Spaces:
Running
Running
| import gradio as gr | |
| # necessary packages | |
| from facemesh import mp_face_mesh_fn | |
| from facedetect import mp_face_detect_fn | |
| from handposedetect import mp_hand_pose_detect_fn | |
| from objectron3d import mp_objectron_fn | |
| from posestimate import mp_pose_estimation_fn | |
| from holistic import mp_holistic_fn | |
| from selfiseg import mp_selfi_segment_fn | |
| def run_mediapipe(image, soln_type, min_detection_confidence): | |
| if soln_type == 'facemesh': | |
| annotated_image = mp_face_mesh_fn(image, min_detect_conf=min_detection_confidence) | |
| elif soln_type == 'facedetect': | |
| annotated_image = mp_face_detect_fn(image, min_detect_conf=min_detection_confidence) | |
| elif soln_type == 'handpose': | |
| annotated_image = mp_hand_pose_detect_fn(image, min_detect_conf=min_detection_confidence) | |
| elif soln_type == 'pose estimate': | |
| annotated_image = mp_pose_estimation_fn(image, min_detect_conf=min_detection_confidence) | |
| elif soln_type == 'holistic': | |
| annotated_image = mp_holistic_fn(image, min_detect_conf=min_detection_confidence) | |
| elif soln_type == 'objectron': | |
| annotated_image = mp_objectron_fn(image, min_detect_conf=min_detection_confidence) | |
| elif soln_type == 'selfie segment': | |
| annotated_image = mp_selfi_segment_fn(image) | |
| return annotated_image | |
| def main(): | |
| solutions = [ | |
| 'facedetect', | |
| 'facemesh', | |
| 'handpose', | |
| 'pose estimate', | |
| 'holistic', | |
| 'objectron', | |
| 'selfie segment' | |
| ] | |
| sample_images = [ | |
| ["examples/0.jpg", solutions[0], 0.5], | |
| ["examples/1.jpg", solutions[1], 0.5], | |
| ["examples/2.png", solutions[2], 0.5], | |
| ["examples/3.jpg", solutions[3], 0.5], | |
| ["examples/4.jpg", solutions[4], 0.5], | |
| ["examples/5.jpg", solutions[5], 0.3], | |
| ["examples/6.jpg", solutions[6], None], | |
| ] | |
| iface = gr.Interface( | |
| fn=run_mediapipe, | |
| inputs=[ | |
| gr.inputs.Image(label="Input Image"), | |
| gr.inputs.Radio( | |
| solutions, | |
| type='value', | |
| default=solutions[0], | |
| label='Solutions' | |
| ), | |
| gr.inputs.Slider( | |
| 0, | |
| 1, | |
| step=0.05, | |
| default=0.5, | |
| label='Minimum Detection Confidence' | |
| ) | |
| ], | |
| outputs=gr.outputs.Image(label="MediaPipe", type='numpy'), | |
| title="Google MediaPipe Demo", | |
| description="Few solutions of <a href='https://mediapipe.dev/'>Google MediaPipe</a> are presented here. Note, to test `Objectron` model, make sure to ensure <a href='https://github.com/google/mediapipe/blob/master/mediapipe/python/solutions/objectron.py#L128-L133'>Model Type</a>. Currently it only supports `Shoe`, `Chair`, `Cup` and `Camera` object.", | |
| examples=sample_images | |
| ) | |
| iface.launch() | |
| if __name__ == '__main__': | |
| main() | |