async def beeds(request):
+    body = request.files.get("f").body
+
+    img = Image.open(io.BytesIO(body))
+    img = img.convert('RGB')
+
+    img_net = img.resize((180, 180, ), Image.BILINEAR)
+    img_array = img_to_array(img_net)
+    test_loss_image_net = test_model_imagenet.predict(
+        np.expand_dims(img_array, 0))
+
+    img = img.resize((200, 200, ), Image.BILINEAR)
+    img_array = img_to_array(img)
+    test_loss = test_model.predict(np.expand_dims(img_array, 0))
+
+    result = {}
+    for i, val in enumerate(test_loss[0]):
+        if val <= 0.09:
+            continue
+        result[val] = dict_names[str(i)]
+
+    result_net = {}
+    for i, val in enumerate(test_loss_image_net[0]):
+        if val <= 0.09:
+            continue
+        result_net[val] = dict_names[str(i)]
+
+    return json_answer({
+        "results": dict(sorted(result.items(), reverse=True)),
+        "results_net": dict(sorted(result_net.items(), reverse=True)),
+    })
+
+if __name__ == "__main__":
+    app.run(auto_reload=True)

server/requirements.txt

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+
+keras==2.12.0
+matplotlib==3.7.1
+numpy==1.23.5
+pandas==2.0.0
+Pillow==9.4.0
+sanic==23.3.0
+scipy==1.10.1
+tensorflow==2.12.0
+ujson==5.7.0

server/static/scripts.js

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+async function SavePhoto() 
+{
+    let photo = document.getElementById("file-input").files[0];
+    let formData = new FormData();
+         
+    formData.append("f", photo);
+    let response = await fetch('/beeds', {method: "POST", body: formData});
+    if (response.ok) { 
+        let json = await response.json();
+        var text = ""
+        for (let key in json.results_net) {
+          text += "<div>" + json.results_net[key] + ": " + key + "</div>"
+        }
+        for (let key in json.results) {
+          text += "<div>" + json.results[key] + ": " + key + "</div>"
+        }
+        document.getElementById("result").innerHTML = text;
+        let urlCreator = window.URL || window.webkitURL;
+        let imageUrl = urlCreator.createObjectURL(photo);
+        document.getElementById("image").src = imageUrl;
+      } else {
+        alert("Ошибка HTTP: " + response.status);
+      }
+}