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File size: 1,993 Bytes
b035cb5 142281a b035cb5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import os
import sys
import cv2
import numpy as np
import pandas as pd
import streamlit as st
import joblib
SCALER_PATH = "scaler.pkl"
ENCODER_PATH = "le.pkl"
MODEL_PATH = "randomforest.pkl"
scaler = joblib.load(SCALER_PATH)
encoder = joblib.load(ENCODER_PATH)
model = joblib.load(MODEL_PATH)
pred = None
def live_data_pred(cam_input):
bytes_data = cam_input.getvalue()
cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), 0)
resize_img = cv2.resize(cv2_img, (256, 256), interpolation=cv2.INTER_CUBIC)
flattened_img = resize_img.flatten()
flattened_img = np.reshape(flattened_img, (1, -1))
scaled_input = scaler.transform(flattened_img)
prediction = model.predict(scaled_input)
prediction = encoder.inverse_transform(prediction)
return prediction
def pick_gif(prediction):
pass
st.set_page_config(page_title="Driver Posture Alert System", layout='wide')
st.markdown("## Driver Posture Detection and Alert System")
st.markdown(">A user friendly platform Indepedent application to detect the real time posture of the driver and provides appropriate actions")
with st.sidebar:
st.markdown("#### Choose the Input type")
opt = st.selectbox(
"Select the type of input",
("Live camera input", "Captured input"),
index = None
)
if opt == "Live camera input":
cam_input = st.camera_input("Live input")
if cam_input is not None:
pred = live_data_pred(cam_input)
st.write(f"{pred}")
#gif_path = pick_gif(pred)
elif opt == "Captured input":
cam_input = st.file_uploader(
label="Upload captured image",
type= ["png", "jpg", "jpeg"]
)
if cam_input is not None:
pred = live_data_pred(cam_input)
if pred is not None:
st.markdown("### Image Captured - Prediction")
st.write(f"{pred[0].capitalize()}")
else:
st.markdown("#### Waiting for Input...")
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