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Update app.py
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app.py
CHANGED
@@ -37,12 +37,12 @@ def load_model(model_repo_id):
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return detection_model
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def predict(pilimg):
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image_np = pil_image_as_numpy_array(pilimg)
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return predict2(image_np),predict3(image_np)
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def predict2(image_np):
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results = detection_model(image_np)
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@@ -60,7 +60,7 @@ def predict2(image_np):
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category_index,
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use_normalized_coordinates=True,
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max_boxes_to_draw=20,
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min_score_thresh=0.38,
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agnostic_mode=False,
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line_thickness=2)
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@@ -69,7 +69,7 @@ def predict2(image_np):
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return result_pil_img2
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def predict3(image_np):
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results = detection_model2(image_np)
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@@ -87,7 +87,7 @@ def predict3(image_np):
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category_index,
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use_normalized_coordinates=True,
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max_boxes_to_draw=20,
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min_score_thresh
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agnostic_mode=False,
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line_thickness=2)
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@@ -168,7 +168,7 @@ test12 = os.path.join(os.path.dirname(__file__), "data/test12.jpeg")
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base_image = gr.Interface(
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fn=predict,
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inputs=[gr.Image(type="pil"),gr.Slider(minimum=0.01, maximum=0.99, value=0.6 ,label="Threshold
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outputs=[gr.Image(type="pil",label="Base Model"),gr.Image(type="pil",label="Tuned Model")],
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title="Luffy and Chopper Head detection. SSD mobile net V2 320x320",
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description="Upload a Image for prediction or click on below examples. Prediction confident >38% will be shown in dectected images. Threshold slider is WIP",
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return detection_model
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def predict(pilimg,Threshold):
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image_np = pil_image_as_numpy_array(pilimg)
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return predict2(image_np,float(Threshold)),predict3(image_np,float(Threshold))
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def predict2(image_np,Threshold):
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results = detection_model(image_np)
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category_index,
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use_normalized_coordinates=True,
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max_boxes_to_draw=20,
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min_score_thresh=float(Threshold),#0.38,
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agnostic_mode=False,
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line_thickness=2)
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return result_pil_img2
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def predict3(image_np,Threshold):
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results = detection_model2(image_np)
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category_index,
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use_normalized_coordinates=True,
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max_boxes_to_draw=20,
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min_score_thresh=float(Threshold),#.38,
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agnostic_mode=False,
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line_thickness=2)
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base_image = gr.Interface(
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fn=predict,
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inputs=[gr.Image(type="pil"),gr.Slider(minimum=0.01, maximum=0.99, value=0.6 ,label="Threshold",info="[not in used]to set prediction confidence threshold")],
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outputs=[gr.Image(type="pil",label="Base Model"),gr.Image(type="pil",label="Tuned Model")],
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title="Luffy and Chopper Head detection. SSD mobile net V2 320x320",
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description="Upload a Image for prediction or click on below examples. Prediction confident >38% will be shown in dectected images. Threshold slider is WIP",
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