what.models.detection.yolo.yolov4 

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