67 lines
2.3 KiB
Python
67 lines
2.3 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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import time
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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 FaceDetectionModel():
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def __init__(self):
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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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def run(self, image):
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outputs, maxpool = self.detection.run([], {'input': image})
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return outputs[0]
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class FaceDetector():
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def __init__(self, detection_threshold=0.6):
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self.model = FaceDetectionModel()
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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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self.targetimageSize = [224, 224]
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self.image = None
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def detect_faces(self, frame):
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self.resizeImage(frame)
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outputs = self.model.run(self.image)
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faceLocation = np.argmax(outputs[0].flatten())
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x = faceLocation % 56
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y = faceLocation // 56
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if outputs[0, y, x] < self.detection_threshold:
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return []
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r = outputs[1, y, x] * 112.
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results= (((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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def resizeImage(self, frame):
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image = cv2.resize(frame, self.targetimageSize, interpolation=cv2.INTER_LINEAR) * self.std + self.mean
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image = np.expand_dims(image, 0)
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image = np.transpose(image, (0,3,1,2))
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self.image = image
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