Merge pull request #12 from Koldim2001/feature/batch_processing

Feature/batch processing

.gitignore

@@ -19,4 +19,5 @@ setup.cfg
 build
 info_how_pip_upload.txt
 examples/patched_yolo_infer
+**.engine
 **.ipynb

README.md

@@ -121,6 +121,7 @@ Class implementing cropping and passing crops through a neural network for detec
 | resize_initial_size   | bool                   | False        | Whether to resize the results to the original input image size (ps: slow operation).                           |
 | memory_optimize       | bool                   | True         | Memory optimization option for segmentation (less accurate results when enabled).                              |
 | inference_extra_args  | dict                   | None         | Dictionary with extra ultralytics [inference parameters](https://docs.ultralytics.com/modes/predict/#inference-arguments) (possible keys: half, device, max_det, augment, agnostic_nms and retina_masks) |
+| batch_inference       | bool                   | False        | Batch inference of image crops through a neural network instead of sequential passes of crops (ps: faster inference, higher gpu memory use).                           |
 
 
 **CombineDetections**

patched_yolo_infer/README.md

@@ -9,7 +9,7 @@ This library facilitates various visualizations of inference results from ultral
 You can install the library via pip:
 
 ```bash
-pip install patched_yolo_infer
+pip install patched-yolo-infer
 ```
 
 Note: If CUDA support is available, it's recommended to pre-install PyTorch with CUDA support before installing the library. Otherwise, the CPU version will be installed by default.
@@ -99,6 +99,7 @@ Class implementing cropping and passing crops through a neural network for detec
 - **resize_initial_size** (*bool*): Whether to resize the results to the original image size (ps: slow operation).
 - **memory_optimize** (*bool*): Memory optimization option for segmentation (less accurate results when enabled).
 - **inference_extra_args** (*dict*): Dictionary with extra ultralytics [inference parameters](https://docs.ultralytics.com/modes/predict/#inference-arguments) (possible keys: half, device, max_det, augment, agnostic_nms and retina_masks)
+- **batch_inference** (*bool*): Batch inference of image crops through a neural network instead of sequential passes of crops (ps: faster inference, higher gpu memory use)
 
 **CombineDetections**
 Class implementing combining masks/boxes from multiple crops + NMS (Non-Maximum Suppression).\

patched_yolo_infer/nodes/MakeCropsDetectThem.py

@@ -50,6 +50,8 @@ class MakeCropsDetectThem:
                                     image size (ps: slow operation).
         class_names_dict (dict): Dictionary containing class names of the YOLO model.
         memory_optimize (bool): Memory optimization option for segmentation (less accurate results)
+        batch_inference (bool): Batch inference of image crops through a neural network instead of 
+                                    sequential passes of crops (ps: Faster inference, higher memory use)
         inference_extra_args (dict): Dictionary with extra ultralytics inference parameters
     """
     def __init__(
@@ -70,6 +72,7 @@ def __init__(
         model=None,
         memory_optimize=True,
         inference_extra_args=None,
+        batch_inference=False,
     ) -> None:
         if model is None:
             self.model = YOLO(model_path)  # Load the model from the specified path
@@ -91,6 +94,7 @@ def __init__(
         self.memory_optimize = memory_optimize # memory opimization option for segmentation
         self.class_names_dict = self.model.names # dict with human-readable class names
         self.inference_extra_args = inference_extra_args # dict with extra ultralytics inference parameters
+        self.batch_inference = batch_inference # batch inference of image crops through a neural network
 
         self.crops = self.get_crops_xy(
             self.image,
@@ -100,7 +104,10 @@ def __init__(
             overlap_y=self.overlap_y,
             show=self.show_crops,
         )
-        self._detect_objects()
+        if self.batch_inference:
+            self._detect_objects_batch() 
+        else:
+            self._detect_objects()
 
     def get_crops_xy(
         self,
@@ -141,6 +148,7 @@ def get_crops_xy(
         x_new = round((x_steps-1) * (shape_x * cross_koef_x) + shape_x)
         image_innitial = image_full.copy()
         image_full = cv2.resize(image_full, (x_new, y_new))
+        batch_of_crops = []
 
         if show:
             plt.figure(figsize=[x_steps*0.9, y_steps*0.9])
@@ -176,12 +184,17 @@ def get_crops_xy(
                                         x_start=x_start,
                                         y_start=y_start,
                 ))
+                if self.batch_inference:
+                    batch_of_crops.append(im_temp)
 
         if show:
             plt.show()
             print('Number of generated images:', count)
 
