{str}
', unsafe_allow_html=True) # ---------------------------------------------------------------------- st.markdown( """ """,unsafe_allow_html=True,) # ---------------------------------------------------------------------- if st.sidebar.button('Github'): js = "window.open('https://github.com/shashankanand13monu/Game-Automation')" # New tab or window # js = "window.location.href = 'https://www.streamlit.io/'" # Current tab html = '🕹️ W- 5 Fingers 🕹️ A- 2 or 3 Fingers' st.markdown(original_title, unsafe_allow_html=True) original_title = '
🕹️ S- Fist 🕹️ D- 4 Fingers' st.markdown(original_title, unsafe_allow_html=True) # st.subheader('''W - 5 Fingers''') # st.subheader('S - Fist\n A - 2 or 3 Fingers\n D - 4 Fingers') st.image('wsad.jpg',width=200) original_title = '
*NOTE' st.markdown(original_title, unsafe_allow_html=True) original_title= '''
Video Option will Experience Lag in Browsers.
If It's Lagging just Reload & Choose your option ASAP
eg: Choosing Max Hands or Using Webcam.
Webcam Will Take about 20 Seconds to Load
Update :
1) I Discovered that you can't use Webcam Online,
Because then it will try Access Server's Which i don't Own.
2) Hand Marks are not showing online + Video freezes
Solution :
Go to main Streamlit WebApp Code & Run it Locally by typing
streamlit run st2.py
'''
# st.markdown('''Video Option will Experience **Lag** in **Browsers**. If It's **Lagging** just **Reload** & Choose your option ASAP eg: **Choosing Max Hands** or **Using Webcam**. Webcam Will Take about **20 Seconds** to Load ''')
st.markdown(original_title, unsafe_allow_html=True)
# ----------------------------------------------------------------------
elif app_mode == 'Run on Image':
drawing_spec = mp_drawing.DrawingSpec(thickness=2, circle_radius=1)
st.sidebar.markdown ('---------' )
st.markdown(
"""
""",unsafe_allow_html=True,)
# st.markdown("**Detected Hands**")
st.header("** Detected Hands **")
kpi1_text = st.markdown("0")
max_hands= st.sidebar.number_input('Maximum Number of Hands',value=2,min_value=1,max_value=4)
# st.sidebar('---')
detection_confidence= st.sidebar.slider('Detection Confidence',min_value=0.0,max_value=1.0,value=0.5)
st.sidebar.markdown('---')
IMAGE_FILE=[]
count=0
img_file_buffer = st.sidebar.file_uploader("Upload an Image", type=["jpg","jpeg", "png"])
if img_file_buffer is not None:
file_bytes = np.asarray(bytearray(img_file_buffer.read()), dtype=np.uint8)
opencv_image = cv2.imdecode(file_bytes, 1)
image = opencv_image.copy()
else:
demo_image= DEMO_IMAGE
image = 'demo.jpg'
cap = cv2.imread('demo.jpg', cv2.IMREAD_UNCHANGED)
image = cap.copy()
# st.sidebar.text('Input Image')
st.sidebar.subheader('Input Image')
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
st.sidebar.image(image)
st.sidebar.subheader('Demo Images')
st.sidebar.image('dddemo.jpg')
st.sidebar.image('Screenshot 2022-01-09 161732.png')
st.sidebar.image('woman-showing-four-fingers-white-background-woman-showing-four-fingers-white-background-closeup-hand-134504006.jpg')
st.sidebar.image('demo.jpg')
hand_count =0
image = cv2.cvtColor(image,cv2.COLOR_RGB2BGR)
cap = cv2.imread('demo.jpg', cv2.IMREAD_UNCHANGED)
with mp_hands.Hands(
static_image_mode=True,
max_num_hands=max_hands,
min_detection_confidence=detection_confidence) as hands:
hand_count+=1
results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
try:
age_height, image_width, _ = image.shape
annotated_image = image.copy()
lmList=[]
lmList2forModel=[]
for hand_landmarks in results.multi_hand_landmarks:
myHands=results.multi_hand_landmarks[0]
for id,lm in enumerate(myHands.landmark):
h,w,c=image.shape
cx,cy=int(lm.x*w), int(lm.y*h)
lmList.append([id,cx,cy])
lmList2forModel.append([cx,cy])
fingers=[]
if lmList[tipIds[0]][1] < lmList[tipIds[0]-1][1]:
fingers.append(1)
else:
fingers.append(0)
for id in range(1,5):
if lmList[tipIds[id]][2] < lmList[tipIds[id]-1][2]:
fingers.append(1)
else:
fingers.append(0)
total= fingers.count(1)
if total==5:
original_title = 'Acclerate
' st.markdown(original_title, unsafe_allow_html=True) # st.markdown('---') overlay = cv2.imread('istockphoto-1179377734-612x612.jpg') overlay = cv2.resize(overlay,(100,100)) x_offset = 80 y_offset = 10 x_end = x_offset + overlay.shape[1] y_end = y_offset + overlay.shape[0] annotated_image[y_offset:y_end,x_offset:x_end] = overlay if total==4: # st.text('Right') original_title = 'Right
' st.markdown(original_title, unsafe_allow_html=True) overlay = cv2.imread('istockphoto-1179377734-612x612 (4).jpg') overlay = cv2.resize(overlay,(100,100)) x_offset = 120 y_offset = 50 x_end = x_offset + overlay.shape[1] y_end = y_offset + overlay.shape[0] annotated_image[y_offset:y_end,x_offset:x_end] = overlay if total==2 or total==3: # st.text('Left') original_title = 'Left
' st.markdown(original_title, unsafe_allow_html=True) overlay = cv2.imread('istockphoto-1179377734-612x612 (1).jpg') overlay = cv2.resize(overlay,(100,100)) x_offset = 50 y_offset = 50 x_end = x_offset + overlay.shape[1] y_end = y_offset + overlay.shape[0] annotated_image[y_offset:y_end,x_offset:x_end] = overlay if total==0: # st.text('Brake') original_title = 'Brake
' st.markdown(original_title, unsafe_allow_html=True) overlay = cv2.imread('istockphoto-1179377734-612x612 (3).jpg') overlay = cv2.resize(overlay,(100,100)) x_offset = 50 y_offset = 90 x_end = x_offset + overlay.shape[1] y_end = y_offset + overlay.shape[0] annotated_image[y_offset:y_end,x_offset:x_end] = overlay mp_drawing.draw_landmarks( annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS, mp_draw.DrawingSpec(color=(0,0,255), thickness=2, circle_radius=2), mp_draw.DrawingSpec(color=(0,255,0), thickness=2, circle_radius=2)) kpi1_text.write(f"Sorry, No Hand Found!!
' st.markdown(original_title, unsafe_allow_html=True) kpi1_text.write(f"Frame Rate
' st.markdown(original_title, unsafe_allow_html=True) kpi1_text = st.markdown ("0") with kpi2: original_title = 'Detected Hands
' st.markdown(original_title, unsafe_allow_html=True) kpi2_text = st.markdown ("0") with kpi3: original_title = 'Video Width
' st.markdown(original_title, unsafe_allow_html=True) kpi3_text = st.markdown("0") st.markdown ("