Define model

utils.py

@@ -0,0 +1,54 @@
+import numpy
+import sahi.predict
+import sahi.utils
+from PIL import Image
+
+TEMP_DIR = "temp"
+
+
+def sahi_mmdet_inference(
+    image,
+    detection_model,
+    slice_height=512,
+    slice_width=512,
+    overlap_height_ratio=0.2,
+    overlap_width_ratio=0.2,
+    image_size=640,
+    postprocess_type="GREEDYNMM",
+    postprocess_match_metric="IOS",
+    postprocess_match_threshold=0.5,
+    postprocess_class_agnostic=False,
+):
+
+    # standard inference
+    detection_model.image_size = image_size
+    prediction_result_1 = sahi.predict.get_prediction(
+        image=image, detection_model=detection_model
+    )
+    visual_result_1 = sahi.utils.cv.visualize_object_predictions(
+        image=numpy.array(image),
+        object_prediction_list=prediction_result_1.object_prediction_list,
+    )
+    output_1 = Image.fromarray(visual_result_1["image"])
+
+    # sliced inference
+    prediction_result_2 = sahi.predict.get_sliced_prediction(
+        image=image,
+        detection_model=detection_model,
+        slice_height=slice_height,
+        slice_width=slice_width,
+        overlap_height_ratio=overlap_height_ratio,
+        overlap_width_ratio=overlap_width_ratio,
+        postprocess_type=postprocess_type,
+        postprocess_match_metric=postprocess_match_metric,
+        postprocess_match_threshold=postprocess_match_threshold,
+        postprocess_class_agnostic=postprocess_class_agnostic,
+    )
+    visual_result_2 = sahi.utils.cv.visualize_object_predictions(
+        image=numpy.array(image),
+        object_prediction_list=prediction_result_2.object_prediction_list,
+    )
+
+    output_2 = Image.fromarray(visual_result_2["image"])
+
+    return output_1, output_2