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from keras.layers import Input
from ssd import SSD
from PIL import Image
from keras.applications.imagenet_utils import preprocess_input
from utils.utils import BBoxUtility,letterbox_image,ssd_correct_boxes
import numpy as np
import os
class mAP_SSD(SSD):
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self,image_id,image):
self.confidence = 0.05
f = open("./input/detection-results/"+image_id+".txt","a")
image_shape = np.array(np.shape(image)[0:2])
crop_img,x_offset,y_offset = letterbox_image(image, (self.model_image_size[0],self.model_image_size[1]))
photo = np.array(crop_img,dtype = np.float64)
# 图片预处理,归一化
photo = preprocess_input(np.reshape(photo,[1,self.model_image_size[0],self.model_image_size[1],3]))
preds = self.ssd_model.predict(photo)
# 将预测结果进行解码
results = self.bbox_util.detection_out(preds, confidence_threshold=self.confidence)
if len(results[0])<=0:
f.close()
return
# 筛选出其中得分高于confidence的框
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin, det_ymin, det_xmax, det_ymax = results[0][:, 2], results[0][:, 3], results[0][:, 4], results[0][:, 5]
top_indices = [i for i, conf in enumerate(det_conf) if conf >= self.confidence]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(det_xmin[top_indices],-1),np.expand_dims(det_ymin[top_indices],-1),np.expand_dims(det_xmax[top_indices],-1),np.expand_dims(det_ymax[top_indices],-1)
# 去掉灰条
boxes = ssd_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape)
for i, c in enumerate(top_label_indices):
predicted_class = self.class_names[int(c)-1]
score = str(top_conf[i])
top, left, bottom, right = boxes[i]
f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))
f.close()
return
ssd = mAP_SSD()
image_ids = open('VOCdevkit/VOC2007/ImageSets/Main/test.txt').read().strip().split()
if not os.path.exists("./input"):
os.makedirs("./input")
if not os.path.exists("./input/detection-results"):
os.makedirs("./input/detection-results")
if not os.path.exists("./input/images-optional"):
os.makedirs("./input/images-optional")
for image_id in image_ids:
image_path = "./VOCdevkit/VOC2007/JPEGImages/"+image_id+".jpg"
image = Image.open(image_path)
image.save("./input/images-optional/"+image_id+".jpg")
ssd.detect_image(image_id,image)
print(image_id," done!")
print("Conversion completed!")
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