Upload 11 files

.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
+data/sample_images/2.jpg filter=lfs diff=lfs merge=lfs -text
+data/uploaded_data/upload.avi filter=lfs diff=lfs merge=lfs -text
+data/uploaded_data/upload.mp4 filter=lfs diff=lfs merge=lfs -text

Procfile

@@ -0,0 +1 @@
+  web: sh setup.sh && streamlit run app.py

app.py

@@ -0,0 +1,210 @@
+import glob
+import streamlit as st
+import wget
+from PIL import Image
+import torch
+import cv2
+import os
+import time
+
+st.set_page_config(layout="wide")
+
+cfg_model_path = 'models/yolov5s.pt'
+model = None
+confidence = .25
+
+
+def image_input(data_src):
+    img_file = None
+    if data_src == 'Sample data':
+        # get all sample images
+        img_path = glob.glob('data/sample_images/*')
+        img_slider = st.slider("Select a test image.", min_value=1, max_value=len(img_path), step=1)
+        img_file = img_path[img_slider - 1]
+    else:
+        img_bytes = st.sidebar.file_uploader("Upload an image", type=['png', 'jpeg', 'jpg'])
+        if img_bytes:
+            img_file = "data/uploaded_data/upload." + img_bytes.name.split('.')[-1]
+            Image.open(img_bytes).save(img_file)
+
+    if img_file:
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(img_file, caption="Selected Image")
+        with col2:
+            img = infer_image(img_file)
+            st.image(img, caption="Model prediction")
+
+
+def video_input(data_src):
+    vid_file = None
+    if data_src == 'Sample data':
+        vid_file = "data/sample_videos/sample.mp4"
+    else:
+        vid_bytes = st.sidebar.file_uploader("Upload a video", type=['mp4', 'mpv', 'avi'])
+        if vid_bytes:
+            vid_file = "data/uploaded_data/upload." + vid_bytes.name.split('.')[-1]
+            with open(vid_file, 'wb') as out:
+                out.write(vid_bytes.read())
+
+    if vid_file:
+        cap = cv2.VideoCapture(vid_file)
+        custom_size = st.sidebar.checkbox("Custom frame size")
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        if custom_size:
+            width = st.sidebar.number_input("Width", min_value=120, step=20, value=width)
+            height = st.sidebar.number_input("Height", min_value=120, step=20, value=height)
+
+        fps = 0
+        st1, st2, st3 = st.columns(3)
+        with st1:
+            st.markdown("## Height")
+            st1_text = st.markdown(f"{height}")
+        with st2:
+            st.markdown("## Width")
+            st2_text = st.markdown(f"{width}")
+        with st3:
+            st.markdown("## FPS")
+            st3_text = st.markdown(f"{fps}")
+
+        st.markdown("---")
+        output = st.empty()
+        prev_time = 0
+        curr_time = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                st.write("Can't read frame, stream ended? Exiting ....")
+                break
+            frame = cv2.resize(frame, (width, height))
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            output_img = infer_image(frame)
+            output.image(output_img)
+            curr_time = time.time()
+            fps = 1 / (curr_time - prev_time)
+            prev_time = curr_time
+            st1_text.markdown(f"**{height}**")
+            st2_text.markdown(f"**{width}**")
+            st3_text.markdown(f"**{fps:.2f}**")
+
+        cap.release()
+
+from roboflow import Roboflow
+rf = Roboflow(api_key="BSImkKkNh25lMbRDYAAC")
+project = rf.workspace().project("ambulances-model")
+modelA = project.version(5).model
+def infer_image(img, size=None):
+    model.conf = confidence
+    result = model(img, size=size) if size else model(img)
+    abc=result.pandas().xyxy[0]
+    tdd=abc['name'].value_counts()
+    annos=tdd.to_string()
+    #print(str(result)[20:69])
+    result.render()
+    image = Image.fromarray(result.ims[0])
+    image.save("abc.jpg")
+    modelA.predict("abc.jpg", confidence=40, overlap=30).save("abc.jpg")
+    image=cv2.cvtColor(cv2.imread("abc.jpg"), cv2.COLOR_BGR2RGB) 
+    y0, dy = 50, 50
+    for i, line in enumerate(annos.split('\n')):
+        y = y0 + i*dy
+        image=cv2.putText(image, line, (10, y ), cv2.FONT_HERSHEY_SIMPLEX,2,(255,255,0),10)
