Trying to upload to streamlit using mediapipe and open cv in web app

[MikeLynagh]

Hi there,
I have a simple file structure in github. Handapp.py and requirements.txt.
Here is the copy of both. My issue is I keep getting the same error log.

requirements.txt
streamlit
mediapipe==0.9.1
numpy
opencv-python-headless

handapp.py
import streamlit as st
import mediapipe as mp
import numpy as np
from tabulate import tabulate

mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils

joint_list = [[4,3,2, “Thumb IP jt”],[3,2,1, “Thump MC jt”],[8, 7, 6, “Index finger PIP jt”], [7, 6, 5, “Index finger DIP jt”], [6, 5, 0, “Index finge MCP jt”],
[12, 11, 10, “Middle Finger DIP jt”], [11, 10, 9, “Middle finger PIP jt”], [10, 9, 0, “Middle finge MCP jt”],
[16, 15, 14, “Ring Finger DIP jt”], [15, 14, 13, “Ring finger PIP jt”], [14, 13, 0, “Ring finge MCP jt”],
[20, 19, 18, “Little Finger DIP jt”], [19, 18, 17, “Little finger PIP jt”], [18, 17, 0, “Little finge MCP jt”]
]

Function to draw finger angles on the image

def draw_finger_angles(image, results, joint_list):
angle_results =

for hand_landmarks in results.multi_hand_landmarks:
    for joint in joint_list:
        a = np.array([hand_landmarks.landmark[joint[0]].x, hand_landmarks.landmark[joint[0]].y])
        b = np.array([hand_landmarks.landmark[joint[1]].x, hand_landmarks.landmark[joint[1]].y])
        c = np.array([hand_landmarks.landmark[joint[2]].x, hand_landmarks.landmark[joint[2]].y])

        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

        # Convert normalized coordinates to pixel coordinates
        b = tuple(np.multiply(b, [image.shape[1], image.shape[0]]).astype(int))

        # Add the results to the list
        angle_results.append([joint[3], angle])

        # Highlighting the added label name in the print statement
        cv2.putText(image, f"{joint[3]}: {angle:.2f} deg", b,
                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)

return image, angle_results

Main Streamlit app

st.title(“Hand Tracking App”)

Sidebar for file upload

uploaded_file = st.file_uploader(“Choose an image file”, type=[“jpg”, “jpeg”, “png”])

Check if a file is uploaded

if uploaded_file is not None:
# Read the uploaded image
image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.flip(image, 1)

# Streamlit button for processing the image
if st.button("Process Image"):
    # Hand tracking using MediaPipe
    with mp_hands.Hands(min_detection_confidence=0.8, min_tracking_confidence=0.5) as hands:
        image.flags.writeable = False
        results = hands.process(image)
        image.flags.writeable = True

        # Draw landmarks and angles on the image
        image, angle_results = draw_

import streamlit as st

import mediapipe as mp

import numpy as np

from tabulate import tabulate

mp_hands = mp.solutions.hands

mp_drawing = mp.solutions.drawing_utils

joint_list = [[4,3,2, “Thumb IP jt”],[3,2,1, “Thump MC jt”],[8, 7, 6, “Index finger PIP jt”], [7, 6, 5, “Index finger DIP jt”], [6, 5, 0, “Index finge MCP jt”],

[12, 11, 10, “Middle Finger DIP jt”], [11, 10, 9, “Middle finger PIP jt”], [10, 9, 0, “Middle finge MCP jt”],

[16, 15, 14, “Ring Finger DIP jt”], [15, 14, 13, “Ring finger PIP jt”], [14, 13, 0, “Ring finge MCP jt”],

[20, 19, 18, “Little Finger DIP jt”], [19, 18, 17, “Little finger PIP jt”], [18, 17, 0, “Little finge MCP jt”]

]

def draw_finger_angles(image, results, joint_list):

angle_results =

for hand_landmarks in results.multi_hand_landmarks:

for joint in joint_list:

a = np.array([hand_landmarks.landmark[joint[0]].x, hand_landmarks.landmark[joint[0]].y])

b = np.array([hand_landmarks.landmark[joint[1]].x, hand_landmarks.landmark[joint[1]].y])

c = np.array([hand_landmarks.landmark[joint[2]].x, hand_landmarks.landmark[joint[2]].y])

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

# Convert normalized coordinates to pixel coordinates

b = tuple(np.multiply(b, [image.shape[1], image.shape[0]]).astype(int))

