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LRCN_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:08f4c257915c46e805cd6bd83ade5612975ba3d832f4271f9362af748e27a67f
+size 946728

app.py

@@ -1,2 +1,37 @@
 import streamlit as st
-st.text("hello")
+from youtube_downloader import Download
+from collections import deque
+from prediction import Predict
+
+downld=Download()
+
+st.title("Human Action Recognization [DL]")
+with st.expander("Details", expanded=False):
+    st.write('''
+        The chart above shows some numbers I picked for you.
+        I rolled actual dice for these, so they're *guaranteed* to
+        be random''')
+    
+
+url= st.text_input("Insert Youtube Url as instructed above ")
+
+if st.button("submit"):
+    title, output_dir=downld.youtube_d(url)
+    col1, col2 = st.columns([2, 2])
+
+    with col1:
+        st.write(f"Video [{title}] downloaded successfully!")
+        print(output_dir)
+        st.video(f"{output_dir}/{title}.mp4")
+
+    with col2:
+        st.write("wait while model is performing its task")
+        pred =Predict()
+        frames_needed=25
+        input_path=f"{output_dir}/{title}.mp4"
+        output_path=f"test_videos/{title}_output.mp4"
+        pred.prediction(input_path,output_path ,frames_needed)
+
+        # st.video(f"test_videos/{title}.mp4")
+
+

prediction.py

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+import cv2
+import numpy as np
+import math
+from collections import deque
+import tensorflow as tf
+from tensorflow import keras
+from collections import OrderedDict
+
+
+from keras.models import load_model
+
+LCRN_model = load_model('LRCN_model.h5')
+
+class Predict:
+
+    def __init__(self) :
+        pass
+
+       
+    def prediction(self,input_path, output_path,frames_needed):
+        video_reader=cv2.VideoCapture(input_path)
+
+        original_video_width=int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
+        original_video_hight=int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        video_writer=cv2.VideoWriter(output_path,cv2.VideoWriter_fourcc("M","P","4","V"),video_reader.get(cv2.CAP_PROP_FPS),(original_video_width,original_video_hight))
+        frames_deque=deque(maxlen=frames_needed)
+
+        predicted_class_name=""
+        IMAGE_HEIGHT , IMAGE_WIDTH = 64, 64
+        classes_to_be_used=["HorseRace","BenchPress","PullUps","PushUps","HorseRiding","HighJump","Swing"]
+
+        while video_reader.isOpened():
+
+            ok, frame= video_reader.read()
+
+            if not ok:
+                break
+
+            resized_frame=cv2.resize(frame,(IMAGE_HEIGHT,IMAGE_WIDTH))
+
+            normalized_frame= resized_frame/255
+
+            frames_deque.append(normalized_frame)
+
+            if len(frames_deque)==frames_needed:
+
+                predicted_labels_probabilities= LCRN_model.predict(np.expand_dims(frames_deque,axis=0)[0])
+
+                predicted_label=np.argmax(predicted_labels_probabilities)
+
+                predicted_class_name=classes_to_be_used[predicted_label]
+
+                cv2.putText(frame,predicted_class_name,(10,30),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
+
+                video_writer.write(frame)
+
+        video_reader.release()
+        video_writer.release()

requirements.txt

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+streamlit
+!pip install pytube@git+https://github.com/priyankaj1311/pytube.git@master_copy
+
+opencv-python

youtube_downloader.py

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+from pytube import YouTube
+import streamlit as st
+import os
+
+# YouTube video URL
+# video_url = 'https://youtu.be/7zJvNZVZMew'
+
+# Set the output directory
+
+
+# # Choose the highest resolution stream (usually the first one)
+# video_stream = yt.streams.get_highest_resolution()
+
+# # Download the video
+# video_stream.download(output_path=output_dir)
+
+# print("Video downloaded successfully!")
+
+class Download:
+    def __init__(self):
+        pass
+
+    def youtube_d(self,url):
+        self.url = url
+        os.makedirs('test_videos',exist_ok=True)
+        output_dir = "test_videos" 
+        print(url)
+        yt = YouTube(url)
+        video_stream = yt.streams.get_highest_resolution()
+        video_stream.download(output_path=output_dir)
+
+        return yt.title, output_dir
+    
+
+
+