Create 2_📷_Webcamera.py

pages/2_📷_Webcamera.py

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+import streamlit as st
+from streamlit_webrtc import webrtc_streamer
+import av
+import cv2
+import time
+import mediapipe as mp
+import numpy as np
+import pandas as pd
+from mediapipe_functions import *
+from utils import *
+import tensorflow as tf
+
+st.title("Webcamera")
+st.write("Steps to use: \n1. Click on Start button.\n2. To stop the video when done, press Stop. \n\n The output will be displayed in about 40 secs.")
+
+class VideoProcessor:
+    def __init__(self) -> None:
+        self.threshold1 = 100
+        self.threshold2 = 200
+        self.my_list = []
+
+    def recv(self, frame):
+        img = frame.to_ndarray(format="bgr24")
+        self.my_list.append(img)
+        return av.VideoFrame.from_ndarray(img, format="bgr24")
+
+# Create the video processor instance
+video_processor = VideoProcessor()
+
+ctx = webrtc_streamer(key="sample",  video_processor_factory=lambda: video_processor)
+
+time.sleep(10)
+st.write(len(ctx.video_processor.my_list))
+
+# Access the frames list after the webrtc_streamer function has finished running
+frames_list = ctx.video_processor.my_list
+
+# # Display the last frame
+# if frames_list:
+#     st.image(frames_list[-1], channels="BGR")
+st.write("Running...")
+
+# Continuing with the code for inference pipeline
+final_landmarks = extract_landmarks(frames_list)
+df1 = pd.DataFrame(final_landmarks,columns=['x','y','z'])
+ROWS_PER_FRAME = 543
+
+# Loading data
+st.write(len(frames_list))
+test_df = load_relevant_data_subset(df1, ROWS_PER_FRAME=ROWS_PER_FRAME)
+test_df = tf.convert_to_tensor(test_df)
+
+# Inference
+interpreter = tf.lite.Interpreter("models/model.tflite")
+prediction_fn = interpreter.get_signature_runner("serving_default")
+output = prediction_fn(inputs=test_df)
+sign = np.argmax(output["outputs"])
+sign_json=pd.read_json("sign_to_prediction_index_map.json",typ='series')
+sign_df=pd.DataFrame(sign_json)
+sign_df.iloc[sign]
+top_indices = np.argsort(output['outputs'])[::-1][:5]
+top_values = output['outputs'][top_indices]
+
+output_df = sign_df.iloc[top_indices]
+output_df['Value'] = top_values
+output_df.rename(columns = {0:'Index'}, inplace = True)
+st.write(output_df)