# Add the results to the list

angle_results.append([joint[3], angle])

# Highlighting the added label name in the print statement

cv2.putText(image, f"{joint[3]}: {angle:.2f} deg", b,

cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)

return image, angle_results

# Streamlit app header

st.title(“Hand Tracking App”)

# File upload widget

uploaded_file = st.file_uploader(“Choose an image file”, type=[“jpg”, “jpeg”, “png”])

# Check if the user uploaded a file

if uploaded_file is not None:

# Read the uploaded image

image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)

image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

image = cv2.flip(image, 1)

# Hand tracking using MediaPipe

with mp_hands.Hands(min_detection_confidence=0.8, min_tracking_confidence=0.5) as hands:

image.flags.writeable = False

results = hands.process(image)

image.flags.writeable = True

image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

if results.multi_hand_landmarks:

for num, hand_landmarks in enumerate(results.multi_hand_landmarks):

mp_drawing.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS,

mp_drawing.DrawingSpec(color=(121, 22, 76), thickness=2, circle_radius=4),

mp_drawing.DrawingSpec(color=(250, 44, 250), thickness=2, circle_radius=2))

if num == 0: # assuming you are tracking only one hand

# Render left or right detection

text, coord = “Left Hand”, (50, 50)

cv2.putText(image, text, coord, cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)

image, angle_results = draw_finger_angles(image, results, joint_list)

# Print the results in a table format using Streamlit

st.text(tabulate(angle_results, headers=[“Joint”, “Angle”], tablefmt=“grid”))

# Display the image with annotations using Streamlit

st.image(image, caption=‘Hand Tracking’, channels=“BGR”, use_column_width=True)

# Display message if no file is selected

else:

st.text(“No file selected.”)

error log

raceback (most recent call last):

  File "/home/adminuser/venv/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 535, in _run_script

    exec(code, module.__dict__)

  File "/mount/src/handgoniometer/handapp.py", line 2, in <module>

    import mediapipe as mp

  File "/home/adminuser/venv/lib/python3.9/site-packages/mediapipe/__init__.py", line 16, in <module>

    import mediapipe.python.solutions as solutions

  File "/home/adminuser/venv/lib/python3.9/site-packages/mediapipe/python/solutions/__init__.py", line 17, in <module>

    import mediapipe.python.solutions.drawing_styles

  File "/home/adminuser/venv/lib/python3.9/site-packages/mediapipe/python/solutions/drawing_styles.py", line 20, in <module>

    from mediapipe.python.solutions.drawing_utils import DrawingSpec

  File "/home/adminuser/venv/lib/python3.9/site-packages/mediapipe/python/solutions/drawing_utils.py", line 19, in <module>

    import cv2

ImportError: libGL.so.1: cannot open shared object file: No such file or directory

2024-01-30 15:56:04.033 503 GET /script-health-check (10.12.126.212) 134.29ms

Any suggestions are helpful

[Franky1]

Error while deploying an app with mediapipe

@ajaygithub2 Try this one: requirements.txt streamlit mediapipe packages.txt libgl1-mesa-glx libglib2.0-0 test.py import streamlit as st import cv2 import mediapipe as mp print(st.__version__) print(cv2.__version__) print(mp.__version__) gave me the folowing log output in a Python 3.9. environment 1.21.0 4.7.0 0.9.2.1 The above quick solution should work. IMHO the problem with mediapipe is, that it enforces the installation of a non-headless version of opencv, which causes issues in a…

[Ced_Vd]

Hi,
I kinda have the same issue no more with opencv but nom with mediapipe on the same aspect.
It works perfectly on local but at the downloading of mediapipe i guess it can’t find the ML model and get it right (error 13 permission denied on pic)
Do you know any way to solve that?
Any of the solved topics works on me
here is a github repo for the exemple:

GitHub - dflneoibnm/stre

Thanks

[MikeLynagh]

Thanks for this, after creating requirements.txt and updating my requirements.txt, I managed to get the project live.
This is the link, it works with mediapipe and cv2 https://handgoniometer-lkqv4qqzmemseacq4z3mlg.streamlit.app/

[MikeLynagh]

What worked for me when going through the errors, take each one, use chatgpt or bard and solve one error at at time.

[Ced_Vd]

Thanks for sharing your project, is there a way to see your github repo?
To try seeing what’s different
Are you on latest version of mediapipe ond cv2?