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Code Issues Actions 2 Packages Projects Releases Wiki Activity

Removed unnecesarry comments, improved documentation, addressed PR items

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This commit is contained in:
Anil Ozyalcin 2023-01-29 19:40:45 -08:00
parent bf934a7651
commit a3bd13b7ea
3 changed files with 6 additions and 8 deletions
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5
docs/docs/configuration/detectors.md
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@ -116,7 +116,6 @@ model:
height: 300 height: 300
input_tensor: nhwc input_tensor: nhwc
input_pixel_format: bgr input_pixel_format: bgr
model_type: ssd
labelmap_path: /openvino-model/coco_91cl_bkgr.txt labelmap_path: /openvino-model/coco_91cl_bkgr.txt
``` ```
@ -128,7 +127,7 @@ detectors:
type: openvino type: openvino
device: AUTO device: AUTO
model: model:
path: /<path>/<to>/yolox_tiny.xml path: /path/to/yolox_tiny.xml
model: model:
width: 416 width: 416
@ -136,7 +135,7 @@ model:
input_tensor: nchw input_tensor: nchw
input_pixel_format: bgr input_pixel_format: bgr
model_type: yolox model_type: yolox
labelmap_path: /<path>/<to>/coco_80cl.txt labelmap_path: /path/to/coco_80cl.txt
``` ```
### Intel NCS2 VPU and Myriad X Setup ### Intel NCS2 VPU and Myriad X Setup

3
docs/docs/configuration/index.md
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@ -105,6 +105,9 @@ model:
# Optional: Object detection model input tensor format # Optional: Object detection model input tensor format
# Valid values are nhwc or nchw (default: shown below) # Valid values are nhwc or nchw (default: shown below)
input_tensor: nhwc input_tensor: nhwc
# Optional: Object detection model type, currently only used with the OpenVINO detector
# Valid values are ssd or yolox (default: shown below)
model_type: ssd
# Optional: Label name modifications. These are merged into the standard labelmap. # Optional: Label name modifications. These are merged into the standard labelmap.
labelmap: labelmap:
2: vehicle 2: vehicle

6
frigate/detectors/plugins/openvino.py
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@ -39,7 +39,6 @@ class OvDetector(DetectionApi):
try: try:
tensor_shape = self.interpreter.output(self.output_indexes).shape tensor_shape = self.interpreter.output(self.output_indexes).shape
logger.info(f"Model Output-{self.output_indexes} Shape: {tensor_shape}") logger.info(f"Model Output-{self.output_indexes} Shape: {tensor_shape}")
logger.info(f"Model Output-{self.output_indexes} Shape: {tensor_shape}")
self.output_indexes += 1 self.output_indexes += 1
except: except:
logger.info(f"Model has {self.output_indexes} Output Tensors") logger.info(f"Model has {self.output_indexes} Output Tensors")
@ -93,9 +92,9 @@ class OvDetector(DetectionApi):
] ]
i += 1 i += 1
return detections return detections
elif(self.ov_model_type == ModelTypeEnum.yolox): elif(self.ov_model_type == ModelTypeEnum.yolox):
out_tensor = infer_request.get_output_tensor() out_tensor = infer_request.get_output_tensor()
# [x, y, h, w, box_score, class_no_1, ..., class_no_80],
results = out_tensor.data results = out_tensor.data
results[..., :2] = (results[..., :2] + self.grids) * self.expanded_strides results[..., :2] = (results[..., :2] + self.grids) * self.expanded_strides
results[..., 2:4] = np.exp(results[..., 2:4]) * self.expanded_strides results[..., 2:4] = np.exp(results[..., 2:4]) * self.expanded_strides
@ -117,7 +116,6 @@ class OvDetector(DetectionApi):
for object_detected in ordered: for object_detected in ordered:
if i < 20: if i < 20:
# [x, y, h, w, box_score, class_no_1, ..., class_no_80],
detections[i] = [ detections[i] = [
object_detected[6], # Label ID object_detected[6], # Label ID
object_detected[5], # Confidence object_detected[5], # Confidence
@ -126,8 +124,6 @@ class OvDetector(DetectionApi):
(object_detected[1]+(object_detected[3]/2))/self.h, # y_max (object_detected[1]+(object_detected[3]/2))/self.h, # y_max
(object_detected[0]+(object_detected[2]/2))/self.w, # x_max (object_detected[0]+(object_detected[2]/2))/self.w, # x_max
] ]
#logger.info(object_detected)
#logger.info(detections[i])
i += 1 i += 1
else: else:
break break