Update for training

Edmond Yoo

2018-09-14 8eb0d400e80f3dd135313834ce103bd0f9e62dbc

 darknet.py

@@ -31,6 +31,7 @@
import math
import random
import os
import cv2

def sample(probs):
    s = sum(probs)
@@ -421,5 +422,49 @@
            print("Unable to show image: "+str(e))
    return detections


def capture(thresh=.5, hier_thresh=.5, nms=.45, configPath="./cfg/yolov3.cfg", weightPath="yolov3.weights",
            metaPath="./data/coco.data", showImage=True, makeImageOnly=False, initOnly=False):
    global metaMain, netMain, altNames  # pylint: disable=W0603
    netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1)  # batch size = 1
    metaMain = load_meta(metaPath.encode("ascii"))

    num = c_int(0)
    pnum = pointer(num)
    num = pnum[0]

    capture = cv2.VideoCapture('../data/test3.mp4')
    print(capture.get(cv2.CAP_PROP_FPS))

    capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1024)
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 768)

    while True:
        ret, frame = capture.read()
        im, arr = array_to_image(frame)
        predict_image(netMain, im)
        dets = get_network_boxes(netMain, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 1)
        if nms:
            do_nms_sort(dets, num, metaMain.classes, nms)
        res = []
        for j in range(num):
            for i in range(metaMain.classes):
                if dets[j].prob[i] > 0:
                    b = dets[j].bbox
                    nameTag = metaMain.names[i]
                    res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h)))
        print(res)
        cv2.imshow('frame', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    capture.release()
    cv2.destroyAllWindows()


if __name__ == "__main__":
    print(performDetect())
    performDetect(imagePath="../data/test1.jpg", thresh=0.25, configPath="./cfg/tiny_yolo.cfg",
                  weightPath="./weights/second_general/tiny_yolo_17000.weights",
                  metaPath="./data/obj.data", showImage=True, makeImageOnly=False, initOnly=False)
    #print(performDetect(showImage=False))
    #capture()