Update pages/2_📷_Webcamera.py

pages/2_📷_Webcamera.py

@@ -1,67 +1,73 @@
-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
+# 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.")
 
-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 = []
 
-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")
 
-    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()
 
-# Create the video processor instance
-video_processor = VideoProcessor()
+# ctx = webrtc_streamer(key="sample",  video_processor_factory=lambda: video_processor)
 
-ctx = webrtc_streamer(key="sample",  video_processor_factory=lambda: video_processor)
+# time.sleep(10)
+# st.write(len(ctx.video_processor.my_list))
 
-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
 
-# 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...")
 
-# # 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
 
-# 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)
 
-# 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)
+import streamlit as st
 
-# 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]
+picture = st.camera_input("Take a picture")
 
-output_df = sign_df.iloc[top_indices]
-output_df['Value'] = top_values
-output_df.rename(columns = {0:'Index'}, inplace = True)
-st.write(output_df)
+if picture:
+    st.image(picture)