.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/misc.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+</project>

README.md

@@ -1,11 +0,0 @@
-Cat detection
-
-Quick guide:
-1. install docker
-2. build image with `./scripts/build_image.bat`
-3. run container with `./scripts/start.bat`
-
-Quick tests guide:
-1. run unit tests `./scripts/run_tests.bat`
-
-Have fun!

basics.ipynb

@@ -0,0 +1,39 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-01-04T16:38:33.550511800Z",
+     "start_time": "2024-01-04T16:38:33.542353Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

cat_detection.py

@@ -1,58 +0,0 @@
-from io import BytesIO
-
-import numpy as np
-from PIL import Image
-from keras.src.applications.resnet import preprocess_input, decode_predictions
-from keras.applications.resnet import ResNet50
-
-
-"""
-    Recognition file.
-    Model is ResNet50. Pretrained model to image recognition.
-    If model recognize cat then returns response with first ten CAT predictions.
-    If first prediction is not a cat then returns False.
-    If prediction is not a cat (is not within list_of_labels) then skips this prediction.
-    Format of response:
-    {
-        'label': {label}
-        'score': {score}
-    }
-"""
-
-
-model = ResNet50(weights='imagenet')
-
-
-# PRIVATE Preprocess image method
-def _preprocess_image(image):
-    try:
-        img = Image.open(BytesIO(image.read()))
-        img = img.resize((224, 224))
-        img_array = np.array(img)
-        img_array = np.expand_dims(img_array, axis=0)
-        img_array = preprocess_input(img_array)
-        return img_array
-    except Exception as e:
-        print(f"Error preprocessing image: {e}")
-        return None
-
-
-# Generate response
-def _generate_response(decoded_predictions, list_of_labels):
-    results = {}
-    for i, (imagenet_id, label, score) in enumerate(decoded_predictions):
-        if i == 0 and label not in list_of_labels:
-            return None
-        if score < 0.01:
-            break
-        if label in list_of_labels:
-            results[len(results) + 1] = {"label": label, "score": round(float(score), 2)}
-    return results
-
-
-# Cat detection
-def detect_cat(image_file, list_of_labels):
-    img_array = _preprocess_image(image_file)
-    prediction = model.predict(img_array)
-    decoded_predictions = decode_predictions(prediction, top=10)[0]
-    return _generate_response(decoded_predictions, list_of_labels)

docker/.dockerignore

@@ -1,6 +0,0 @@
-*.md
-/venv
-.git
-__pycache__
-.pytest_cache
-/tests

docker/Dockerfile

@@ -1,11 +0,0 @@
-FROM python:3.11
-
-WORKDIR /app
-
-COPY . /app
-
-RUN pip install --no-cache-dir -r requirements.txt
-
-EXPOSE 5000
-
-CMD ["python", "main.py"]

docker/docker-compose.yml

@@ -1,10 +0,0 @@
-version: '3.3'
-
-services:
-  cat-detection:
-    image: cat-detection
-    build:
-      context: ../
-      dockerfile: ./docker/Dockerfile
-    ports:
-      - "5000:5000"

docs/basics.md

@@ -1,6 +0,0 @@
-#inzynieriaOprogramowania #semestr3 
-
-# Faza 1 - ogólna architektura projektu
-Aplikacja oparta na micro-frameworku Flask, w której użytkownik przesyła zdjęcie na serwer i w odpowiedzi dostaje na ile % na zdjęciu znajduje się kot (poszczególne przedziały będą miały inną informację typu 100%-95%: "to *prawie* na pewno jest kot" itd.).
-
-Wyniki oceny będą przechowywane w sesji - będą zapisane do momentu wyłączenia przeglądarki, przez co nie ma potrzeby stawiania bazy danych.

