yolov5_tflite_inference.py

import tflite_runtime.interpreter as tflite
import numpy as np




class yolov5_tflite:

    def __init__(self,weights = 'yolov5s-fp16.tflite',image_size = 416,conf_thres=0.25,iou_thres=0.45):

        self.weights = weights
        self.image_size = image_size
        self.conf_thres = conf_thres
        self.iou_thres = iou_thres
        self.interpreter = tflite.Interpreter(self.weights)
        self.interpreter.allocate_tensors()
        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()

        with open('class_names.txt') as f:
                self.names = [line.rstrip() for line in f]



    def xywh2xyxy(self,x):
        # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
        y = x.copy()
        y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
        y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
        y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
        y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
        return y
    

    def non_max_suppression(self,boxes, scores, threshold):	
        assert boxes.shape[0] == scores.shape[0]
        # bottom-left origin
        ys1 = boxes[:, 0]
        xs1 = boxes[:, 1]
        # top-right target
        ys2 = boxes[:, 2]
        xs2 = boxes[:, 3]
        # box coordinate ranges are inclusive-inclusive
        areas = (ys2 - ys1) * (xs2 - xs1)
        scores_indexes = scores.argsort().tolist()
        boxes_keep_index = []
        while len(scores_indexes):
            index = scores_indexes.pop()
            boxes_keep_index.append(index)
            if not len(scores_indexes):
                break
            ious = self.compute_iou(boxes[index], boxes[scores_indexes], areas[index],
                            areas[scores_indexes])
            filtered_indexes = set((ious > threshold).nonzero()[0])
            # if there are no more scores_index
            # then we should pop it
            scores_indexes = [
                v for (i, v) in enumerate(scores_indexes)
                if i not in filtered_indexes
            ]
        return np.array(boxes_keep_index)


    def compute_iou(self,box, boxes, box_area, boxes_area):
        # this is the iou of the box against all other boxes
        assert boxes.shape[0] == boxes_area.shape[0]
        # get all the origin-ys
        # push up all the lower origin-xs, while keeping the higher origin-xs
        ys1 = np.maximum(box[0], boxes[:, 0])
        # get all the origin-xs
        # push right all the lower origin-xs, while keeping higher origin-xs
        xs1 = np.maximum(box[1], boxes[:, 1])
        # get all the target-ys
        # pull down all the higher target-ys, while keeping lower origin-ys
        ys2 = np.minimum(box[2], boxes[:, 2])
        # get all the target-xs
        # pull left all the higher target-xs, while keeping lower target-xs
        xs2 = np.minimum(box[3], boxes[:, 3])
        # each intersection area is calculated by the
        # pulled target-x minus the pushed origin-x
        # multiplying
        # pulled target-y minus the pushed origin-y
        # we ignore areas where the intersection side would be negative
        # this is done by using maxing the side length by 0
        intersections = np.maximum(ys2 - ys1, 0) * np.maximum(xs2 - xs1, 0)
        # each union is then the box area
        # added to each other box area minusing their intersection calculated above
        unions = box_area + boxes_area - intersections
        # element wise division
        # if the intersection is 0, then their ratio is 0
        ious = intersections / unions
        return ious




    def nms(self,prediction):



        
        prediction = prediction[prediction[...,4] > self.conf_thres]
        
        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
        boxes = self.xywh2xyxy(prediction[:, :4])
        
        res = self.non_max_suppression(boxes,prediction[:,4],self.iou_thres)
        
        result_boxes = []
        result_scores = []
        result_class_names = []
        for r in res:
            result_boxes.append(boxes[r])
            result_scores.append(prediction[r,4])
            result_class_names.append(self.names[np.argmax(prediction[r,5:])])

        
        return result_boxes, result_scores, result_class_names

    

    def detect(self,image):
        original_size = image.shape[:2]
        input_data = np.ndarray(shape=(1, self.image_size, self.image_size, 3), dtype=np.float32)
        #image = cv2.resize(image,(self.image_size,self.image_size))
        #input_data[0] = image.astype(np.float32)/255.0
        input_data[0] = image
        
        #self.interpreter.allocate_tensors()

        # Get input and output tensors
        #input_details = self.interpreter.get_input_details()
        #output_details = self.interpreter.get_output_details()


        self.interpreter.set_tensor(self.input_details[0]['index'], input_data)
        self.interpreter.invoke()
        pred = self.interpreter.get_tensor(self.output_details[0]['index'])

        # Denormalize xywh
        pred[..., 0] *= original_size[1]  # x
        pred[..., 1] *= original_size[0]  # y
        pred[..., 2] *= original_size[1]  # w
        pred[..., 3] *= original_size[0]  # h

        
        
        result_boxes, result_scores, result_class_names = self.nms(pred)

        return result_boxes,result_scores, result_class_names