-        return data_all_crops
+        if self.batch_inference:
+            return data_all_crops, batch_of_crops
+        else:
+            return data_all_crops
 
     def _detect_objects(self):
         """
@@ -207,3 +220,76 @@ def _detect_objects(self):
             crop.calculate_real_values()
             if self.resize_initial_size:
                 crop.resize_results()
+
+    def _detect_objects_batch(self):
+        """
+        Method to detect objects in batch of image crops.
+
+        This method performs batch inference using the YOLO model,
+        calculates real values, and optionally resizes the results.
+
+        Returns:
+            None
+        """
+        crops, batch = self.crops
+        self.crops = crops
+        self._calculate_batch_inference(
+            batch,
+            self.crops,
+            self.model,
+            imgsz=self.imgsz,
+            conf=self.conf,
+            iou=self.iou,
+            segment=self.segment,
+            classes_list=self.classes_list,
+            memory_optimize=self.memory_optimize,
+            extra_args=self.inference_extra_args
+        )
+        for crop in self.crops:
+            crop.calculate_real_values()
+            if self.resize_initial_size:
+                crop.resize_results()
+
+    def _calculate_batch_inference(
+        self,
+        batch,
+        crops,
+        model,
+        imgsz=640,
+        conf=0.35,
+        iou=0.7,
+        segment=False,
+        classes_list=None,
+        memory_optimize=False,
+        extra_args=None,
+    ):
+        # Perform batch inference of image crops through a neural network
+        extra_args = {} if extra_args is None else extra_args
+        predictions = model.predict(
+            batch,
+            imgsz=imgsz,
+            conf=conf,
+            iou=iou,
+            classes=classes_list,
+            verbose=False,
+            **extra_args
+        )
+
+        for pred, crop in zip(predictions, crops):
+
+            # Get the bounding boxes and convert them to a list of lists
+            crop.detected_xyxy = pred.boxes.xyxy.cpu().int().tolist()
+
+            # Get the classes and convert them to a list
+            crop.detected_cls = pred.boxes.cls.cpu().int().tolist()
+
+            # Get the mask confidence scores
+            crop.detected_conf = pred.boxes.conf.cpu().numpy()
+
+            if segment and len(crop.detected_cls) != 0:
+                if memory_optimize:
+                    # Get the polygons
+                    crop.polygons = [mask.astype(np.uint16) for mask in pred.masks.xy]
+                else:
+                    # Get the masks
+                    crop.detected_masks = pred.masks.data.cpu().numpy()

requirements.txt

@@ -1,5 +1,5 @@
+numpy<2.0
 torch
-numpy
 opencv-python
 matplotlib
 ultralytics

setup.py

@@ -8,8 +8,8 @@
     long_description = "\n" + fh.read()
 
 
-VERSION = '1.2.6'
-DESCRIPTION = '''YOLO-Patch-Based-Inference for detection/segmentation of small objects in images.'''
+VERSION = '1.2.7'
+DESCRIPTION = '''Patch-Based-Inference for detection/segmentation of small objects in images.'''
 
 setup(
     name="patched_yolo_infer",
@@ -23,7 +23,7 @@
     packages=find_packages(),
     python_requires=">=3.8",
     install_requires=[
-        'numpy',
+        'numpy<2.0',
         'opencv-python',
         'matplotlib',
         'torch',
@@ -33,8 +33,10 @@
         "python",
         "yolov8",
         "yolov9",
+        "yolov10",
         "rtdetr",
-        "sam",
+        "fastsam",
+        "sahi",
         "object detection",
         "instance segmentation",
         "patch-based inference",