def choose_positive_samples(image_dir, sample_size, bbinfo_dir):
filtered_image_list = utils.list_images_in_directory(image_dir)
# Filter out images without bounding boxes:
global_info = training.loadGlobalInfo(bbinfo_dir)
bbox_filter = lambda img_path: os.path.split(img_path)[1] in global_info
filtered_image_list = filter(bbox_filter, filtered_image_list)
# Extract image regions:
img_regions = []
for img_path in filtered_image_list:
key = os.path.split(img_path)[1]
rects = utils.rectangles_from_cache_string(global_info[key])
for rect in rects:
region = utils.ImageRegion(rect, img_path)
img_regions.append(region)
# Truncate region list to sample the correct number of images:
if sample_size is None:
random.shuffle(img_regions)
else:
img_regions = random.sample(img_regions, min(sample_size, len(img_regions)))
if len(img_regions) < sample_size:
raise ValueError(
"Requested {} positive samples, but only {} could be found in '{}'!".format(
sample_size, len(img_regions), image_dir
)
)
return img_regions
def choose_positive_samples(image_dir, sample_size, bbinfo_dir):
filtered_image_list = utils.list_images_in_directory(image_dir)
# Filter out images without bounding boxes:
global_info = training.loadGlobalInfo(bbinfo_dir)
bbox_filter = lambda img_path: os.path.split(img_path)[1] in global_info
filtered_image_list = filter(bbox_filter, filtered_image_list)
# Extract image regions:
img_regions = []
for img_path in filtered_image_list:
key = os.path.split(img_path)[1]
rects = utils.rectangles_from_cache_string(global_info[key])
for rect in rects:
region = utils.ImageRegion(rect, img_path)
img_regions.append(region)
# Truncate region list to sample the correct number of images:
if sample_size is None:
random.shuffle(img_regions)
else:
img_regions = random.sample(img_regions,
min(sample_size, len(img_regions)))
if len(img_regions) < sample_size:
raise ValueError(
'Requested {} positive samples, but only {} could be found in \'{}\'!'
.format(sample_size, len(img_regions), image_dir))
return img_regions
def view_positive_samples(classifier_yaml, output_dir):
bbinfo_dir = classifier_yaml['dataset']['directory']['bbinfo']
global_info = utils.load_opencv_bounding_box_info_directory(
bbinfo_dir, suffix='bbinfo')
# global_info = loadGlobalInfo(bbinfo_dir)
positive_dir = classifier_yaml['dataset']['directory']['positive']
# for img_path in glob.glob("{}/*_*.jpg".format(positive_dir)):
# for img_path in utils.list_images_in_directory(positive_dir):
pos_samples = sampleTrainingImages(positive_dir, ['.*'],
None,
require_bboxes=True,
bbinfo_dir=bbinfo_dir)
print 'Selected {} positive samples.'.format(len(pos_samples))
for img_path in pos_samples:
img = cv2.imread(img_path)
key = img_path.split('/')[-1]
rects_str = global_info[key]
rects = utils.rectangles_from_cache_string(rects_str)
print img_path, len(rects)
for rect in rects:
lw = max(2, min(img.shape[0], img.shape[1]) / 150)
cvDrawRectangle(img, rect, (255, 0, 0), lw)
# cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,255),lw+2)
# cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),lw)
# aspectRect = gm.extendBoundingBox(rect, 83/64.0)
# cvDrawRectangle(img, aspectRect, (0,255,0),2)
#
# paddedRect = gm.padBoundingBox(aspectRect, (0.1, 0.1))
# cvDrawRectangle(img, paddedRect, (0,0,255),2)
h, w = img.shape[:2]
max_dim = 1200.0
# if w > max_dim + 50 or h > max_dim + 50:
if abs(w - max_dim) > 50 or abs(h - max_dim) > 50:
sx = max_dim / w
sy = max_dim / h
current_scale = min(sx, sy)
img = cv2.resize(img,
dsize=None,
fx=current_scale,
fy=current_scale)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def view_positive_samples(classifier_yaml, output_dir):
bbinfo_dir = classifier_yaml['dataset']['directory']['bbinfo']
global_info = utils.load_opencv_bounding_box_info_directory(bbinfo_dir, suffix='bbinfo')
# global_info = loadGlobalInfo(bbinfo_dir)
positive_dir = classifier_yaml['dataset']['directory']['positive']
# for img_path in glob.glob("{}/*_*.jpg".format(positive_dir)):
# for img_path in utils.list_images_in_directory(positive_dir):
pos_samples = sampleTrainingImages(positive_dir, ['.*'], None, require_bboxes=True, bbinfo_dir=bbinfo_dir)
print 'Selected {} positive samples.'.format(len(pos_samples))
for img_path in pos_samples:
img = cv2.imread(img_path)
key = img_path.split('/')[-1]
rects_str = global_info[key]
rects = utils.rectangles_from_cache_string(rects_str)
print img_path, len(rects)
for rect in rects:
lw = max(2, min(img.shape[0], img.shape[1]) / 150)
cvDrawRectangle(img, rect, (255,0,0), lw)
# cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,255),lw+2)
# cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),lw)
# aspectRect = gm.extendBoundingBox(rect, 83/64.0)
# cvDrawRectangle(img, aspectRect, (0,255,0),2)
#
# paddedRect = gm.padBoundingBox(aspectRect, (0.1, 0.1))
# cvDrawRectangle(img, paddedRect, (0,0,255),2)
h, w = img.shape[:2]
max_dim = 1200.0
# if w > max_dim + 50 or h > max_dim + 50:
if abs(w - max_dim) > 50 or abs(h - max_dim) > 50:
sx = max_dim / w
sy = max_dim / h
current_scale = min(sx, sy)
img = cv2.resize(img, dsize=None, fx=current_scale, fy=current_scale)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
