predicted_class = self.class_names[int(c)]
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
centernet = mAP_CenterNet()
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 tqdm(image_ids):
image_path = "./VOCdevkit/VOC2007/JPEGImages/" + image_id + ".jpg"
image = Image.open(image_path)
# image.save("./input/images-optional/"+image_id+".jpg")
centernet.detect_image(image_id, image)
print("Conversion completed!")
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
result["image_id"] = int(image_id)
result["category_id"] = clsid2catid[c]
result["bbox"] = [
float(left),
float(top),
float(right - left),
float(bottom - top)
]
result["score"] = float(top_conf[i])
results.append(result)
return results
centernet = mAP_CenterNet()
jpg_names = os.listdir("./coco_dataset/val2017")
with open("./coco_dataset/eval_results.json", "w") as f:
results = []
for jpg_name in tqdm(jpg_names):
if jpg_name.endswith("jpg"):
image_path = "./coco_dataset/val2017/" + jpg_name
image = Image.open(image_path)
# 开启后在之后计算mAP可以可视化
results = centernet.detect_image(
jpg_name.split(".")[0], image, results)
json.dump(results, f)