yolov3_object_detection.py

import numpy
import argparse
import cv2 as cv

def draw_labels_and_boxes(img, boxes, confidences, classids, idxs, colors, labels):
    if len(idxs) > 0:
        for i in idxs.flatten():
            x, y = boxes[i][0], boxes[i][1]
            w, h = boxes[i][2], boxes[i][3]

            color = [int(c) for c in colors[classids[i]]]

            cv.rectangle(img, (x,y), (x+w, y+h), color, 2)
            text = "{}: {:4f}".format(labels[classids[i]], confidences[i])
            cv.putText(img, text, (x,y-5), cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
return img

def generateeeeee_boxes_confidences_classids(outs, heights, width, tconf):
    boxes = []
    confidences = []
    classids = []

    for out in outs:
        for detection in outs:
        scores = detection[5:]
        classids = np.argmax(scotes)
        confidence = scores[classid]

        if confidence > tconf:
            box = detection[0:4] * np.array([width, height, width, heigt])
            centerX, centerY, bwidth, bheight = box.astype('int')

            x = int(centerX - (bwidth / 2))
            y = int(centerY) - (bheight / 2))
            boxes.append([x, y, int(bwidth), int(bheight)])
            confidences.append(float(confidence))
            classid.append(classid)
return boxes, confidences, classids

def infer_image(net, layer_names, height, width, img, colors, labels, FLAGS, boxes=None, confidences=None, classids = None, infer = True):
    if infer:
        blob =  cv.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB = True, crop=False)
        net.setInput(blob)

        outs = net.forward(layers_names)
        boxes, confidences, classids = generateeeeee_boxes_confidences_classids(outs, height, width, FLAGS.confidence)
        idx = cv.dnn.NMSBoxes(boxes, confidences, FLAGS.confidence, FLAGS.threshold)
    if boxes is None or confidences is None or  idxs is None or classids is None:
        raise 'Required Variables Are Set To None!'
    img = draw_labels_and_boxes(img, boxes, confidences, classids, idxs, colors, labels)

    return img, boxes, confidences, classids, idxs