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import streamlit as st
from PIL import Image
import cv2
import numpy as np
import subprocess
import shutil
import os
import torch
import time
import streamlit_analytics
def clear_detect_directory():
detect_directory = "yolov5/runs/detect"
if os.path.exists(detect_directory):
shutil.rmtree(detect_directory)
os.makedirs(detect_directory)
def save_image():
st.title("Hand Sign Detection")
col1, col2 = st.columns(2) # 2 for two col
pd_df = None
with col1:
genre = st.radio(
"Upload Your Hand Sign",
('Browse', 'Camera'))
if genre == 'Camera':
uploaded_image = st.camera_input("Take a picture")
else:
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_image is not None:
# Convert the image to a format compatible with PIL and OpenCV
pil_image = Image.open(uploaded_image)
opencv_image = np.array(pil_image)
opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)
# Provide a file path to save the image
upload_image_path = "processed_image.jpg"
cv2.imwrite(upload_image_path, opencv_image)
st.text("Detection class probabilities")
st.success(f"Processing Image as {upload_image_path}")
# label of image:
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt') # local model
results = model(upload_image_path)
pd_df = (results.pandas().xyxy[0])
# max_confidence_name = pd_df.loc[pd_df['confidence'].idxmax(), 'name']
clear_detect_directory()
command = [
"python",
"yolov5/detect.py",
"--weights", "best.pt",
"--img", "416",
"--conf", "0.50",
"--source", upload_image_path
]
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
std_out, std_err = process.communicate()
if process.returncode != 0:
error_message = f"Error: {std_err}"
st.text(error_message)
st.text(pd_df)
with col2:
detect_image_pred = "yolov5/runs/detect/exp/processed_image.jpg"
if os.path.exists(detect_image_pred):
st.text("Detected Gesture")
st.image(detect_image_pred, caption="Detected Image", use_column_width=True)
else:
st.text("Detection Threshold is 60")
st.text("Detection Gesture")
st.text("Note: Detection model is train on limited Gestures (as shown in example image) Make clean Gesture if not detected try another Gesture")
st.image("Untitled_img.png")
streamlit_analytics.start_tracking()
save_image()
streamlit_analytics.stop_tracking()
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