Update app.py

app.py

@@ -2,21 +2,27 @@ import gradio as gr
 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):
+def run_mediapipe(image, soln_type, min_detection_confidence):
     if soln_type == 'facemesh':
-        annotated_image = mp_face_mesh_fn(image)
+        annotated_image = mp_face_mesh_fn(image, min_detect_conf=min_detection_confidence)
     elif soln_type == 'facedetect':
-        annotated_image = mp_face_detect_fn(image)
+        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)
+        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)
+        annotated_image = mp_pose_estimation_fn(image, min_detect_conf=min_detection_confidence)
     elif soln_type == 'holistic':
-        annotated_image = mp_holistic_fn(image)
+        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
 
 
@@ -26,15 +32,19 @@ def main():
         'facemesh', 
         'handpose', 
         'pose estimate',
-        'holistic'
+        'holistic',
+        'objectron',
+        'selfie segment'
     ]
 
     sample_images = [
-        ["examples/0.jpg", solutions[0]],
-        ["examples/1.jpg", solutions[1]],
-        ["examples/2.png", solutions[2]],
-        ["examples/3.jpg", solutions[3]],
-        ["examples/4.jpg", solutions[4]],
+        ["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(
@@ -47,6 +57,13 @@ def main():
                 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"),
         title="Google MediaPipe Demo",