def load_image(self, image_index):
""" Load an image at the image_index.
"""
path = os.path.join(
self.data_dir, 'JPEGImages',
self.image_names[image_index] + self.image_extension)
return read_image_bgr(path)
def load_image(self, image_index):
""" Load an image at the image_index.
"""
image_info = self.coco.loadImgs(self.image_ids[image_index])[0]
path = os.path.join(self.data_dir, 'images', self.set_name, image_info['file_name'])
return read_image_bgr(path)
exist_ok=True)
shutil.copy(
os.path.join(anns_path, i[:-4] + '.xml'),
f'/home/palm/PycharmProjects/seven2/xmls/readjusted/{set_name}/'
+ i[:-4] + '.xml')
continue
if '<name>obj</name>' not in x:
os.makedirs(
f'/home/palm/PycharmProjects/seven2/xmls/readjusted/{set_name}/',
exist_ok=True)
shutil.copy(
os.path.join(anns_path, i[:-4] + '.xml'),
f'/home/palm/PycharmProjects/seven2/xmls/readjusted/{set_name}/'
+ i[:-4] + '.xml')
continue
image = read_image_bgr(os.path.join(folder, i))
start_time = time.time()
# copy to draw ong
draw = image.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# preprocess image for network
image = preprocess_image(image)
image, scale = resize_image(image, min_side=720, max_side=1280)
# process image
boxes, scores, labels = prediction_model.predict_on_batch(
np.expand_dims(image, axis=0))
# correct for image scale
boxes /= scale
def load_image(self, image_index):
""" Load an image at the image_index.
"""
return read_image_bgr(self.images[image_index])
import time
from boxutils import add_bbox
if __name__ == '__main__':
labels_to_names = [x.split(',')[0] for x in open('/home/palm/PycharmProjects/seven2/anns/c.csv').read().split('\n')[:-1]]
model_path = '/home/palm/PycharmProjects/seven2/snapshots/infer_model_5.h5'
model = models.load_model(model_path)
dst = '/home/palm/PycharmProjects/seven/predict/1'
path = '/home/palm/PycharmProjects/seven/data1/1'
pad = 0
for image_name in os.listdir(path):
p = os.path.join(path, image_name)
image = read_image_bgr(p)
# copy to draw on
draw = image.copy()
# preprocess image for network
image = preprocess_image(image)
image, scale = resize_image(image, min_side=720, max_side=1280)
# process image
start = time.time()
boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))
print("processing time: ", time.time() - start)
# correct for image scale
boxes /= scale
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
keras.backend.set_session(sess)
if __name__ == '__main__':
prediction_model = models.load_model('snapshots/infer_model_test.h5')
root = '/media/palm/BiggerData/mine/new/i/'
# srcs = [
# 'PU_23550891_00_20200905_214516_BKQ02-003',
# 'PU_23550891_00_20200905_230000_BKQ02',
# ]
srcs = os.listdir(root)
for p in srcs:
src = os.path.join(root, p)
os.makedirs(f'/media/palm/BiggerData/mine/out/i/{p}', exist_ok=True)
for f in os.listdir(src):
frame = read_image_bgr(os.path.join(src, f))
im = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = np.dstack((im, im, im))
frame, scale = resize_image(frame, min_side=720, max_side=1280)
image = preprocess_image(frame)
boxes, scores, labels = prediction_model.predict_on_batch(
np.expand_dims(image, axis=0))
for box, score, label in zip(boxes[0], scores[0], labels[0]):
# scores are sorted so we can break
if score < 0.5:
break
b = box.astype(int)
frame = add_bbox(frame, b, label, [
'crane',
def load_image(self, image_index):
return read_image_bgr(self.image_path(image_index))
names_to_labels = {}
for x in open('/home/palm/PycharmProjects/seven2/anns/classes.csv').read(
).split('\n')[:-1]:
names_to_labels[x.split(',')[0]] = int(x.split(',')[1])
query_path = '/home/palm/PycharmProjects/seven/images/cropped7/train'
cache_path = '/home/palm/PycharmProjects/seven/caches'
cache_dict = {}
all_detections = []
all_annotations = []
known_classes = os.listdir(query_path)
for instance in valid_ints:
all_annotation = all_annotation_from_instance(instance,
names_to_labels)
image = read_image_bgr(instance["filename"])
t = time.time()
# copy to draw ong
draw = image.copy()
# preprocess image for network
image = preprocess_image(image)
image, scale = resize_image(image, min_side=720, max_side=1280)
# process image
boxes, scores, labels = prediction_model.predict_on_batch(
np.expand_dims(image, axis=0))
# correct for image scale
