52 lines
2.1 KiB
Python
52 lines
2.1 KiB
Python
import os
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import numpy as np
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import cv2
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import onnxruntime
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#kinda split on how to handle this one
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#It's the cause of all of my slowest frames
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#but it's not run often
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#and I don't know how to make it faster
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class FaceDetector():
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def __init__(self, detection_threshold=0.6):
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model_base_path = os.path.join(os.path.dirname(__file__), os.path.join("models"))
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providersList = onnxruntime.capi._pybind_state.get_available_providers()
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options = onnxruntime.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = 1
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options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
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options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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options.log_severity_level = 3
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self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options, providers=providersList)
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self.mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
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self.std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
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self.detection_threshold = detection_threshold
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def detect_faces(self, frame):
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im = cv2.resize(frame, (224, 224), interpolation=cv2.INTER_LINEAR) * self.std + self.mean
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im = np.expand_dims(im, 0)
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im = np.transpose(im, (0,3,1,2))
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outputs, maxpool = self.detection.run([], {'input': im})
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outputs[0, 0, outputs[0, 0] != maxpool[0, 0]] = 0
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detections = np.flip(np.argsort(outputs[0,0].flatten()))
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results = []
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if len(detections) == 0:
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return results
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x = detections[0] % 56
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y = detections[0] // 56
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if outputs[0, 0, y, x] < self.detection_threshold:
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return results
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r = outputs[0, 1, y, x] * 112.
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results.append(((x * 4) - r, (y * 4) - r, r*2,r*2))
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results = np.array(results).astype(np.float32)
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results[:, [0,2]] *= frame.shape[1] / 224.
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results[:, [1,3]] *= frame.shape[0] / 224.
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return results
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