File size: 3,853 Bytes
7a43526 6aef42d 7a43526 10edeb5 7a43526 6a749b1 7a43526 55173b7 7a43526 bacd351 7a43526 db59562 7a43526 a15dfc9 7a43526 499aa61 b92100b 7a43526 af4695c b92100b 10edeb5 b92100b 10edeb5 7a43526 a15dfc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
import cv2
import mediapipe as mp
import numpy as np
import streamlit as st
from PIL import Image
st.title("Custom_Hand_Gesture_Recognition")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
# Convert the uploaded file to a PIL Image
img = Image.open(uploaded_file)
# Convert the PIL Image to a numpy array
img = np.array(img)
# Convert the image from RGB to BGR (OpenCV uses BGR format)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img = cv2.resize(img,(256,256))
# Display the image using OpenCV (cv2.imshow does not work in Streamlit)
st.image(img, channels="BGR")
mp_hands = mp.solutions.hands
# Now second step is to set the hands function which will hold the landmarks points
hands = mp_hands.Hands(static_image_mode=True, max_num_hands=2, min_detection_confidence=0.1)
# Last step is to set up the drawing function of hands landmarks on the image
mp_drawing = mp.solutions.drawing_utils
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = hands.process(img)
def calculate_angle(a,b,c):
a = np.array(a) # First
b = np.array(b) # Mid
c = np.array(c) # End
radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
angle = np.abs(radians*180.0/np.pi)
if angle >180.0:
angle = 360-angle
return angle
def distance(a, b, width, height):
distance = np.sqrt(((b[0] - a[0]) * width) ** 2 + ((b[1] - a[1]) * height) ** 2)
return distance
if results.multi_hand_landmarks:
# Render detections
for i in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(img, i, mp_hands.HAND_CONNECTIONS,
mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2),
mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2))
st.image(img)
for hand_no, hand_landmarks in enumerate(results.multi_hand_landmarks):
first = [hand_landmarks.landmark[mp_hands.HandLandmark(6).value].x,hand_landmarks.landmark[mp_hands.HandLandmark(6).value].y]
mid = [(hand_landmarks.landmark[mp_hands.HandLandmark(5).value].x + hand_landmarks.landmark[mp_hands.HandLandmark(9).value].x)/2,(hand_landmarks.landmark[mp_hands.HandLandmark(5).value].y + hand_landmarks.landmark[mp_hands.HandLandmark(9).value].y)/2]
last = [hand_landmarks.landmark[mp_hands.HandLandmark(10).value].x,hand_landmarks.landmark[mp_hands.HandLandmark(10).value].y]
ring_top = [hand_landmarks.landmark[mp_hands.HandLandmark(16).value].x,hand_landmarks.landmark[mp_hands.HandLandmark(16).value].y]
pinky_top = [hand_landmarks.landmark[mp_hands.HandLandmark(20).value].x,hand_landmarks.landmark[mp_hands.HandLandmark(20).value].y]
thumb_top = [hand_landmarks.landmark[mp_hands.HandLandmark(4).value].x,hand_landmarks.landmark[mp_hands.HandLandmark(4).value].y]
height,width,_ = img.shape
thumb_ring_dist = distance(ring_top,thumb_top,width,height)
thumb_pinky_dist = distance(pinky_top,thumb_top,width,height)
st.write('Distance between thumb and ring finger is',thumb_ring_dist)
st.write('Distance between thumb and pinky finger is',thumb_pinky_dist)
if thumb_pinky_dist <= 25 and thumb_ring_dist <= 25:
angle = calculate_angle(first,mid,last)
st.write('angle is ',angle)
if angle >= 50:
st.write('The fingers are opened')
elif angle < 50:
st.write('The fingers are closed')
else:
st.write('error') |