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_lite.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)

    rgb_frame=cv2.circle(rgb_frame,(90,90),(255,0,0),20)
    # 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 rgb_frame


with gr.Blocks() as demo:
    image = WebRTC(label="Stream", mode="send-receive", modality="video", height=480, width=640, time_limit=10)
    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(share=True)