# import logging # import queue # from pathlib import Path # from typing import List, NamedTuple # import mediapipe as mp # import av # import cv2 # import numpy as np # import streamlit as st # from streamlit_webrtc import WebRtcMode, webrtc_streamer # from sample_utils.turn import get_ice_servers # from cvzone.HandTrackingModule import HandDetector # from cvzone.SelfiSegmentationModule import SelfiSegmentation # import time # import os # logger = logging.getLogger(__name__) # st.title("Interactive Virtual Keyboard with Twilio Integration") # st.info("Use your webcam to interact with the virtual keyboard via hand gestures.") # class Button: # def __init__(self, pos, text, size=[100, 100]): # self.pos = pos # self.size = size # self.text = text # # Initialize components # detector = HandDetector(maxHands=1, detectionCon=0.8) # # segmentor = SelfiSegmentation() # # keys = [["Q", "W", "E", "R", "T", "Y", "U", "I", "O", "P"], # # ["A", "S", "D", "F", "G", "H", "J", "K", "L", ";"], # # ["Z", "X", "C", "V", "B", "N", "M", ",", ".", "/"]] # # listImg = os.listdir('model/street') # # imgList = [cv2.imread(f'model/street/{imgPath}') for imgPath in listImg] # # indexImg = 0 # # # Function to process the video frame from the webcam # # def process_video_frame(frame, detector, segmentor, imgList, indexImg, keys, session_state): # # # Convert the frame to a numpy array (BGR format) # # image = frame.to_ndarray(format="bgr24") # # # Remove background using SelfiSegmentation # # imgOut = segmentor.removeBG(image, imgList[indexImg]) # # # Detect hands on the background-removed image # # hands, img = detector.findHands(imgOut, flipType=False) # # # Create a blank canvas for the keyboard # # keyboard_canvas = np.zeros_like(img) # # buttonList = [] # # # Create buttons for the virtual keyboard based on the keys list # # for key in keys[0]: # # buttonList.append(Button([30 + keys[0].index(key) * 105, 30], key)) # # for key in keys[1]: # # buttonList.append(Button([30 + keys[1].index(key) * 105, 150], key)) # # for key in keys[2]: # # buttonList.append(Button([30 + keys[2].index(key) * 105, 260], key)) # # # Draw the buttons on the keyboard canvas # # for button in buttonList: # # x, y = button.pos # # cv2.rectangle(keyboard_canvas, (x, y), (x + button.size[0], y + button.size[1]), (255, 255, 255), -1) # # cv2.putText(keyboard_canvas, button.text, (x + 20, y + 70), cv2.FONT_HERSHEY_PLAIN, 5, (0, 0, 0), 3) # # # Handle input and gestures from detected hands # # if hands: # # for hand in hands: # # lmList = hand["lmList"] # # if lmList: # # # Get the coordinates of the index finger tip (landmark 8) # # x8, y8 = lmList[8][0], lmList[8][1] # # for button in buttonList: # # bx, by = button.pos # # bw, bh = button.size # # # Check if the index finger is over a button # # if bx < x8 < bx + bw and by < y8 < by + bh: # # # Highlight the button and update the text # # cv2.rectangle(img, (bx, by), (bx + bw, by + bh), (0, 255, 0), -1) # # cv2.putText(img, button.text, (bx + 20, by + 70), cv2.FONT_HERSHEY_PLAIN, 5, (255, 255, 255), 3) # # # Update the output text in session_state # # session_state["output_text"] += button.text # # # Corrected return: Create a video frame from the ndarray image # # return av.VideoFrame.from_ndarray(img, format="bgr24") # # Shared state for output text # if "output_text" not in st.session_state: # st.session_state["output_text"] = "" # class Detection(NamedTuple): # label: str # score: float # box: np.ndarray # @st.cache_resource # Cache label colors # def generate_label_colors(): # return np.random.uniform(0, 255, size=(2, 3)) # Two classes: Left and Right Hand # COLORS = generate_label_colors() # # Initialize MediaPipe Hands # mp_hands = mp.solutions.hands # detector = mp_hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5) # # Session-specific caching # result_queue: "queue.Queue[List[Detection]]" = queue.Queue() # # Hand detection callback # def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame: # image = frame.to_ndarray(format="bgr24") # h, w = image.shape[:2] # # Process image with MediaPipe Hands # results = detector.