what.models.detection.yolo.yolov4
1import cv2 2import numpy as np 3 4from keras.models import load_model 5import tensorflow.keras.backend as K 6 7from what.models.detection.utils.time_utils import Timer 8 9from .utils.yolo_utils import yolo_process_output, yolov4_anchors 10 11def mish(x): 12 return x * K.tanh(K.softplus(x)) 13 14class YOLOV4: 15 def __init__(self, class_names, model_path): 16 self.model = load_model(model_path, custom_objects = { 17 'mish': mish 18 }) 19 self.class_names = class_names 20 self.timer = Timer() 21 22 def predict(self, image, top_k=-1, prob_threshold=None): 23 input_cv_image = cv2.resize(image, (416, 416)) 24 input_cv_image = np.array(input_cv_image).astype(np.float32) / 255.0 25 26 # Yolo inference 27 self.timer.start() 28 outs = self.model.predict(np.array([input_cv_image])) 29 print("FPS: ", int(1.0 / self.timer.end())) 30 31 boxes, class_ids, confidences = yolo_process_output(outs, yolov4_anchors, len(self.class_names)) 32 33 return input_cv_image, boxes, class_ids, confidences
def
mish(x):
class
YOLOV4:
15class YOLOV4: 16 def __init__(self, class_names, model_path): 17 self.model = load_model(model_path, custom_objects = { 18 'mish': mish 19 }) 20 self.class_names = class_names 21 self.timer = Timer() 22 23 def predict(self, image, top_k=-1, prob_threshold=None): 24 input_cv_image = cv2.resize(image, (416, 416)) 25 input_cv_image = np.array(input_cv_image).astype(np.float32) / 255.0 26 27 # Yolo inference 28 self.timer.start() 29 outs = self.model.predict(np.array([input_cv_image])) 30 print("FPS: ", int(1.0 / self.timer.end())) 31 32 boxes, class_ids, confidences = yolo_process_output(outs, yolov4_anchors, len(self.class_names)) 33 34 return input_cv_image, boxes, class_ids, confidences
def
predict(self, image, top_k=-1, prob_threshold=None):
23 def predict(self, image, top_k=-1, prob_threshold=None): 24 input_cv_image = cv2.resize(image, (416, 416)) 25 input_cv_image = np.array(input_cv_image).astype(np.float32) / 255.0 26 27 # Yolo inference 28 self.timer.start() 29 outs = self.model.predict(np.array([input_cv_image])) 30 print("FPS: ", int(1.0 / self.timer.end())) 31 32 boxes, class_ids, confidences = yolo_process_output(outs, yolov4_anchors, len(self.class_names)) 33 34 return input_cv_image, boxes, class_ids, confidences