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112 lines
3.7 KiB
112 lines
3.7 KiB
Inference or prediction of a task returns a list of `Results` objects. Alternatively, in the streaming mode, it returns
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a generator of `Results` objects which is memory efficient. Streaming mode can be enabled by passing `stream=True` in
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predictor's call method.
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!!! example "Predict"
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=== "Getting a List"
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```python
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inputs = [img, img] # list of np arrays
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results = model(inputs) # List of Results objects
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for result in results:
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boxes = result.boxes # Boxes object for bbox outputs
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masks = result.masks # Masks object for segmenation masks outputs
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probs = result.probs # Class probabilities for classification outputs
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```
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=== "Getting a Generator"
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```python
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inputs = [img, img] # list of numpy arrays
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results = model(inputs, stream=True) # generator of Results objects
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for r in results:
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boxes = r.boxes # Boxes object for bbox outputs
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masks = r.masks # Masks object for segmenation masks outputs
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probs = r.probs # Class probabilities for classification outputs
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```
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## Working with Results
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Results object consists of these component objects:
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- `Results.boxes` : `Boxes` object with properties and methods for manipulating bboxes
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- `Results.masks` : `Masks` object used to index masks or to get segment coordinates.
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- `Results.probs` : `torch.Tensor` containing the class probabilities/logits.
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- `Results.orig_shape` : `tuple` containing the original image size as (height, width).
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Each result is composed of torch.Tensor by default, in which you can easily use following functionality:
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```python
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results = results.cuda()
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results = results.cpu()
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results = results.to("cpu")
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results = results.numpy()
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```
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### Boxes
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`Boxes` object can be used index, manipulate and convert bboxes to different formats. The box format conversion
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operations are cached, which means they're only calculated once per object and those values are reused for future calls.
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- Indexing a `Boxes` objects returns a `Boxes` object
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```python
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results = model(inputs)
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boxes = results[0].boxes
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box = boxes[0] # returns one box
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box.xyxy
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```
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- Properties and conversions
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```python
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boxes.xyxy # box with xyxy format, (N, 4)
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boxes.xywh # box with xywh format, (N, 4)
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boxes.xyxyn # box with xyxy format but normalized, (N, 4)
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boxes.xywhn # box with xywh format but normalized, (N, 4)
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boxes.conf # confidence score, (N, 1)
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boxes.cls # cls, (N, 1)
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boxes.data # raw bboxes tensor, (N, 6) or boxes.boxes .
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```
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### Masks
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`Masks` object can be used index, manipulate and convert masks to segments. The segment conversion operation is cached.
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```python
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results = model(inputs)
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masks = results[0].masks # Masks object
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masks.segments # bounding coordinates of masks, List[segment] * N
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masks.data # raw masks tensor, (N, H, W) or masks.masks
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```
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### probs
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`probs` attribute of `Results` class is a `Tensor` containing class probabilities of a classification operation.
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```python
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results = model(inputs)
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results[0].probs # cls prob, (num_class, )
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```
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Class reference documentation for `Results` module and its components can be found [here](reference/results.md)
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## Visualizing results
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You can use `visualize()` function of `Result` object to get a visualization. It plots all componenets(boxes, masks, classification logits, etc) found in the results object
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```python
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res = model(img)
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res_plotted = res[0].visualize()
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cv2.imshow("result", res_plotted)
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```
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!!! example "`visualize()` arguments"
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`show_conf (bool)`: Show confidence
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`line_width (Float)`: The line width of boxes. Automatically scaled to img size if not provided
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`font_size (Float)`: The font size of . Automatically scaled to img size if not provided
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