Upload 6 files

activity_recognition.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dbceddfb0b6e54dafd62e7a06831659633db33694bf3859f32b8639af75cd38
+size 44792360

activity_recognition2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:13a14c7b271bb053fefc262a231e94ed8210218120a6aba936d164140efea524
+size 18776384

classes.txt

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+1 ApplyEyeMakeup
+2 ApplyLipstick
+3 Archery
+4 BabyCrawling
+5 BalanceBeam
+6 BandMarching
+7 BaseballPitch
+8 Basketball
+9 BasketballDunk
+10 BenchPress
+11 Biking
+12 Billiards
+13 BlowDryHair
+14 BlowingCandles
+15 BodyWeightSquats
+16 Bowling
+17 BoxingPunchingBag
+18 BoxingSpeedBag
+19 BreastStroke
+20 BrushingTeeth
+21 CleanAndJerk
+22 CliffDiving
+23 CricketBowling
+24 CricketShot
+25 CuttingInKitchen
+26 Diving
+27 Drumming
+28 Fencing
+29 FieldHockeyPenalty
+30 FloorGymnastics

main.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Dec 11 19:31:04 2023
+
+@author: Pranav
+"""
+
+import streamlit as st
+import os
+import cv2
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow import keras
+from keras.models import load_model
+from utils import eval_real,load_video
+import tempfile
+
+st.title('Action Recognition video spliter')
+st.header('Please Upload a Video')
+
+file = st.file_uploader('',type=['mp4'])
+isresnet = st.button("Load Resnet Model")
+iscnnlstm = st.button("Load CNN-LSTM Model")
+
+label_data = pd.read_csv("classes.txt", sep = ' ', header = None)
+label_data.columns = ['index','labels']
+classes = label_data['labels']
+
+if file is not None :
+    video_bytes = file.read()
+    st.video(video_bytes) 
+    
+    with tempfile.NamedTemporaryFile(dir='.') as f:
+        f.write(file.getbuffer())
+
+        model = load_model('activity_recognition.h5',compile = False)
+
+        if iscnnlstm == True :
+            model = load_model('activity_recognition2.h5',compile = False)
+
+        images = load_video(f.name)
+        
+        edited_img = []
+        count = 0
+        for count in range(len(images[0]) - 15):
+            imgs = images[0][count:count+15]
+            edited_img.append(imgs)
+            count += 1
+        edited_imgs = np.array(edited_img)
+        images_arr = np.array(images)
+
+        model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
+        pred = []
+        with st.spinner('Wait for it...'):
+            for img in edited_imgs:
+                img = img.reshape(1,15,64,64,3)
+                prediction = eval_real(img,model)
+                pred.append(prediction)
+        st.success('Done!')
+
+        names = []
+        start = []
+        end = []
+        start.append(0)
+        names.append(classes[pred[0]])
+        for i in range(len(pred)-1):
+            if pred[i] != pred[i+1] :
+                names.append(classes[pred[i]])
+                if len(end) != 0:
+                    end.pop()
+                end.append(i)
+                start.append(i+1)
+                end.append(i+1)
+            else :
+                if len(end) != 0:
+                    end.pop()
+                end.append(i)
+
+        for j in range(len(end)):
+            if j < len(names) :
+                name = names[j]
+            else: name = names[j-1]
+            if start[j] - end[j] != 0:
+                st.write('Video ',j,' Start: ',start[j],' End: ',end[j],name)
+            else:
+                st.write('Video',j,'Insignificant Change')

requirements.txt

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+keras==2.12.0
+numpy==2.0.0
+opencv_python==4.9.0.80
+pandas==2.2.2
+streamlit==1.33.0
+tensorflow==2.12.0
+tensorflow_intel==2.12.0

utils.py

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+import cv2
+import numpy as np
+
+
+def video_Frames(clip_path,img_size = 64):
+    video = cv2.VideoCapture(clip_path)
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    for count in range(frame_count):
+        flag, frame = video.read()
+        if not flag:
+            break
+        frame = cv2.resize(frame,(img_size,img_size))
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 
+        #normalizing the pixels between 0 and 1
+        frame = frame/255.0
+        yield frame 
+    video.release()
+
+
+def load_video(folder_path):
+    imgs = []
+    frames_generator = video_Frames(folder_path)
+    frames_array = np.array(list(frames_generator))
+    imgs.append(frames_array)
+    real_imgs = np.array(imgs)
+
+    return imgs
+
+
+def eval_real(real_imgs, model):    
+    pred1 = model.predict(real_imgs)
+    pred1_max = pred1.argmax()
+
+    return pred1_max