def show_data(file_name, error_list):
with open(file_name) as f:
lines = f.readlines()
shuffle(lines)
for line in lines:
line = line.strip().split('\t')
if int(line[0]) not in error_list:
if int(line[0]) > (5466 + 4698):
print(line[0])
poly = list(map(int, line[2].split(',')))
bbox = [
min(poly[::2]),
min(poly[1::2]),
max(poly[::2]),
max(poly[1::2])
]
image = cv_load_image(line[1])
image = cv2.putText(image, line[-1], (bbox[0], bbox[1] + 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255),
2)
image = cv2.rectangle(image, (bbox[0], bbox[1]),
(bbox[2], bbox[3]), (0, 0, 255), 2)
cv2.imshow('image', image)
cv2.waitKey()
key = cv2.waitKey()
if key & 0xFF == ord('q'):
break
def imloader(path, loader='OPNENCV'):
assert loader in ('OPENCV', 'PIL'), 'Only support OPENCV, PIL for now.'
if loader == 'OPENCV':
return cv_load_image(path)
else:
if os.path.exists(path):
return Image.open(path).convert('RGB')
else:
imfile = requests.get(path)
return Image.open(BytesIO(imfile.content)).convert('RGB')
def show_gt():
file1 = "/core/data/dataset/biaozhu/yf_filtr/air_test.json"
with open(file1, 'r', encoding='utf8')as fp:
json_data = json.load(fp)
count = 0
cv2.namedWindow("enhanced", 0)
cv2.resizeWindow("enhanced", 1920, 1080)
for image in json_data['images']:
url = image['file_name']
img = cv_load_image(url)
count += 1
print(count)
imgStack = stackImages(0.5, ([img, ],))
cv2.imshow("enhanced", img)
cv2.waitKey(0)
def worker(self, data):
imp = data[0]
flipped = data[2]
pixel_means = self.cfg.network.PIXEL_MEANS
#im = cv2.imread(imp, cv2.IMREAD_COLOR)
im = cv_load_image(imp)
# Flip the image
if flipped:
im = im[:, ::-1, :]
# Crop if required
if self.crop_size:
crop = data[1]
max_size = [self.crop_size, self.crop_size]
im = im[int(crop[0][1]):int(crop[0][3]), int(crop[0][0]):int(crop[0][2]), :]
scale = crop[1]
else:
max_size = data[1]
# Compute scale based on config
min_target_size = self.target_size[0]
max_target_size = self.target_size[1]
im_size_min = np.min(im.shape[0:2])
im_size_max = np.max(im.shape[0:2])
scale = float(min_target_size) / float(im_size_min)
if np.round(scale * im_size_max) > max_target_size:
scale = float(max_target_size) / float(im_size_max)
# Resize the image
try:
im = cv2.resize(im, None, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
except:
print 'Image Resize Failed!'
rim = np.zeros((3, max_size[0], max_size[1]), dtype=np.float32)
d1m = min(im.shape[0], max_size[0])
d2m = min(im.shape[1], max_size[1])
for j in range(3):
rim[j, :d1m, :d2m] = im[:d1m, :d2m, 2 - j] - pixel_means[2 - j]
if self.crop_size:
return mx.nd.array(rim, dtype='float32')
else:
return mx.nd.array(rim, dtype='float32'), scale, (im.shape[0],im.shape[1])
def vis_prop(fn, boxes, scores, labels):
def add_object_on_image(image, box, score, posi=True):
b = list(map(int, box))
text = '{:.4f}'.format(score)
c = (255, 0, 0) if posi else (0, 0, 255)
cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), c, thickness=1)
font = cv2.FONT_HERSHEY_SIMPLEX
tag_font_scale = 0.3
((txt_w, txt_h), _) = cv2.getTextSize(text, font, tag_font_scale, 1)
txt_tl = b[0], b[1] + int(1. * txt_h)
cv2.putText(image, text, txt_tl, font, tag_font_scale, c, lineType=cv2.LINE_AA)
return image
image = cv_load_image(fn)
for b, s, l in zip(boxes, scores, labels):
image = add_object_on_image(image, b, s, l > 0)
cv2.imshow('prop', image)
cv2.waitKey()
#
# # mor = cv2.erode(binary, kernel=np.ones((3, 3), np.uint8), iterations=7)
# # mor = cv2.dilate(mor, kernel=np.ones((3, 3), np.uint8), iterations=7)
# # imgCanny = cv2.Canny(binary, 100, 100)
# # imgBlank = np.zeros_like(img)
#
# getContours(binary, imgContour)
# # getContours(mor, imgContour1)
#
# imgStack = stackImages(0.5, ([imgContour, ],))
# # new_filename = os.path.join(root, 'new_' + name)
# # cv2.imwrite(new_filename, imgStack)
#
# cv2.imshow('test', imgStack)
# cv2.waitKey(0)
img0 = cv_load_image('http://192.168.7.29:8081/334,0b3b1f438b259f1b')
orig_img0 = img0.copy()
a = torch.load('/data4t/data/zhangyufei/data/pk0.pth', map_location='cpu')
print('torch load: ', a)
b = a[0].bbox.numpy().tolist()
print('num of proposal: ', len(b))
for c in b:
cv2.rectangle(img0, (int(c[0]), int(c[1])), (int(c[2]), int(c[3])), (0, 0, 255), 1)
img2 = cv_load_image('http://192.168.7.13:8081/82,0b3b1fbc39e81393')
orig_img2 = img2.copy()
a = torch.load('/data4t/data/zhangyufei/data/pk2.pth', map_location='cpu')
print('torch load: ', a)
b = a[0].bbox.numpy().tolist()
print('num of proposal: ', len(b))
for c in b:
return w, 0, img
vertical_group = [x for x in consecutive(vertical) if (len(x) > 20)]
if len(vertical_group) == 0:
return w, 0, img
elif len(vertical_group) == 1:
if vertical_group[0][0] == 0:
r, l = (w, vertical_group[0][-1])
elif vertical_group[0][-1] == w - 1:
r, l = (vertical_group[0][0], 0)
else:
r, l = w, 0
elif len(vertical_group) >= 2:
if vertical_group[-1][-1] == w - 1 and vertical_group[0][0] == 0:
r, l = (min(vertical_group[-1]), max(vertical_group[0]))
else:
r, l = w, 0
else:
r, l = w, 0
if r - l + 1 <= 1000:
return w, 0, img
return r, l, img[:, l:r, :]
if __name__ == "__main__":
import sys
from cvtools import cv_load_image
img = cv_load_image(sys.argv[1])
print(clamp(img))
for image in json_data['images']:
url = image['file_name']
img = cv_load_image(url)
count += 1
print(count)
imgStack = stackImages(0.5, ([img, ],))
cv2.imshow("enhanced", img)
cv2.waitKey(0)
file1 = "/core/data/dataset/biaozhu/yf_wuxi/val_wuxi.json"
with open(file1, 'r', encoding='utf8')as fp:
json_data = json.load(fp)
pic_dir_root = "/core/data/dataset/biaozhu/yf_box"
if not os.path.exists(pic_dir_root):
os.makedirs(pic_dir_root)
count = 0
for image in json_data['images']:
pic_file_name = os.path.join(pic_dir_root, str(image['id']) + '.jpg')
print(pic_file_name)
url = image['file_name']
img = cv_load_image(url)
# cv2.imwrite(pic_file_name, img)
count += 1
print(count)