add inference paramters

app.py

@@ -50,9 +50,31 @@ def load_models():
 
     return predictor
 
-def detect(image: np.ndarray, predictor: Predictor) -> np.ndarray:
+def detect(
+        image: np.ndarray,
+        score_threshold: float,
+        iou_threshold: float,
+        mode: str,
+        predictor: Predictor
+) -> np.ndarray:
     # input is rgb image, output must be rgb too
 
+    predictor.detector.detector_kwargs['conf'] = score_threshold
+    predictor.detector.detector_kwargs['iou'] = iou_threshold
+
+    if mode == "Use persons and faces":
+        use_persons = True
+        disable_faces = False
+    elif mode == "Use persons only":
+        use_persons = True
+        disable_faces = True
+    elif mode == "Use faces only":
+        use_persons = False
+        disable_faces = False
+
+    predictor.age_gender_model.meta.use_persons = use_persons
+    predictor.age_gender_model.meta.disable_faces = disable_faces
+
     image = image[:, :, ::-1]  # RGB -> BGR
     detected_objects, out_im = predictor.recognize(image)
     return out_im[:, :, ::-1]  # BGR -> RGB
@@ -61,13 +83,21 @@ def detect(image: np.ndarray, predictor: Predictor) -> np.ndarray:
 predictor = load_models()
 
 image_dir = pathlib.Path('images')
-examples = [[path.as_posix()] for path in sorted(image_dir.glob('*.jpg'))]
+examples = [[path.as_posix(), 0.4, 0.7, "Use persons and faces"] for path in sorted(image_dir.glob('*.jpg'))]
 
 func = functools.partial(detect, predictor=predictor)
 
 gr.Interface(
     fn=func,
-    inputs=gr.Image(label='Input', type='numpy'),
+    inputs=[
+        gr.Image(label='Input', type='numpy'),
+        gr.Slider(0, 1, value=0.4, step=0.05, label='Score Threshold'),
+        gr.Slider(0, 1, value=0.7, step=0.05, label='Iou Threshold'),
+        gr.Radio(["Use persons and faces", "Use persons only", "Use faces only"],
+                 value="Use persons and faces",
+                 label="Inference mode",
+                 info="What to use for gender and age recognition"),
+    ],
     outputs=gr.Image(label='Output', type='numpy'),
     examples=examples,
     examples_per_page=30,