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Clean up YOLO post-processing

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This commit is contained in:
Anil Ozyalcin 2023-02-17 22:19:16 -08:00
parent 76fd29a3d6
commit 54fc0dbd3a
1 changed files with 28 additions and 54 deletions
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82
frigate/detectors/plugins/openvino.py
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@ -67,6 +67,18 @@ class OvDetector(DetectionApi):
self.grids = np.concatenate(grids, 1) self.grids = np.concatenate(grids, 1)
self.expanded_strides = np.concatenate(expanded_strides, 1) self.expanded_strides = np.concatenate(expanded_strides, 1)
## Takes in class ID, confidence score, and array of [x, y, w, h] that describes detection position,
## returns an array that's easily passable back to Frigate.
def process_yolo(self, class_id, conf, pos):
return [
class_id, # class ID
conf, # confidence score
(pos[1] - (pos[3] / 2)) / self.h, # y_min
(pos[0] - (pos[2] / 2)) / self.w, # x_min
(pos[1] + (pos[3] / 2)) / self.h, # y_max
(pos[0] + (pos[2] / 2)) / self.w, # x_max
]
def detect_raw(self, tensor_input): def detect_raw(self, tensor_input):
infer_request = self.interpreter.create_infer_request() infer_request = self.interpreter.create_infer_request()
infer_request.infer([tensor_input]) infer_request.infer([tensor_input])
@ -113,25 +125,11 @@ class OvDetector(DetectionApi):
ordered = dets[dets[:, 5].argsort()[::-1]][:20] ordered = dets[dets[:, 5].argsort()[::-1]][:20]
detections = np.zeros((20, 6), np.float32) detections = np.zeros((20, 6), np.float32)
i = 0
for object_detected in ordered: for i, object_detected in enumerate(ordered):
if i < 20: detections[i] = self.process_yolo(
detections[i] = [ object_detected[6], object_detected[5], object_detected[:4]
object_detected[6], # Label ID )
object_detected[5], # Confidence
(object_detected[1] - (object_detected[3] / 2))
/ self.h, # y_min
(object_detected[0] - (object_detected[2] / 2))
/ self.w, # x_min
(object_detected[1] + (object_detected[3] / 2))
/ self.h, # y_max
(object_detected[0] + (object_detected[2] / 2))
/ self.w, # x_max
]
i += 1
else:
break
return detections return detections
elif self.ov_model_type == ModelTypeEnum.yolov8: elif self.ov_model_type == ModelTypeEnum.yolov8:
out_tensor = infer_request.get_output_tensor() out_tensor = infer_request.get_output_tensor()
@ -148,25 +146,13 @@ class OvDetector(DetectionApi):
# limit to top 20 scores, descending order # limit to top 20 scores, descending order
ordered = dets[dets[:, -1].argsort()[::-1]][:20] ordered = dets[dets[:, -1].argsort()[::-1]][:20]
detections = np.zeros((20, 6), np.float32) detections = np.zeros((20, 6), np.float32)
i = 0
for object_detected in ordered: for i, object_detected in enumerate(ordered):
if i < 20: detections[i] = self.process_yolo(
detections[i] = [ np.argmax(object_detected[4:-1]),
np.argmax(object_detected[4:-1]), # Label ID object_detected[-1],
object_detected[-1], # Confidence object_detected[:4],
(object_detected[1] - (object_detected[3] / 2)) )
/ self.h, # y_min
(object_detected[0] - (object_detected[2] / 2))
/ self.w, # x_min
(object_detected[1] + (object_detected[3] / 2))
/ self.h, # y_max
(object_detected[0] + (object_detected[2] / 2))
/ self.w, # x_max
]
i += 1
else:
break
return detections return detections
elif self.ov_model_type == ModelTypeEnum.yolov5: elif self.ov_model_type == ModelTypeEnum.yolov5:
out_tensor = infer_request.get_output_tensor() out_tensor = infer_request.get_output_tensor()
@ -178,23 +164,11 @@ class OvDetector(DetectionApi):
ordered = output_data[output_data[:, 4].argsort()[::-1]][:20] ordered = output_data[output_data[:, 4].argsort()[::-1]][:20]
detections = np.zeros((20, 6), np.float32) detections = np.zeros((20, 6), np.float32)
i = 0
for object_detected in ordered: for i, object_detected in enumerate(ordered):
if i < 20: detections[i] = self.process_yolo(
detections[i] = [ np.argmax(object_detected[5:]),
np.argmax(object_detected[5:]), # Label ID object_detected[4],
object_detected[4], # Confidence object_detected[:4],
(object_detected[1] - (object_detected[3] / 2)) )
/ self.h, # y_min
(object_detected[0] - (object_detected[2] / 2))
/ self.w, # x_min
(object_detected[1] + (object_detected[3] / 2))
/ self.h, # y_max
(object_detected[0] + (object_detected[2] / 2))
/ self.w, # x_max
]
i += 1
else:
break
return detections return detections