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import streamlit as st
import cv2
import numpy as np
import joblib
import mediapipe as mp
from PIL import Image
import os
import tempfile
# Load the trained MLP Classifier model
model = joblib.load('mlp_model.joblib')
# Loading the class dictionary
class_names = {i: str(i) for i in range(10)} # For numbers 0-9
class_names.update({10 + i: chr(97 + i) for i in range(26)}) # For letters a-z
# Initialize MediaPipe Hand model
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(static_image_mode=True, max_num_hands=1, min_detection_confidence=0.5)
def skeletal_image(image_path, shape=(256, 256, 3)):
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image_rgb)
if not results.multi_hand_landmarks:
return None
white_background = np.ones(shape, dtype=np.uint8) * 255
for hand_landmarks in results.multi_hand_landmarks:
mp.solutions.drawing_utils.draw_landmarks(white_background, hand_landmarks, mp_hands.HAND_CONNECTIONS)
white_background_bgr = cv2.cvtColor(white_background, cv2.COLOR_RGB2BGR)
return white_background_bgr
# Function to extract landmarks from an uploaded image
def extract_landmarks(image_path):
image = cv2.imread(image_path)
results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
if results.multi_hand_landmarks:
# Extract landmarks
landmarks = np.array([[lm.x, lm.y, lm.z] for lm in results.multi_hand_landmarks[0].landmark]).flatten()
return landmarks
return None
# Streamlit app
st.title('Hand Gesture Recognition')
# Option for the user to choose the input method
input_method = st.radio("Choose the input method:", ("Upload an Image", "Use Webcam"))
if input_method == "Upload an Image":
uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
uploaded_image = Image.open(uploaded_file).convert('RGB')
elif input_method == "Use Webcam":
uploaded_file = st.camera_input("Take a picture")
if uploaded_file is not None:
uploaded_image = Image.open(uploaded_file).convert('RGB')
if uploaded_file is not None:
st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
# Save the uploaded or captured image to a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmpfile:
uploaded_image.save(tmpfile, format="JPEG")
tmpfile_path = tmpfile.name
try:
skeletal_img = skeletal_image(tmpfile_path)
if skeletal_img is not None:
st.image(skeletal_img, channels="BGR", caption='This processed image contains your hand landmarks')
processed_image = extract_landmarks(tmpfile_path)
if processed_image is not None:
with st.spinner('Please wait, while the model predicts...'):
# Reshape the processed_image for the model if necessary
processed_image = processed_image.reshape(1, -1) # Reshape if your model expects a specific input shape
predictions = model.predict(processed_image)
predicted_class_name = class_names[predictions[0]]
# Display the prediction
st.write(f"The predicted ASL sign seems to be {predicted_class_name.upper()}")
else:
st.write("No hand landmarks were detected.")
finally:
# Ensure the temporary file is deleted even if an error occurs
os.remove(tmpfile_path)
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