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#!/usr/bin/python
#****************************************************************#
# ScriptName: analysis_data.py
# Author: Anonymous_123
# Create Date: 2022-07-25 19:54
# Modify Author: Anonymous_123
# Modify Date: 2022-09-25 12:04
# Function:
#***************************************************************#
import os
import sys
import numpy as np
import cv2
import torch
from tqdm import tqdm
import shutil
import pdb
import argparse
parser = argparse.ArgumentParser(description='resize object')
parser.add_argument('--scale', type=float, default=None, help='object scale')
parser.add_argument('--img_path', type=str, help='image path')
parser.add_argument('--mask_path', type=str, help='mask path')
def get_bbox_and_rate(mask):
gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
if len(contours) == 0:
return None, None
max_area = 0
max_idx = 0
for i, cnt in enumerate(contours):
x,y,w,h = cv2.boundingRect(cnt)
if w*h > max_area:
max_idx = i
max_area = w*h
# 外接矩形
x,y,w,h = cv2.boundingRect(contours[max_idx])
mask_new = np.zeros(mask.shape, dtype='uint8')
mask_new[y:y+h, x:x+w, :] = mask[y:y+h, x:x+w, :]
rate = (mask_new[:,:,0]>127.5).sum()/mask.shape[0]/mask.shape[1]
return (x,y,w,h), rate
def resize_around_the_center(img, mask, bbox, operation, scale_step=1.2):
x,y,w,h = bbox
H,W,C = mask.shape
obj_mask = mask[y:y+h, x:x+w, :].copy()
# obj_mask = cv2.resize(obj_mask, (int(w*scale_step),int(h*scale_step)) if operation == 'upsample' else (int(w/scale_step), int(h/scale_step)))
obj_mask = cv2.resize(obj_mask, (int(w*scale_step),int(h*scale_step)))
start_point_x = max(x+w//2 - obj_mask.shape[1]//2, 0) # center - w
start_point_y = max(y+h//2 - obj_mask.shape[0]//2, 0) # center - h
end_point_x = min(x+w//2 + obj_mask.shape[1]//2, W) # center+w
end_point_y = min(y+h//2 + obj_mask.shape[0]//2, H) # center+h
start_point_x_obj = max(0,obj_mask.shape[1]//2-(x+w//2))
start_point_y_obj = max(0, obj_mask.shape[0]//2-(y+h//2))
mask[:] = 0
mask[start_point_y:end_point_y, start_point_x:end_point_x] = obj_mask[start_point_y_obj:start_point_y_obj+(end_point_y-start_point_y), start_point_x_obj:start_point_x_obj+(end_point_x-start_point_x)]
obj_img = img[y:y+h, x:x+w, :].copy()
# obj_img = cv2.resize(obj_img, (int(w*scale_step),int(h*scale_step)) if operation == 'upsample' else (int(w/scale_step), int(h/scale_step)))
obj_img = cv2.resize(obj_img, (int(w*scale_step),int(h*scale_step)))
img = cv2.GaussianBlur(img, (49, 49), 0)
img[start_point_y:end_point_y, start_point_x:end_point_x] = obj_img[start_point_y_obj:start_point_y_obj+(end_point_y-start_point_y), start_point_x_obj:start_point_x_obj+(end_point_x-start_point_x)]
return img, mask
def resize_around_the_center_padding(img, mask, bbox, scale_step=1.2):
x,y,w,h = bbox
H,W,C = mask.shape
mask_new = np.zeros((int(H/scale_step), int(W/scale_step), 3), dtype='uint8')
mask_new_full = np.zeros((int(H/scale_step), int(W/scale_step), 3), dtype='uint8')
# img_new = np.zeros((int(H/scale_step), int(W/scale_step), 3), dtype='uint8')
img_new = cv2.resize(img, (int(W/scale_step), int(H/scale_step)))
if scale_step < 1:
mask_new[int((y+h/2)*(1/scale_step-1)):int((y+h/2)*(1/scale_step-1)+H), int((x+w/2)*(1/scale_step-1)):int((x+w/2)*(1/scale_step-1)+W)] = mask
mask_new_full[int((y+h/2)*(1/scale_step-1)):int((y+h/2)*(1/scale_step-1)+H), int((x+w/2)*(1/scale_step-1)):int((x+w/2)*(1/scale_step-1)+W)] = mask.max()*np.ones(mask.shape, dtype='uint8')
img_new[int((y+h/2)*(1/scale_step-1)):int((y+h/2)*(1/scale_step-1)+H), int((x+w/2)*(1/scale_step-1)):int((x+w/2)*(1/scale_step-1)+W)] = img
