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show_learning_points_and_boxes.py
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show_learning_points_and_boxes.py
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# -*- coding: utf-8 -*-
import pickle
import cv2
import json
import os
import shutil
import numpy as np
detection_pkl_path = 'work_dirs/r50_dotav1/results.pkl'
val_json = 'data/dataset_demo_split/test_datasetdemo.json'
outpath = 'work_dirs/r50_dotav1/vis_result/'
inputpath = 'data/dataset_demo_split/images/'
if os.path.exists(outpath):
shutil.rmtree(outpath) # delete output folderX
os.makedirs(outpath)
conf_thresh = 0.2
def transfer_to_order_point(corner_points):
a_x = corner_points[0][0]
a_y = corner_points[0][1]
b_x = corner_points[1][0]
b_y = corner_points[1][1]
c_x = corner_points[2][0]
c_y = corner_points[2][1]
d_x = corner_points[3][0]
d_y = corner_points[3][1]
# top x0 y0
if a_y == min(a_y, b_y, c_y, d_y) :
x0 = a_x
y0 = a_y
if b_y == min(a_y, b_y, c_y, d_y) :
x0 = b_x
y0 = b_y
if c_y == min(a_y, b_y, c_y, d_y) :
x0 = c_x
y0 = c_y
if d_y == min(a_y, b_y, c_y, d_y) :
x0 = d_x
y0 = d_y
# right x1 y1
if a_x == max(a_x, b_x, c_x, d_x) :
x1 = a_x
y1 = a_y
if b_x == max(a_x, b_x, c_x, d_x) :
x1 = b_x
y1 = b_y
if c_x == max(a_x, b_x, c_x, d_x) :
x1 = c_x
y1 = c_y
if d_x == max(a_x, b_x, c_x, d_x) :
x1 = d_x
y1 = d_y
# bottom x2 y2
if a_y == max(a_y, b_y, c_y, d_y) :
x2 = a_x
y2 = a_y
if b_y == max(a_y, b_y, c_y, d_y) :
x2 = b_x
y2 = b_y
if c_y == max(a_y, b_y, c_y, d_y) :
x2 = c_x
y2 = c_y
if d_y == max(a_y, b_y, c_y, d_y) :
x2 = d_x
y2 = d_y
# left x3 y3
if a_x == min(a_x, b_x, c_x, d_x) :
x3 = a_x
y3 = a_y
if b_x == min(a_x, b_x, c_x, d_x) :
x3 = b_x
y3 = b_y
if c_x == min(a_x, b_x, c_x, d_x) :
x3 = c_x
y3 = c_y
if d_x == min(a_x, b_x, c_x, d_x) :
x3 = d_x
y3 = d_y
order_points = np.array([(x0,y0),(x1,y1),(x2,y2),(x3,y3)], np.int32)
return order_points
#open result pkl
with open(detection_pkl_path, 'rb') as file:
while True:
try:
data = pickle.load(file) # list[num_epoch/batch_size * list[batch_size * list[num_classes * array(n, [18, 8, score])]][0] ]
except EOFError:
break
num_img = len(data)
#open json
with open(val_json) as f:
ann=json.load(f)
for img_item in ann['images']: # 遍历每张图
#print('img id:', img_item['id'])
index_img = img_item['id'] - 1
img_results = data[index_img]
img_name = img_item['file_name']
img_base_name = img_name.split('.png')[0] # 'P0003__1.0__0___0'
bboxes = img_results # bboxes： list[num_classes * array(n, [18, 8, score])]
num_bboxes = len(bboxes)
for iter_box in range(num_bboxes): # 遍历每个类的检测结果
if len(bboxes[iter_box])>0:
#print(bboxes[iter_box])
if iter_box == 0:
class_name = 'plane'
r_color = (0, 0, 255) #纯红
elif iter_box == 1:
class_name = 'baseball-diamond'
r_color = (0, 255, 0) #纯绿
elif iter_box == 2:
class_name = 'bridge'
r_color = (255, 0, 0) # 纯蓝
elif iter_box == 3:
class_name = 'ground-track-field'
r_color = (169, 169, 169) # 深灰色
elif iter_box == 4:
class_name = 'small-vehicle'
r_color = (0, 0, 139) #深红色
elif iter_box == 5:
class_name = 'large-vehicle'
r_color = (0, 69, 255) #橙红色
elif iter_box == 6:
class_name = 'ship'
r_color = (30, 105, 210)# 巧克力色
elif iter_box == 7:
class_name = 'tennis-court'
r_color = (10,215,255) #金色
elif iter_box == 8:
class_name = 'basketball-court'
r_color = (0,128,128) #橄榄色
elif iter_box == 9:
class_name = 'storage-tank'
r_color = (203,192,255) #粉色
elif iter_box == 10:
class_name = 'soccer-ball-field'
r_color = (230,216,173) #天蓝色
elif iter_box == 11:
class_name = 'roundabout'
r_color = (238,130,238) #紫罗兰
elif iter_box == 12:
class_name = 'harbor'
r_color = (144,238,144) #浅绿
elif iter_box == 13:
class_name = 'swimming-pool'
r_color = (130,0,75) #藏青色
elif iter_box == 14:
class_name = 'helicopter'
r_color = (147,20,255) #深粉色
elif iter_box == 15:
class_name = 'container-crane'
r_color = (255,0,255) #????
elif iter_box == 16:
class_name = 'airport'
r_color = (255,255,0) #
elif iter_box == 17:
class_name = 'helipad'
r_color = (0,255,255) #
else:
class_name = None
print('Unknown class_name!!!!!!!!!!!!!!!!!!!!!!! ')
for bbox in bboxes[iter_box]: # bboxes[iter_box]： array(n, [18, 8, score]) x_first # 遍历每个bbox
pt_x = bbox[0:-9:2] # (start_index:end_index:step) 取points 的x坐标
pt_y = bbox[1:-9:2] # (start_index:end_index:step) 取points 的y坐标
confidence = float(bbox[-1])
#confidence threshold
if confidence > conf_thresh:
if os.path.exists(outpath+img_name):
#print(img_name)
image = cv2.imread(outpath+img_name)
else:
image = cv2.imread(inputpath+img_name)
# visulize the oriented boxes
box_list = []
box_list.append((float(bbox[-9]), float(bbox[-8])))
box_list.append((float(bbox[-7]), float(bbox[-6])))
box_list.append((float(bbox[-5]), float(bbox[-4])))
box_list.append((float(bbox[-3]), float(bbox[-2]))) # box_list.size(4, 2)
box_order = transfer_to_order_point(box_list) # 给四个点排序
cv2.polylines(image, [box_order], True, r_color, thickness=1) # 四个点连线
for i in range(len(pt_x)):
# learning points
cv2.circle(img=image, center=(int(pt_x[i]), int(pt_y[i])), radius=1, color=r_color, thickness=2) # 画点
text_label = '%s %.2f' % (class_name, confidence)
cv2.putText(image, text_label,
(int((box_order[1][0] + box_order[3][0])/2), int((box_order[0][1] + box_order[2][1])/2) - 0),
0, 0.25, r_color, thickness=1, lineType=cv2.LINE_AA)
cv2.imwrite(outpath+img_name, image)