resultPath = os.path.split(FLAGS.model_path)[0]
resultPath = os.path.join(resultPath, 'detections.txt')
resultFile = open(resultPath, 'w')
firstStart = time.time()
cont = testFile.readlines()
for i in range(0, len(cont)):
print("detecting image: {}".format(cont[i].rstrip()))
imgPath = imgsPath + cont[i].rstrip() + '.jpg'
try:
image = Image.open(imgPath)
except:
print('Image Open Error')
out_boxes, out_scores, out_classes = yolo.get_boxes(image)
if visualise:
r_image = yolo.detect_image(image)
r_image.show()
for idx, c in reversed(list(enumerate(out_classes))):
predicted_class = 'plum'
box = out_boxes[idx]
score = out_scores[idx]
top, left, bottom, right = box #left,top,right,bottom = x0,y0,x1,y1
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
#-------------------------------------#
# 对单张图片进行预测
#-------------------------------------#
from yolo import YOLO
from PIL import Image
import numpy as np
yolo = YOLO()
while True:
#img = input('Input image filename:')
img = '../input/global-wheat-detection/test/cc3532ff6.jpg'
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
boxes = yolo.get_boxes(image)
#r_image = yolo.detect_image(image)
for i in boxes:
print(i)
break