+    return image
+
+
+@st.experimental_singleton
+def load_model(path, device):
+    model_ = torch.hub.load('ultralytics/yolov5', 'custom', path=path, force_reload=True)
+    model_.to(device)
+    print("model to ", device)
+    return model_
+
+
+@st.experimental_singleton
+def download_model(url):
+    model_file = wget.download(url, out="models")
+    return model_file
+
+
+def get_user_model():
+    model_src = st.sidebar.radio("Model source", ["file upload", "url"])
+    model_file = None
+    if model_src == "file upload":
+        model_bytes = st.sidebar.file_uploader("Upload a model file", type=['pt'])
+        if model_bytes:
+            model_file = "models/uploaded_" + model_bytes.name
+            with open(model_file, 'wb') as out:
+                out.write(model_bytes.read())
+    else:
+        url = st.sidebar.text_input("model url")
+        if url:
+            model_file_ = download_model(url)
+            if model_file_.split(".")[-1] == "pt":
+                model_file = model_file_
+
+    return model_file
+
+def main():
+    # global variables
+    global model, confidence, cfg_model_path
+
+    st.title("Traffic Management Yolo")
+
+    st.sidebar.title("Options")
+
+    # upload model
+    model_src = st.sidebar.radio("Select yolov5 file", ["Use Yolo"])
+    # URL, upload file (max 200 mb)
+    if model_src == "Use your own model":
+        user_model_path = get_user_model()
+        if user_model_path:
+            cfg_model_path = user_model_path
+
+        st.sidebar.text(cfg_model_path.split("/")[-1])
+        st.sidebar.markdown("---")
+
+    # check if model file is available
+    if not os.path.isfile(cfg_model_path):
+        st.warning("Model not Found", icon="⚠️")
+    else:
+        # device options
+        if torch.cuda.is_available():
+            device_option = st.sidebar.radio("PC type", ['cpu', 'cuda'], disabled=False, index=0)
+        else:
+            device_option = st.sidebar.radio("PC type", ['cpu', 'cuda'], disabled=True, index=0)
+
+        # load model
+        model = load_model(cfg_model_path, device_option)
+
+        # confidence slider
+        confidence = st.sidebar.slider('Confidence', min_value=0.1, max_value=1.0, value=.45)
+
+        # custom classes
+        if st.sidebar.checkbox("Select Classes"):
+            model_names = list(model.names.values())
+            assigned_class = st.sidebar.multiselect("Select Classes", model_names, default=[model_names[0]])
+            classes = [model_names.index(name) for name in assigned_class]
+            model.classes = classes
+        else:
+            model.classes = list(model.names.keys())
+
+        st.sidebar.markdown("---")
+
+        # input options
+        input_option = st.sidebar.radio("Select type: ", ['image', 'video'])
+
+        # input src option
+        data_src = st.sidebar.radio("Select input source: ", ['Sample data', 'Upload your own data'])
+
+        if input_option == 'image':
+            image_input(data_src)
+        else:
+            video_input(data_src)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except SystemExit:
+        pass

data/uploaded_data/upload.avi

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7c7d98162e1ca5a971d29916b47cd15120cfc60f5bc435fb81d8807e254bade
+size 2528436

data/uploaded_data/upload.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b41d9fba9daea828e3281bb5a846b9222b6b75341ed6332b6a7d20256b2fd37c
+size 2505855

models/yolov5s.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b3b748c1e592ddd8868022e8732fde20025197328490623cc16c6f24d0782ee
+size 14808437

requirements.txt

@@ -0,0 +1,19 @@
+streamlit
+ultralytics
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python-headless
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+protobuf<4.21.5  
+pandas>=1.1.4
+seaborn>=0.11.0
+ipython
+psutil 
+thop
+wget

setup.sh

@@ -0,0 +1,11 @@
+mkdir -p ~/.streamlit/
+echo "\
+[general]\n\
+email = \"[email protected]\"\n\
+" > ~/.streamlit/credentials.toml
+echo "\
+[server]\n\
+headless = true\n\
+enableCORS=false\n\
+port = $PORT\n\
+" > ~/.streamlit/config.toml