test

images/testvideo.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7bf1d40b9cd1f19e0eb57656d71df18a37f1d1f2a00e550bf6c6bd82577ccbb5
+size 18275551

main.py

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+import streamlit as st
+
+st.title('Проект по computer vision')
+

packages.txt

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+freeglut3-dev
+libgtk2.0-dev
+libgl1-mesa-glx

pages/2_Brain tumor.py

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+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import io
+import numpy as np
+import PIL
+import os
+import matplotlib.pyplot as plt
+import requests
+from io import BytesIO
+import base64
+
+from torchvision import transforms as T
+model = torch.hub.load(
+    'ultralytics/yolov5', 
+    'custom', 
+    path='weights/braintumor.pt', 
+    )
+
+model.eval()
+model.conf = 0.3 
+st.title('Brain tumor detection')
+
+st.title("Single Detection")
+uploaded_file = st.file_uploader("Загрузите фотографию", type=["png", "jpg", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Загруженное изображение', use_column_width=True)
+
+    if st.button("Определить наличие опухоли"):
+        image.save("ggg.jpg")
+
+        # Image
+        img = 'ggg.jpg'
+        # Inference
+        results = model(img)
+        # results.show()  # or .show(), .save(), .crop(), .pandas(), etc
+        annotated_img = results.render()[0]
+        st.image(annotated_img, caption='Результат', use_column_width=True)
+
+st.title("Multiple Detection")
+uploaded_files = st.file_uploader("Upload your images", accept_multiple_files=True)
+
+images = []
+
+if uploaded_files is not None:
+    for image in uploaded_files:
+        # Преобразуйте файл изображения в объект PIL
+        image = Image.open(image)
+        st.image(image, caption='Original Image', use_column_width=True)
+        images.append(image)
+
+if st.button("Detection (Multiple)"):
+    # Примените модель YOLOv5 к изображению
+    for image in images:
+
+        results = model(image)
+
+        # Отобразите результаты
+        st.image(results.render()[0], caption='Detection Result', use_column_width=True)
+
+
+st.title("Upload image by URL")
+    
+# Ввод URL изображения
+url = st.text_input("Enter image URL:")
+
+
+    
+# При нажатии на кнопку "Загрузить", выводим изображение
+if url:
+    try:
+        if url.startswith("data:image"):
+            # Handle data URI
+            image_data = url.split(',')[1]
+            image_binary = base64.b64decode(image_data)
+            image = Image.open(BytesIO(image_binary))
+        else:
+            response = requests.get(url)
+            image_bytes = response.content
+            image = Image.open(BytesIO(image_bytes))
+
+        st.image(image, caption='Uploaded image', use_column_width=True)
+
+        result = model(image)
+
+        # Отобразите результаты
+        st.image(result.render()[0], caption='Detection Result', use_column_width=True)
+        
+    except Exception as e:
+        st.error("Error: " + str(e))
+
+#if st.button("Detection (URL)"):
+
+    # Inference
+    #result = model(image)
+
+    # Отобразите результаты
+    #st.image(result.render()[0], caption='Detection Result', use_column_width=True)

pages/3_textcleaner.py

@@ -0,0 +1,44 @@
+import streamlit as st
+import matplotlib.pyplot as plt 
+import numpy as np
+import torch
+from PIL import Image
+from weights.model import  ConvAutoencoder
+from weights.preprocessing import preprocess
+from torchvision.io import read_image
+
+@st.cache_resource
+
+def load_model():
+    model = ConvAutoencoder()
+    model.load_state_dict(torch.load('weights/autoencoder.pt', map_location = 'cpu'))
+    return model
+
+DEVICE = 'cpu'
+model = load_model()
+model.to(DEVICE)
+model.eval()
+
+st.title('Denoising images with noise')
+
+loaded_image = st.file_uploader('Загрузите картинку с текстом')
+
+def predict(img):
+    img = preprocess(img)
+    img.to(DEVICE)
+    outputs = model(img.unsqueeze(0))
+    pred = outputs.detach().cpu().squeeze(0).numpy()
+    return pred
+
+if loaded_image:
+    img = Image.open(loaded_image)
+    prediction = predict(img)
+    left_col, right_col = st.columns(2)
+    with left_col:
+        st.write('Original text')
+        st.image(img)
+    with right_col:
+        st.write('Denoised text')
+        st.image(prediction[0])
+
+