docs/docs.md

@@ -1,53 +0,0 @@
-# Api
-
-Port -> 5000
-
-endpoint -> api/v1/detect-cat
-
-Key -> 'Image'
-
-Value -> {UPLOADED_FILE}
-
-Flask Rest API application to cat recognition.
-If request is valid then send response with results of recognition.
-If key named 'Image' in body does not occur then returns 400 (BAD REQUEST).
-Otherwise, returns 200 with results of recognition.
-Format of response:
-```json
-{
-    "lang": "{users_lang}",
-    "results": {
-        "{filename}": {
-            "isCat": "{is_cat}",
-            "results": {
-                "1": "{result}",
-                "2": "{result}",
-                "3": "{result}",
-                "4": "{result}",
-                "5": "{result}",
-                "6": "{result}",
-                "7": "{result}",
-                "8": "{result}",
-                "9": "{result}",
-                "10": "{result}"
-            }
-        }
-    },
-    "errors": [
-        "{error_message}",
-        "{error_message}"
-    ]
-}
-```
-Format of result:
-```json
-    {
-        "label": "{label}",
-        "score": "{score}"
-    }
-```
-
-Example response:
-```json
-
-```

language_label_mapper.py

@@ -1,30 +0,0 @@
-import os
-
-from jproperties import Properties
-
-"""
-    Translator method.
-    If everything fine then returns translated labels.
-    Else throws an Exception and returns untranslated labels.
-"""
-
-
-def translate(to_translate, lang):
-    try:
-        config = Properties()
-        script_directory = os.path.dirname(os.path.abspath(__file__))
-        resources_path = os.path.join(script_directory, "./resources")
-
-        # Load properties file for given lang
-        with open(os.path.join(resources_path, f"./{lang}.properties"), 'rb') as config_file:
-            config.load(config_file, encoding='UTF-8')
-
-        # Translate labels for given to_translate dictionary
-        for index, label_info in to_translate.items():
-            label = label_info.get("label")
-            to_translate[index]["label"] = config.get(label).data
-        return to_translate, []
-    except Exception as e:
-        error_message = f"Error translating labels: {e}"
-        print(error_message)
-        return to_translate, error_message

main.py

@@ -1,108 +1,59 @@
-from flask import Flask, request, Response, json
-from cat_detection import detect_cat
-from language_label_mapper import translate
-from validator import validate
-from flask_cors import CORS
+from PIL import Image
+import torch
+import torch.nn.functional as F
+from torchvision.models.resnet import resnet50, ResNet50_Weights
+from torchvision.transforms import transforms
 
-""" 
-    Flask Rest API application to cat recognition.
-    If request is valid then send response with results of recognition.
-    If key named 'Image' in body does not occurred then returns 400 (BAD REQUEST).
-    Otherwise returns 200 with results of recognition.
-    Format of response:
-        {
-            "lang": {users_lang},
-            "results": {
-                {filename}: {
-                    "isCat": {is_cat},
-                    "results": {
-                        "1": {result}
-                        "2": {result}
-                        "3": {result}
-                        ...
-                        "10" {result}
-                    }
-                },
-                ...
-            },
-            errors[
-                {error_message},
-                {error_message},
-                ...
-            ]
-        }
-    To see result format -> cat_detection.py
-"""
+# Load the pre-trained model
+model = resnet50(weights=ResNet50_Weights.DEFAULT)
+
+model.eval()
+
+# Define the image transformations
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
 
 
-# Define flask app
-app = Flask(__name__)
-app.secret_key = 'secret_key'
-CORS(app)
+def is_cat(image_path):
+    # Open the image
+    img = Image.open(image_path)
 
-# Available cats
-list_of_labels = [
-    'lynx',
-    'lion',
-    'tiger',
-    'cheetah',
-    'leopard',
-    'jaguar',
-    'tabby',
-    'Egyptian_cat',
-    'cougar',
-    'Persian_cat',
-    'Siamese_cat',
-    'snow_leopard',
-    'tiger_cat'
-]
+    # Preprocess the image
+    img_t = preprocess(img)
+    batch_t = torch.unsqueeze(img_t, 0)
 
