def save_results(image_path, anno):
"""Saves results of the prediction.
Args:
image_path (string): The path to source image to predict bounding boxes.
anno (Annotation): The predicted annotations for source image.
Returns:
Nothing.
"""
# draw
new_img = Image.open(image_path)
d = ImageDraw.Draw(new_img)
rects = anno['rects'] if type(anno) is dict else anno.rects
for r in rects:
d.rectangle([r.left(), r.top(),
r.right(), r.bottom()],
outline=(255, 0, 0))
# save
fpath = os.path.join(os.path.dirname(image_path), 'result.png')
new_img.save(fpath)
subprocess.call(['chmod', '777', fpath])
fpath = os.path.join(os.path.dirname(image_path), 'result.json')
if type(anno) is dict:
with open(fpath, 'w') as f:
json.dump(anno, f)
else:
al.saveJSON(fpath, anno)
subprocess.call(['chmod', '777', fpath])
def save_results(image_path, anno, fname='result'):
"""Saves results of the prediction.
Args:
image_path (string): The path to source image to predict bounding boxes.
anno (Annotation, list): The predicted annotations for source image or the list of bounding boxes.
Returns:
Nothing.
"""
# draw
new_img = Image.open(image_path)
d = ImageDraw.Draw(new_img)
is_list = type(anno) is list
rects = anno if is_list else anno.rects
for r in rects:
if is_list:
d.rectangle([r['x1'], r['y1'], r['x2'], r['y2']], outline=(255, 0, 0))
else:
d.rectangle([r.left(), r.top(), r.right(), r.bottom()], outline=(255, 0, 0))
# save
prediction_image_path = os.path.join(os.path.dirname(image_path), fname + '.png')
new_img.save(prediction_image_path)
subprocess.call(['chmod', '644', prediction_image_path])
fpath = os.path.join(os.path.dirname(image_path), fname + '.json')
if is_list:
json.dump({'image_path': prediction_image_path, 'rects': anno}, open(fpath, 'w'))
else:
al.saveJSON(fpath, anno)
subprocess.call(['chmod', '644', fpath])
def save_results(image_path, anno):
"""Saves results of the prediction.
Args:
image_path (string): The path to source image to predict bounding boxes.
anno (Annotation): The predicted annotations for source image.
Returns:
Nothing.
"""
# draw
new_img = Image.open(image_path)
d = ImageDraw.Draw(new_img)
for r in anno.rects:
d.rectangle([r.left(), r.top(), r.right(), r.bottom()], outline=(0, 255, 0))
detections_count=len(anno.rects)
if detections_count>0:
print("Number of target detections:", detections_count)
else:
print("Target hasn't been detected.")
# save
output_path=os.path.dirname(image_path)+os.path.sep+'output'
if not os.path.exists(output_path):
os.makedirs(output_path)
fpath = os.path.join(output_path, os.path.basename(image_path)+'_result.png')
new_img.save(fpath)
subprocess.call(['chmod', '777', fpath])
fpath = os.path.join(output_path, os.path.basename(image_path)+'_result.json')
al.saveJSON(fpath, anno)
subprocess.call(['chmod', '777', fpath])
def save_results_but_not_image(image_path, anno):
"""Saves results of the prediction.
Args:
image_path (string): The path to source image to predict bounding boxes.
anno (Annotation): The predicted annotations for source image.
Returns:
Nothing.
"""
fpath = os.path.join(os.path.dirname(image_path), 'result.json')
if type(anno) is dict:
with open(fpath, 'w') as f:
json.dump(anno, f)
else:
al.saveJSON(fpath, anno)
subprocess.call(['chmod', '777', fpath])
def save_results(image_path, anno):
"""Saves results of the prediction.
Args:
image_path (string): The path to source image to predict bounding boxes.
anno (Annotation): The predicted annotations for source image.
Returns:
Nothing.
"""
import cv2 as cv
# draw - old pil
# new_img = Image.open(image_path)
# d = ImageDraw.Draw(new_img)
# draw - new opencv
new_img = imread(image_path)
# open diameters file for writing
diamfile = open(
'/media/chris/data1/cda/cda/tensorbox/data/cda/ctx_output/' +
'result-' + os.path.basename(image_path) + '.txt', 'w')
rects = anno['rects'] if type(anno) is dict else anno.rects
for r in rects:
if r.score > 0.8:
# draw circles
centre = (int(round(r.left() + (r.right() - r.left()) / 2)),
int(round(r.bottom() + (r.top() - r.bottom()) / 2)))
print centre
print 'r.left ', r.left()
print 'r.right ', r.right()
print 'r.top ', r.top()
print 'r.bottom ', r.bottom()
# radius is the average of box height and width, halved
radius = int(
round(((r.right() - r.left()) / 2 +
(r.bottom() - r.top()) / 2) / 2))
print 'radius = ', radius
cv.circle(new_img, centre, radius, (0, 0, 255), -2, 8, 0)
cv.line(new_img, (centre[0] - 3, centre[1]),
(centre[0] + 3, centre[1]), (0, 0, 255), 2, 8, 0)
cv.line(new_img, (centre[0], centre[1] - 3),
(centre[0], centre[1] + 3), (0, 0, 255), 2, 8, 0)
# find diameter in m
diam = (((r.right() - r.left()) / 2 +
(r.bottom() - r.top()) / 2) / 2) * SCALE
# write diameter to file
diamfile.write(str(diam) + '\n')
# draw thick rectangles
# cor = (r.left(), r.top(), r.right(), r.bottom())
# width = 3
# for i in range(width):
# d.rectangle(cor, outline="red")
# cor = (cor[0]+1,cor[1]+1, cor[2]+1,cor[3]+1)
#d.rectangle([r.left(), r.top(), r.right(), r.bottom()], outline=(255, 0, 0), 2, 8, 0)
# save
#fpath = os.path.join(os.path.dirname(image_path), 'result-' + os.path.basename(image_path) + '.png')
fpath = os.path.join(
'/media/chris/data1/cda/cda/tensorbox/data/cda/ctx_output/',
'result-' + os.path.basename(image_path) + '.png')
# new_img.save(fpath)
cv.imwrite(fpath, new_img)
subprocess.call(['chmod', '777', fpath])
#fpath = os.path.join(os.path.dirname(image_path), 'result-' + os.path.basename(image_path) + '.json')
fpath = os.path.join(
'/media/chris/data1/cda/cda/tensorbox/data/cda/ctx_output/',
'result-' + os.path.basename(image_path) + '.json')
if type(anno) is dict:
with open(fpath, 'w') as f:
json.dump(anno, f)
else:
al.saveJSON(fpath, anno)
subprocess.call(['chmod', '777', fpath])
