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import cv2
import argparse
import json
import numpy as np
from tqdm import tqdm
from os.path import exists
import os
from segment_anything import sam_model_registry
from automatic_mask_generator import SamAutomaticMaskGenerator
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(description="Few Shot Counting Evaluation code")
parser.add_argument("-dp", "--data_path", type=str, default='/data/counte/', help="Path to the coco dataset")
parser.add_argument("-ts", "--test_split", type=str, default='val2017', choices=["val2017"], help="what data split to evaluate on")
parser.add_argument("-mt", "--model_type", type=str, default="vit_h", help="model type")
parser.add_argument("-mp", "--model_path", type=str, default="/home/teddy/segment-anything/sam_vit_h_4b8939.pth", help="path to trained model")
parser.add_argument("-v", "--viz", type=bool, default=True, help="wether to visualize")
parser.add_argument("-d", "--device", default='0', help='assign device')
args = parser.parse_args()
data_path = args.data_path
anno_file = data_path + 'annotations_trainval2017/annotations/instances_val2017.json'
im_dir = data_path + 'val2017'
if not exists(anno_file) or not exists(im_dir):
print("Make sure you set up the --data-path correctly.")
print("Current setting is {}, but the image dir and annotation file do not exist.".format(args.data_path))
print("Aborting the evaluation")
exit(-1)
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
for ann in sorted_anns:
x0, y0, w, h = ann['bbox']
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))
ax.scatter([x0+w//2], [y0+h//2], color='green', marker='*', s=10, edgecolor='white', linewidth=1.25)
debug = True
os.environ['CUDA_VISIBLE_DEVICES'] = args.device.strip()
device = 'cuda'
sam = sam_model_registry[args.model_type](checkpoint=args.model_path)
sam.to(device=device)
mask_generator = SamAutomaticMaskGenerator(
model=sam,
min_mask_region_area=25
)
with open(anno_file) as f:
annotations = json.load(f)
images = sorted(annotations['images'],key=lambda x:x['file_name'])
prepared_json = {}
for i in images:
prepared_json[i['file_name']] = {
"H":i['height'],
"W":i['width'],
"boxes":{},
# "category_ids":[],
}
for i in annotations['annotations']:
im_id = str(i['image_id'])
prezero = 12 - len(im_id)
im_id = '0'*prezero + im_id + ".jpg"
if i["category_id"] in prepared_json[im_id]["boxes"]:
prepared_json[im_id]["boxes"][i["category_id"]].append(i['bbox'])
else:
prepared_json[im_id]["boxes"][i["category_id"]] = []
prepared_json[im_id]["boxes"][i["category_id"]].append(i['bbox'])
im_ids = []
for i in prepared_json.keys():
im_ids.append(i)
cnt = 0
folds = [
[1,5,9,14,18,22,27,33,37,41,46,50,54,58,62,67,74,78,82,87],
[2,6,10,15,19,23,28,34,38,42,47,51,55,59,63,70,75,79,84,88],
[3,7,11,16,20,24,31,35,39,43,48,52,56,60,64,72,76,80,85,89],
[4,8,13,17,21,25,32,36,40,44,49,53,57,61,65,73,77,81,86,90],
]
SAE = [0,0,0,0] # sum of absolute errors
SSE = [0,0,0,0] # sum of square errors
print("Evaluation on {} data".format(args.test_split))
# logs = []
pbar = tqdm(im_ids)
# err_list = []
for im_id in pbar:
category_id = list(prepared_json[im_id]['boxes'].keys())
image = cv2.imread('{}/{}'.format(im_dir, im_id))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# log = []
# log.append(im_id)
for id in category_id:
boxes = prepared_json[im_id]['boxes'][id]
input_boxes = list()
x1, y1 = boxes[0][0],boxes[0][1]
x2, y2 = boxes[0][0] + boxes[0][2],boxes[0][1] + boxes[0][3]
input_boxes.append([x1, y1, x2, y2])
masks = mask_generator.generate(image, input_boxes)
if args.viz:
if not exists('viz'):
os.mkdir('viz')
plt.figure(figsize=(10,10))
plt.imshow(image)
show_anns(masks)
plt.axis('off')
plt.savefig('viz/{}_{}.jpg'.format(im_id[0:-4],id))
plt.close()
gt_cnt = len(boxes)
pred_cnt = len(masks)
err = abs(gt_cnt - pred_cnt)
log.append("\n{},gt_cnt:{},pred_cnt:{}".format(id,gt_cnt,pred_cnt))
if id in folds[0]:
SAE[0] += err
SSE[0] += err**2
elif id in folds[1]:
SAE[1] += err
SSE[1] += err**2
elif id in folds[2]:
SAE[2] += err
SSE[2] += err**2
elif id in folds[3]:
SAE[3] += err
SSE[3] += err**2
cnt = cnt + 1
# logs.append(log)
pbar.set_description('fold1: {:6.2f}, fold2: {:6.2f}, fold3: {:6.2f}, fold4: {:6.2f},'.\
format(SAE[0]/cnt,SAE[1]/cnt,SAE[2]/cnt,SAE[3]/cnt))
print('On {} data, fold1 MAE: {:6.2f}, RMSE: {:6.2f}\n \
fold2 MAE: {:6.2f}, RMSE: {:6.2f}\n \
fold3 MAE: {:6.2f}, RMSE: {:6.2f}\n \
fold4 MAE: {:6.2f}, RMSE: {:6.2f}\n \
'.format(args.test_split,SAE[0]/cnt,(SSE[0]/cnt)**0.5,SAE[1]/cnt,(SSE[1]/cnt)**0.5,SAE[2]/cnt,(SSE[2]/cnt)**0.5,SAE[3]/cnt,(SSE[3]/cnt)**0.5)) |