pages/Turbine.py

@@ -0,0 +1,63 @@
+from ultralytics import YOLO
+import streamlit as st
+from PIL import Image
+import io
+import os
+import torch
+import torchvision.transforms as transforms
+import requests
+from torchvision import transforms as T
+import base64
+import numpy as np
+
+@st.cache_resource
+
+def load_model(weights_path):
+    model = YOLO(weights_path)
+    return model
+
+model = load_model('weights/turbine.pt')
+model = model.cpu()
+
+st.title('Turbine and cable tower detection')
+
+
+
+image_type = st.radio("Способ загрузки", ["Олд", "Ньюфаг", "Тест"])
+
+if image_type == "Олд":
+    # image = st.file_uploader('Загрузи файл', type=['jpg', 'jpeg', 'png'])
+    uploaded_images = st.file_uploader("Загрузите изображения", type=["jpg", "png"], accept_multiple_files=True)
+    if uploaded_images is not None:
+        for image in uploaded_images:
+            image = Image.open(image)
+            # image_bytes = image.read()
+            # image = Image.open(io.BytesIO(image_bytes))
+            results = model.predict(image)
+            result = results[0]
+            img = Image.fromarray(result.plot()[:, :, ::-1])
+            st.image(img)
+
+
+if image_type == "Ньюфаг":
+    image_url = st.text_input("Введите URL изображения для загрузки")
+    if image_url:
+        if image_url.startswith("data:image"):
+            # Handle data URI
+            image_data = image_url.split(',')[1]
+            image_binary = base64.b64decode(image_data)
+            image = Image.open(io.BytesIO(image_binary))
+        else:
+            response = requests.get(image_url)
+            image_bytes = response.content
+            image = Image.open(io.BytesIO(image_bytes))
+        results = model.predict(image)
+        result = results[0]
+        img = Image.fromarray(result.plot()[:, :, ::-1])
+        st.image(img)
+if image_type == "Тест":
+    video_file = open('images/testvideo.mp4', 'rb')
+    video_bytes = video_file.read()
+
+    st.video(video_bytes)
+

requirements.txt

@@ -0,0 +1,82 @@
+altair==5.1.2
+attrs==23.1.0
+blinker==1.6.3
+cachetools==5.3.2
+certifi==2023.7.22
+charset-normalizer==3.3.1
+click==8.1.7
+contourpy==1.1.1
+cycler==0.12.1
+filelock==3.12.4
+fonttools==4.43.1
+fsspec==2023.10.0
+gitdb==4.0.11
+GitPython==3.1.40
+idna==3.4
+importlib-metadata==6.8.0
+Jinja2==3.1.2
+jsonschema==4.19.1
+jsonschema-specifications==2023.7.1
+kiwisolver==1.4.5
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+matplotlib==3.8.0
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.2
+numpy==1.26.1
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.18.1
+nvidia-nvjitlink-cu12==12.3.52
+nvidia-nvtx-cu12==12.1.105
+opencv-python==4.8.1.78
+opencv-python-headless==4.8.1.78
+packaging==23.2
+pandas==2.1.1
+Pillow==10.1.0
+protobuf==4.24.4
+psutil==5.9.6
+py-cpuinfo==9.0.0
+pyarrow==13.0.0
+pydeck==0.8.1b0
+Pygments==2.16.1
+pyparsing==3.1.1
+python-dateutil==2.8.2
+pytz==2023.3.post1
+PyYAML==6.0.1
+referencing==0.30.2
+requests==2.31.0
+rich==13.6.0
+rpds-py==0.10.6
+scipy==1.11.3
+seaborn==0.13.0
+six==1.16.0
+smmap==5.0.1
+streamlit==1.27.2
+sympy==1.12
+tenacity==8.2.3
+thop==0.1.1.post2209072238
+toml==0.10.2
+toolz==0.12.0
+torch==2.1.0
+torchutils==0.0.4
+torchvision==0.16.0
+tornado==6.3.3
+tqdm==4.66.1
+triton==2.1.0
+typing_extensions==4.8.0
+tzdata==2023.3
+tzlocal==5.2
+ultralytics==8.0.201
+urllib3==2.0.7
+validators==0.22.0
+watchdog==3.0.0
+zipp==3.17.0

weights/autoencoder.pt

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+size 174416

weights/braintumor.pt

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+oid sha256:86c06c82deb20ca5b735d068ac02f5303f8d356febfe33641d2413efd2214be0
+size 14443752

weights/model.py

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+oid sha256:837c9232cadb151a3b12260841da0ace793a6bff16b37e192cadf91d980c93b4
+size 2231

weights/preprocessing.py

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ef682ff4bf4554c52786ff31fc044020280b8d95dbc9a5feb39315137182736
+size 575

weights/turbine.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd0318c891e83f984858570ae9344f78b21a555f68321bae1161ceeff7bca0ab
+size 6257113