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app.py

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+import streamlit as st
+import numpy as np
+import pickle
+import pandas as pd
+from ultralytics import YOLO
+import os
+import glob
+import cv2
+from statistics import mode
+
+
+letter_map={0: 'അ', 1: 'ആ', 2: 'ച', 3: 'ഇ', 4: 'ജ', 5: 'ക', 6: 'ല', 7: 'ൾ', 8: 'ണ', 9: 'ന', 10: 'പ', 11: 'ര', 12: 'ർ', 13: 'റ', 14: 'സ', 15: 'ട', 16: 'ത', 17: 'വ', 18: 'യ', 19: 'ഴ'}
+
+st.header('Sign Detection')
+model = YOLO(r"C:\Users\hp\OneDrive\Desktop\mini pro\mini\best.onnx",task='detect')
+
+
+
+def extract_frames(uploaded_file, output_folder):
+    input_name = os.path.splitext(uploaded_file.name)[0]
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+
+    content = uploaded_file.getvalue()
+
+    temp_file_path = os.path.join(output_folder, "temp_video.mp4")
+    with open(temp_file_path, "wb") as temp_file:
+        temp_file.write(content)
+
+    video_capture = cv2.VideoCapture(temp_file_path)
+    frame_count = 0
+
+    while True:
+        ret, frame = video_capture.read()
+        if not ret:
+            break
+        frame_filename = os.path.join(output_folder, f"{input_name}-{frame_count:04d}.jpg")
+        cv2.imwrite(frame_filename, frame)
+        frame_count += 1
+    video_capture.release()
+    st.write(f"Extracted {frame_count} frames from uploaded video to {output_folder}")
+    os.remove(temp_file_path)
+
+
+uploaded_file = st.file_uploader("Choose a video file", type=["mp4"])
+if uploaded_file is not None:
+    st.video(uploaded_file)
+    st.text("Uploaded Video")
+
+    if st.button("Submit"):
+        output_folder = r"C:\Users\hp\OneDrive\Desktop\mini pro\mini\output"
+        files = glob.glob(os.path.join(output_folder, '*'))
+        for file in files:
+            os.remove(file)
+        extract_frames(uploaded_file, output_folder)
+
+        
+        preds=[]
+        t=[]
+        cl = []
+        previous_element = None
+        word=''
+
+        for file in sorted(os.listdir(output_folder)):
+            frame=os.path.join(output_folder,file)
+            print(frame)
+            res=model.predict(frame)
+            try:
+                preds.append(int(res[0].boxes.data[0][-1]))
+            except:
+                preds.append(-1)
+        
+        st.write(preds)
+
+
+        if len(preds)%25:
+            a=0
+            b=a+25
+            for i in range((len(preds)//25)+1):
+                t.append(mode(preds[a:b]))
+                a,b=b,b+25
+        elif len(preds)%25:
+            a=0
+            b=a+25
+            for i in range(len(preds)//25):
+                t.append(mode(preds[a:b]))
+                a,b=b,b+25
+
+
+        for element in t:
+                if element != -1:
+                    if element != previous_element:
+                        cl.append(element)
+                previous_element = element
+
+
+        for i in cl:
+            word+=letter_map[i]
+        st.write(word)
+
+        
+
+

best.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dbe0feb4b15b63c6c536cf954c764ddfab61fcfdca20761f19cf0ebe4cbd4cc4
+size 103633604