-# Available languages
-languages = {'pl', 'en'}
+    # Make the prediction
+    out = model(batch_t)
+
+    # Apply softmax to get probabilities
+    probabilities = F.softmax(out, dim=1)
+
+    # Get the maximum predicted class and its probability
+    max_prob, max_class = torch.max(probabilities, dim=1)
+    max_prob = max_prob.item()
+    max_class = max_class.item()
+
+    # Check if the maximum predicted class is within the range 281-285
+    if 281 <= max_class <= 285:
+        return max_class, max_prob
+    else:
+        return max_class, None
 
 
-@app.route('/api/v1/detect-cat', methods=['POST'])
-def upload_file():
-    # Validate request
-    error_messages = validate(request)
-
-    # If any errors occurred, return 400 (BAD REQUEST)
-    if len(error_messages) > 0:
-        errors = json.dumps(
-            {
-                'errors': error_messages
-            }
-        )
-        return Response(errors, status=400, mimetype='application/json')
-
-    # Get files from request
-    files = request.files.getlist('image')
-
-    # Get user's language (Value in header 'Accept-Language'). Default value is English
-    lang = request.accept_languages.best_match(languages, default='en')
-
-    # Define JSON structure for results
-    results = {
-        'lang': lang,
-        'results': {},
-        'errors': []
-    }
-
-    # Generate results
-    for file in files:
-        predictions = detect_cat(file, list_of_labels)
-        if predictions is not None:
-            predictions, error_messages = translate(predictions, lang)
-        results['results'][file.filename] = {
-            'isCat': False if not predictions else True,
-            **({'predictions': predictions} if predictions is not None else {})
-        }
-        if len(error_messages) > 1:
-            results['errors'].append(error_messages)
-
-    # Send response with 200 (Success)
-    return Response(json.dumps(results), status=200, mimetype='application/json')
-
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0')
+image_path = 'wolf.jpg'
+max_class, max_prob = is_cat(image_path)
+translator = {
+    281: "tabby cat",
+    282: "tiger cat",
+    283: "persian cat",
+    284: "siamese cat",
+    285: "egyptian cat"
+}
+if max_prob is not None:
+    print(f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%")
+else:
+    print(f"The image is not recognized as a class within the range 281-285 ({max_class})")

requirements.txt

@@ -1,9 +0,0 @@
-flask==3.0.0
-numpy==1.26.3
-pillow==10.2.0
-keras==2.15.0
-jproperties==2.1.1
-tensorflow==2.15.0
-werkzeug==3.0.1
-pytest==7.4.4
-flask-cors==4.0.0

resources/en.properties

@@ -1,14 +0,0 @@
-# EN
-lynx=lynx
-lion=lion
-tiger=tiger
-cheetah=cheetah
-leopard=leopard
-jaguar=jaguar
-tabby=tabby
-Egyptian_cat=Egyptian cat
-cougar=cougar
-Persian_cat=Persian cat
-Siamese_cat=Siamese cat
-snow_leopard=snow leopard
-tiger_cat=tiger cat

resources/pl.properties

@@ -1,14 +0,0 @@
-# PL
-lynx=ryś
-lion=lew
-tiger=tygrys
-cheetah=gepard
-leopard=lampart
-jaguar=jaguar
-tabby=kot pręgowany
-Egyptian_cat=kot egipski
-cougar=puma
-Persian_cat=kot perski
-Siamese_cat=kot syjamski
-snow_leopard=lampart śnieżny
-tiger_cat=kot tygrysi

scripts/build_image.bat

@@ -1,24 +0,0 @@
-@echo off
-chcp 65001 >nul
-
-docker -v >nul 2>&1
-if %errorlevel% neq 0 (
-    echo Docker is not installed.
-    exit /b 1
-)
-
-docker info >nul 2>&1
-if %errorlevel% neq 0 (
-    echo Docker engine is not running.
-    exit /b 1
-)
-
-cd %~dp0
-echo Building docker image...
-docker-compose -f ../docker/docker-compose.yml build
-if %errorlevel% neq 0 (
-    echo Building docker image failed.
-    exit /b 1
-)
-
-echo The image was built successfully.