else:
mask_new = mask[int((y+h/2)*(1-1/scale_step)):int((y+h/2)*(1-1/scale_step))+int(H/scale_step), int((x+w/2)*(1-1/scale_step)):int((x+w/2)*(1-1/scale_step))+int(W/scale_step)]
mask_new_full = mask[int((y+h/2)*(1-1/scale_step)):int((y+h/2)*(1-1/scale_step))+int(H/scale_step), int((x+w/2)*(1-1/scale_step)):int((x+w/2)*(1-1/scale_step))+int(W/scale_step)]
img_new = img[int((y+h/2)*(1-1/scale_step)):int((y+h/2)*(1-1/scale_step))+int(H/scale_step), int((x+w/2)*(1-1/scale_step)):int((x+w/2)*(1-1/scale_step))+int(W/scale_step)]
img_new = cv2.resize(img_new, (W,H))
mask_new = cv2.resize(mask_new, (W,H))
mask_new_full = cv2.resize(mask_new_full, (W,H))
return img_new, mask_new, mask_new_full
def rescale(img, mask, scale=None, max_steps=50):
bbox, rate = get_bbox_and_rate(mask)
if bbox is None:
return None, None, None
num_steps = 0
mask_full = mask.copy()
while np.floor(rate*100) != scale*100. and abs(rate-scale) > 0.015:
# while not (abs(bbox[0]-0)<10 or abs(bbox[1]-0)<10 or abs(bbox[0]+bbox[2]-img.shape[1])<10 or abs(bbox[1]+bbox[3]-img.shape[0])<10):
operation = 'upsample' if np.floor(rate*100) < scale*100. else 'downsample'
scale_step = np.sqrt(scale/rate)
# img, mask = resize_around_the_center(img, mask, bbox, operation, scale_step=scale_step)
img, mask, mask_full = resize_around_the_center_padding(img, mask, bbox, scale_step=scale_step)
bbox, rate_ = get_bbox_and_rate(mask)
if (operation == 'upsample' and rate_ < rate) or (operation == 'downsample' and rate_ > rate):
return None, None, None
num_steps += 1
rate = rate_
print(rate)
if num_steps > max_steps:
return None, None, None
return img, mask_full, mask
def rescale_maximum(img, mask, scale=None, max_steps=50):
bbox, rate = get_bbox_and_rate(mask)
if bbox is None:
return None, None, None
x,y,w,h = bbox
H,W,C = img.shape
if H/h < W/w:
y_start, y_end = y, y+h
new_w = w/H*h
c_x = x + w//2
c_x_new = new_w*c_x/W
x_start = c_x - c_x_new
x_end = x_start + new_w
else:
x_start, x_end = x, x+w
new_h = h/W*w
c_y = y+h//2
c_y_new = new_h*c_y/H
y_start = c_y - c_y_new
y_end = y_start + new_h
img_new = img[min(y, int(y_start)):max(int(y_end), y+h), min(x, int(x_start)):max(int(x_end),x+w), :]
mask_new = mask[min(y, int(y_start)):max(int(y_end),y+h),min(x, int(x_start)):max(int(x_end),x+w),:]
img_new = cv2.resize(img_new, (W,H))
mask_new = cv2.resize(mask_new, (W,H))
return img_new, mask_new, mask_new
if __name__ == '__main__':
args = parser.parse_args()
scale = args.scale
img_path_save = 'results/img_rescaled.png'
mask_path_save = 'results/mask_rescaled.png'
if scale == None:
shutil.copy(args.img_path, img_path_save)
shutil.copy(args.mask_path, mask_path_save)
else:
try:
finals = []
img = cv2.imread(args.img_path)
mask = cv2.imread(args.mask_path)
img_rescale, mask_rescale, mask_obj = rescale_maximum(img.copy(), mask.copy(), scale=scale)
bbox, max_rate = get_bbox_and_rate(mask_obj)
if scale < max_rate:
img_rescale, mask_rescale, mask_obj = rescale(img.copy(), mask.copy(), scale=scale)
if img_rescale is None:
print('Invalid size')
shutil.copy(args.img_path, img_path_save)
shutil.copy(args.mask_path, mask_path_save)
sys.exit()
final = [img, img_rescale, mask, mask_rescale, mask_obj]
# cv2.imwrite('tmp.png', cv2.hconcat(final))
cv2.imwrite(img_path_save, img_rescale)
cv2.imwrite(mask_path_save, mask_obj)
# cv2.imwrite(mask_path_save_full, mask_rescale)
except:
print('Invalid size, using the original one')
shutil.copy(args.img_path, img_path_save)
shutil.copy(args.mask_path, mask_path_save)
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