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) # detections = [] # if results.multi_hand_landmarks: # for hand_landmarks, hand_class in zip(results.multi_hand_landmarks, results.multi_handedness): # # Extract bounding box # x_min, y_min = 1, 1 # x_max, y_max = 0, 0 # for lm in hand_landmarks.landmark: # x_min = min(x_min, lm.x) # y_min = min(y_min, lm.y) # x_max = max(x_max, lm.x) # y_max = max(y_max, lm.y) # # Scale bbox to image size # box = np.array([x_min * w, y_min * h, x_max * w, y_max * h]).astype("int") # # Label and score # label = hand_class.classification[0].label # score = hand_class.classification[0].score # detections.append(Detection(label=label, score=score, box=box)) # # Draw bounding box and label # color = COLORS[0 if label == "Left" else 1] # cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), color, 2) # caption = f"{label}: {round(score * 100, 2)}%" # cv2.putText( # image, # caption, # (box[0], box[1] - 15 if box[1] - 15 > 15 else box[1] + 15), # cv2.FONT_HERSHEY_SIMPLEX, # 0.5, # color, # 2, # ) # # Put results in the queue # result_queue.put(detections) # return av.VideoFrame.from_ndarray(image, format="bgr24") # webrtc_ctx = webrtc_streamer( # key="keyboard-demo", # mode=WebRtcMode.SENDRECV, # rtc_configuration={ # "iceServers": get_ice_servers(), # "iceTransportPolicy": "relay", # }, # video_frame_callback=video_frame_callback, # media_stream_constraints={"video": True, "audio": False}, # async_processing=True, # ) # st.markdown("### Instructions") # st.write( # """ # 1. Turn on your webcam using the checkbox above. # 2. Use hand gestures to interact with the virtual keyboard. # """ # ) import logging import cv2 import numpy as np import streamlit as st from streamlit_webrtc import WebRtcMode, webrtc_streamer from cvzone.HandTrackingModule import HandDetector from cvzone.SelfiSegmentationModule import SelfiSegmentation import os import time from sample_utils.turn import get_ice_servers logger = logging.getLogger(__name__) # Streamlit settings st.set_page_config(page_title="Virtual Keyboard", layout="wide") st.title("Interactive Virtual Keyboard") st.subheader('''Turn on the webcam and use hand gestures to interact with the virtual keyboard. Use 'a' and 'd' from the keyboard to change the background.''') # Initialize modules detector = HandDetector(maxHands=1, detectionCon=0.8) segmentor = SelfiSegmentation() # Define virtual keyboard layout keys = [["Q", "W", "E", "R", "T", "Y", "U", "I", "O", "P"], ["A", "S", "D", "F", "G", "H", "J", "K", "L", ";"], ["Z", "X", "C", "V", "B", "N", "M", ",", ".", "/"]] class Button: def __init__(self, pos, text, size=[100, 100]): self.pos = pos self.size = size self.text = text listImg = os.listdir('street') if os.path.exists('street') else [] if not listImg: st.error("Error: 'street' directory is missing or empty. Please add background images.") st.stop() else: imgList = [cv2.imread(f'street/{imgPath}') for imgPath in listImg if cv2.imread(f'street/{imgPath}') is not None] indexImg = 0 prev_key_time = [time.time()] * 2 output_text = "" if "output_text" not in st.session_state: st.session_state["output_text"] = "" def video_frame_callback(frame): global indexImg, output_text img = frame.to_ndarray(format="bgr24") imgOut = segmentor.removeBG(img, imgList[indexImg]) hands, img = detector.findHands(imgOut, flipType=False) keyboard_canvas = np.zeros_like(img) buttonList = [] for key in keys[0]: buttonList.append(Button([30 + keys[0].index(key) * 105, 30], key)) for key in keys[1]: buttonList.append(Button([30 + keys[1].index(key) * 105, 150], key)) for key in keys[2]: buttonList.append(Button([30 + keys[2].index(key) * 105, 260], key)) for i, hand in enumerate(hands): lmList = hand['lmList'] if lmList: x4, y4 = lmList[4][0], lmList[4][1] x8, y8 = lmList[8][0], lmList[8][1] distance = np.sqrt((x8 - x4) ** 2 + (y8 - y4) ** 2) click_threshold = 10 for button in buttonList: x, y = button.pos w, h = button.size if x < x8 < x + w and y < y8 < y + h: cv2.rectangle(img, button.pos, (x + w, y + h), (0, 255, 160), -1) cv2.putText(img, button.text, (x + 20, y + 70), cv2.FONT_HERSHEY_PLAIN, 5, (255, 255, 255), 3) if (distance / np.sqrt((hand['bbox'][2]) ** 2 + (hand['bbox'][3]) ** 2)) * 100 < click_threshold: if time.time() - prev_key_time[i] > 2: prev_key_time[i] = time.time() if button.text != 'BS' and button.text != 'SPACE': output_text += button.text elif button.text == 'BS': output_text = output_text[:-1] else: output_text += ' ' st.session_state["output_text"] = output_text return frame.from_ndarray(img, format="bgr24") webrtc_streamer( key="virtual-keyboard", mode=WebRtcMode.SENDRECV, rtc_configuration={"iceServers": get_ice_servers(), "iceTransportPolicy": "relay"}, media_stream_constraints={"video": True, "audio": False}, video_frame_callback=video_frame_callback, async_processing=True, ) st.subheader("Output Text") st.text_area("Live Input:", value=st.session_state["output_text"], height=200)