Update prototype_static_test.py

prototype_static_test.py

@@ -1,91 +1,91 @@
-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('Static_Detection/saved_models/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)
-
-
+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)
+
+