Upload app.py

app.py

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+# To run use
+# $ streamlit run yolor_streamlit_demo.py
+
+from yolo_v7 import names, load_yolov7_and_process_each_frame
+
+import tempfile
+import cv2
+
+from models.models import *
+from utils.datasets import *
+from utils.general import *
+import streamlit as st
+
+
+def main():
+    
+    #title
+    st.title('Object Tracking Dashboard YOLOv7-tiny')
+    
+    #side bar title
+    st.sidebar.title('Settings')
+    
+    st.markdown(
+    """
+    <style>
+    [data-testid="stSidebar"][aria-expanded="true"] > div:first-child {
+        width: 350px;
+    }
+    [data-testid="stSidebar"][aria-expanded="false"] > div:first-child {
+        width: 350px;
+        margin-left: -350px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+    )
+
+    use_webcam = st.sidebar.checkbox('Use Webcam')
+
+    st.sidebar.markdown('---')
+    confidence = st.sidebar.slider('Confidence',min_value=0.0, max_value=1.0, value = 0.25)
+    st.sidebar.markdown('---')
+
+    save_img = st.sidebar.checkbox('Save Video')
+    enable_GPU = st.sidebar.checkbox('enable GPU')
+
+    custom_classes = st.sidebar.checkbox('Use Custom Classes')
+    assigned_class_id = []
+    if custom_classes:
+        assigned_class = st.sidebar.multiselect('Select The Custom Classes',list(names),default='person')
+        for each in assigned_class:
+            assigned_class_id.append(names.index(each))
+
+    video_file_buffer = st.sidebar.file_uploader("Upload a video", type=[ "mp4", "mov",'avi','asf', 'm4v' ])
+
+    DEMO_VIDEO = 'test.mp4'
+
+    tfflie = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
+
+
+    ##We get our input video here
+
+    if not video_file_buffer:
+        if use_webcam:
+            vid = cv2.VideoCapture(0, cv2.CAP_ARAVIS)
+            tfflie.name = 0
+        else:
+            vid = cv2.VideoCapture(DEMO_VIDEO)
+            tfflie.name = DEMO_VIDEO
+            dem_vid = open(tfflie.name,'rb')
+            demo_bytes = dem_vid.read()
+    
+            st.sidebar.text('Input Video')
+            st.sidebar.video(demo_bytes)
+
+    else:
+        tfflie.write(video_file_buffer.read())
+        # print("No Buffer")
+        dem_vid = open(tfflie.name,'rb')
+        demo_bytes = dem_vid.read()
+    
+        st.sidebar.text('Input Video')
+        st.sidebar.video(demo_bytes)
+
+
+    print(tfflie.name)
+    # vid = cv2.VideoCapture(tfflie.name)
+    
+    stframe = st.empty()
+    
+    st.markdown("<hr/>", unsafe_allow_html=True)
+    kpi1, kpi2, kpi3 = st.beta_columns(3) #st.columns(3)
+
+    # stframe.image(im0,channels = 'BGR',use_column_width=True)
+
+    with kpi1:
+        st.markdown("**Frame Rate**")
+        kpi1_text = st.markdown("0")
+
+    with kpi2:
+        st.markdown("**Tracked Objects**")
+        kpi2_text = st.markdown("0")
+
+    with kpi3:
+        st.markdown("**Total Count**")
+        kpi3_text = st.markdown("0")
+
+    st.markdown("<hr/>", unsafe_allow_html=True)
+    # call yolor 
+    # load_yolor_and_process_each_frame(tfflie.name, enable_GPU, confidence, assigned_class_id, kpi1_text, kpi2_text, kpi3_text, stframe)
+    load_yolov7_and_process_each_frame('yolov7-tiny', tfflie.name, enable_GPU, save_img, confidence, assigned_class_id, kpi1_text, kpi2_text, kpi3_text, stframe)
+
+    st.text('Video is Processed')
+        
+if __name__ == '__main__':
+    try:
+        main()
+    except SystemExit:
+        pass
+
+