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import mediapipe as mp
import numpy as np
import streamlit as st
import os
import math
# Streamlit app setup
st.title("AI Virtual Painter")
st.text("Interact with the AI Virtual Painter using hand gestures!")
# Mediapipe and OpenCV setup
mp_hands = mp.solutions.hands
# Configuration
width, height = 640, 480
draw_color = (0, 0, 255) # Default color: Red
thickness = 15
tipIds = [4, 8, 12, 16, 20] # Fingertips indexes
xp, yp = 0, 0 # Previous points for drawing
# Load header images for color options
header_images = [f"Header/{img}" for img in os.listdir("Header") if img.endswith((".png", ".jpg", ".jpeg"))]
header_list = [cv2.imread(img) for img in header_images]
header = header_list[0] # Default header
# Sidebar for color selection
# st.sidebar.title("Select Drawing Color")
color_map = {
"Red": (0, 0, 255),
"Blue": (255, 0, 0),
"Green": (0, 255, 0),
"Eraser": (0, 0, 0)
}
selected_color = list(color_map.keys())[0]
draw_color = color_map[selected_color]
# Canvas for drawing
canvas = np.zeros((height, width, 3), dtype=np.uint8)
# Start capturing video
cap = cv2.VideoCapture(0)
# Create a placeholder for the live video feed
video_placeholder = st.empty()
# Process the video feed
with mp_hands.Hands(min_detection_confidence=0.85, min_tracking_confidence=0.5, max_num_hands=1) as hands:
while cap.isOpened():
ret, frame = cap.read()
if not ret:
st.error("Failed to capture video.")
break
frame = cv2.flip(frame, 1) # Flip the frame horizontally
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Process hand landmarks
results = hands.process(frame_rgb)
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
points = [
(int(lm.x * width), int(lm.y * height))
for lm in hand_landmarks.landmark
]
if points:
x1, y1 = points[8] # Index finger
x2, y2 = points[12] # Middle finger
x3, y3 = points[4] # Thumb
x4, y4 = points[20] # Pinky finger
## Checking which fingers are up
fingers = []
# Checking the thumb
if points[tipIds[0]][0] < points[tipIds[0] - 1][0]:
fingers.append(1)
else:
fingers.append(0)
# The rest of the fingers
for id in range(1, 5):
if points[tipIds[id]][1] < points[tipIds[id] - 2][1]:
fingers.append(1)
else:
fingers.append(0)
# Check if in selection mode (two fingers up)
nonSel = [0, 3, 4] # indexes of the fingers that need to be down in the Selection Mode
if (fingers[1] and fingers[2]) and all(fingers[i] == 0 for i in nonSel):
# Selection mode (change color)
xp, yp =[x1, y1] # Reset drawing position
# Selecting the colors and the eraser on the screen
for i, img in enumerate(header_list):
if (10 < y1 < 100):
if (100 < x1 < 120):
header = header_list[0]
draw_color = color_map[list(color_map.keys())[0]]
elif (230 < x1 < 250):
header = header_list[1]
draw_color = color_map[list(color_map.keys())[1]]
elif (370 < x1 < 390):
header = header_list[2]
draw_color = color_map[list(color_map.keys())[2]]
elif (510 < x1 < 540):
header = header_list[3]
draw_color = color_map[list(color_map.keys())[3]]
break
cv2.rectangle(frame, (x1-10, y1-15), (x2+10, y2+23), draw_color, -1)
# Adjust line thickness using index finger and thumb
selecting = [1, 1, 0, 0, 0] # Thumb and index finger up
setting = [1, 1, 0, 0, 1] # Thumb, index, and pinky up
if all(fingers[i] == j for i, j in zip(range(0,5), selecting)) or \
all(fingers[i] == j for i, j in zip(range(0,5), setting)):
# Calculate radius based on distance between thumb and index finger
r = int(math.sqrt((x1 - x3) ** 2 + (y1 - y3) ** 2) / 3)
# Find midpoint between thumb and index finger
x0, y0 = [(x1 + x3) / 2, (y1 + y3) / 2]
# Orthogonal vector for alignment
v1, v2 = [x1 - x3, y1 - y3]
v1, v2 = [-v2, v1]
# Normalize vector
mod_v = math.sqrt(v1 ** 2 + v2 ** 2)
v1, v2 = [v1 / mod_v, v2 / mod_v]
# Draw circle indicating thickness selection
c = 3 + r
x0, y0 = [int(x0 - v1 * c), int(y0 - v2 * c)]
cv2.circle(frame, (x0, y0), int(r / 2), draw_color, -1)
# Confirm thickness when pinky is up
if fingers[4]:
thickness = r
cv2.putText(frame, "Thickness Set", (x4 - 25, y4 - 8),
cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0, 0, 0), 1)
xp, yp = [x1, y1]
# Check if in drawing mode (only index finger up)
nonDraw = [0, 2, 3, 4]
if fingers[1] and all(fingers[i] == 0 for i in nonDraw):
cv2.circle(frame, (x1, y1), thickness // 2, draw_color, -1)
if xp == 0 and yp == 0:
xp, yp = [x1, y1]
cv2.line(canvas, (xp, yp), (x1, y1), draw_color, thickness)
xp, yp = [x1, y1]
# Check if erasing (hand closed - no fingers up)
if not any(fingers):
canvas = np.zeros((height, width, 3), dtype=np.uint8)
xp, yp = 0, 0
# Overlay the canvas onto the video feed
canvas_resized = cv2.resize(canvas, (frame.shape[1], frame.shape[0]))
# Create an inverted binary mask
img_gray = cv2.cvtColor(canvas_resized, cv2.COLOR_BGR2GRAY)
_, img_inv = cv2.threshold(img_gray, 50, 255, cv2.THRESH_BINARY_INV)
img_inv = cv2.cvtColor(img_inv, cv2.COLOR_GRAY2BGR)
# Combine the frame and the canvas
frame = cv2.bitwise_and(frame, img_inv)
frame = cv2.bitwise_or(frame, canvas_resized)
# Add the header image to the frame
frame_height, frame_width = frame.shape[:2]
header_resized = cv2.resize(header, (frame_width, 100))
frame[0:100, 0:frame_width] = header_resized
# Update the live video feed in Streamlit
video_placeholder.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), channels="RGB")
# Release the video capture
cap.release()
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