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import gradio as gr
import cv2
from gradio_webrtc import WebRTC
import os
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision, BaseOptions
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
import numpy as np
import cv2
from PIL import Image
MODEL_PATH = r"pose_landmarker_heavy.task"
# Drawing landmarks
def draw_landmarks_on_image(rgb_image, detection_result):
pose_landmarks_list = detection_result.pose_landmarks
annotated_image = np.copy(rgb_image)
for pose_landmarks in pose_landmarks_list:
pose_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
pose_landmarks_proto.landmark.extend([
landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in pose_landmarks
])
solutions.drawing_utils.draw_landmarks(
annotated_image,
pose_landmarks_proto,
solutions.pose.POSE_CONNECTIONS,
solutions.drawing_styles.get_default_pose_landmarks_style())
return annotated_image
base_options = python.BaseOptions(delegate=0,model_asset_path=MODEL_PATH)
options = vision.PoseLandmarkerOptions(
base_options=base_options,
output_segmentation_masks=True)
detector = vision.PoseLandmarker.create_from_options(options)
def detection(image, conf_threshold=0.3):
frame = cv2.flip(image, 1)
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
# # Pose detection
detection_result = detector.detect(mp_image)
# Draw landmarks
annotated_image = draw_landmarks_on_image(mp_image.numpy_view(), detection_result)
return annotated_image
with gr.Blocks() as demo:
image = WebRTC(label="Stream", mode="send-receive", modality="video", height=480, width=640, mirror_webcam=True)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.stream(
fn=detection,
inputs=[image, conf_threshold],
outputs=[image]
)
if __name__ == "__main__":
demo.launch()
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