scripts/build_image.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-
-export LC_ALL=C.UTF-8
-export LANG=C.UTF-8
-
-docker -v > /dev/null 2>&1
-if [ $? -ne 0 ]; then
-    echo -e "\033[31mDocker is not installed.\033[0m"
-    exit 1
-fi
-
-docker info > /dev/null 2>&1
-if [ $? -ne 0 ]; then
-    echo -e "\033[31mDocker engine is not running.\033[0m"
-    exit 1
-fi
-
-cd ../docker
-echo -e "\033[32mBuilding docker image...\033[0m"
-docker-compose build cat-detection
-
-if [ $? -ne 0 ]; then
-    echo -e "\033[31mBuilding docker image failed.\033[0m"
-    exit 1
-fi
-
-echo -e "\033[32mThe image was built successfully.\033[0m"

scripts/run_tests.bat

@@ -1,12 +0,0 @@
-@echo off
-echo Running unit tests.
-
-cd %~dp0
-pytest ../tests
-
-if %ERRORLEVEL% equ 0 (
-    echo Tests passed successfully.
-) else (
-Tests failed.
-    echo Tests failed.
-)

scripts/start.bat

@@ -1,9 +0,0 @@
-@echo off
-chcp 65001 >nul
-
-cd %~dp0
-docker compose -f ../docker/docker-compose.yml up cat-detection -d
-if %errorlevel% neq 0 (
-    echo Starting docker container failed.
-    exit /b 1
-)

scripts/start.sh

@@ -1,11 +0,0 @@
-#!/bin/bash
-
-export LC_ALL=C.UTF-8
-export LANG=C.UTF-8
-
-docker-compose -f ../docker/docker-compose.yml up cat-detection -d
-
-if [ $? -ne 0 ]; then
-    echo -e "\033[31mStarting docker container failed.\033[0m"
-    exit 1
-fi

tests/test_1.py

@@ -1,19 +0,0 @@
-import os
-
-from werkzeug.datastructures import FileStorage
-
-from main import app
-
-
-def test_upload_file():
-    with app.test_client() as test_client:
-        script_directory = os.path.dirname(os.path.abspath(__file__))
-        image_path = os.path.join(script_directory, "./img/tiger_cat/cat1.jpg")
-        image = FileStorage(
-            stream=open(image_path, "rb"),
-            filename="cat1.jpg",
-            content_type="image/jpeg",
-        )
-
-        response = test_client.post('/api/v1/detect-cat', data={'image': image}, content_type='multipart/form-data')
-        assert response.status_code == 200

validator.py

@@ -1,31 +0,0 @@
-"""
-    Validation method.
-    If everything fine then returns empty list.
-    Else returns list of error messages.
-"""
-
-# Allowed extensions
-allowed_extensions = {'jpg', 'jpeg', 'png'}
-
-
-def validate(request):
-    errors = []
-    try:
-        images = request.files.getlist('image')
-
-        # Case 1 - > request has no 'Image' Key in body
-        if images is None:
-            raise KeyError("'Image' key not found in request.")
-
-        # Case 2 - > if some of the images has no filename
-        if not images or all(img.filename == '' for img in images):
-            raise ValueError("Value of 'Image' key is empty.")
-
-        # Case 3 -> if some of the images has wrong extension
-        for img in images:
-            if not img.filename.lower().endswith(('.png', '.jpg', '.jpeg')):
-                raise ValueError(f"Given file '{img.filename}' has no allowed extension. "
-                                 f"Allowed extensions: {allowed_extensions}.")
-    except Exception as e:
-        errors.append(e.args[0])
-    return errors