def training_data_generator(coco, start_index, num_images, bounding_box_count,
cell_width_px, cell_height_px, batch_size):
img_ids = get_downloaded_ids()
img_ids = list(filter(is_not_greyscale, img_ids))[:num_images]
while 1:
for img_id in img_ids:
image_batch = np.empty((batch_size, 640, 640, 3),
np.float) #hard code
y_true_batch = np.empty((batch_size, 10, 10, 5),
np.float) #hard code
for i in range(batch_size):
image = file_management.get_image(img_id)
image = np.divide(image, 256, dtype=np.float32)
try:
image = pad_image(image, PADDED_SIZE)
except Exception as e:
print(id)
raise e
ground_truth = get_y_true(coco, bounding_box_count,
cell_width_px, cell_height_px,
img_id)
image_batch[i, :, :, :] = image
y_true_batch[i, :, :, :] = ground_truth
yield (image_batch, y_true_batch)
def all_imgs_numpy(num_images):
img_ids = get_downloaded_ids()
img_ids = list(filter(is_not_greyscale, img_ids))[:num_images]
imgs = np.empty((len(img_ids), 640, 640, 3), np.float)
gen = image_generator(img_ids)
for index in range(len(img_ids)):
imgs[index, :, :, :] = next(gen)
return imgs
def all_ground_truth_numpy(coco, num_images, bounding_box_count, cell_width_px,
cell_height_px):
#assert(cell_rows == ceil(PADDED_SIZE/cell_height_px))
#assert(cell_columns == ceil(PADDED_SIZE/cell_width_px))
img_ids = get_downloaded_ids()
img_ids = list(filter(is_not_greyscale, img_ids))[:num_images]
output = np.empty(
(len(img_ids), ceil(PADDED_SIZE / cell_height_px),
ceil(PADDED_SIZE / cell_width_px), bounding_box_count * 5), np.float)
gen = y_true_generator(coco, bounding_box_count, cell_width_px,
cell_height_px, img_ids)
for index in range(len(img_ids)):
output[index, :, :, :] = next(gen)
return output
def test_single_abs_ann_to_rect_mask():
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from file_management import ANNOTATION_FILE, get_downloaded_ids
from annotations import get_image_annotations, plot_annotations
coco = COCO(ANNOTATION_FILE)
img_ids = get_downloaded_ids()
anns = [get_image_annotations(coco, img) for img in img_ids]
#view_img = lambda index: plot_annotations(img_ids[index], anns[index])
ann = anns[0][0]
plt.figure(2)
fig, ax = plt.subplots()
ax.imshow(single_abs_ann_to_rect_mask(640, 420, ann['bbox']))
plt.figure(1)
plot_annotations(img_ids[0], [ann])
def test_abs_anns_to_heatmap():
import matplotlib.pyplot as plt
from matplotlib import cm
from pycocotools.coco import COCO
from file_management import ANNOTATION_FILE, get_downloaded_ids
from annotations import get_image_annotations, plot_annotations
coco = COCO(ANNOTATION_FILE)
img_ids = get_downloaded_ids()
anns = [get_image_annotations(coco, img) for img in img_ids]
plt.figure(0)
ann = anns[0]
heatmap = abs_anns_to_heatmap(640, 640, ann)
plt.figure(2)
fig, ax = plt.subplots()
ax.imshow(heatmap) #, cmap=cm.jet)
plt.figure(1)
plot_annotations(img_ids[0], ann)
action='store_true',
help=
'If this flag is passed, run on ALL images downloaded. Note that you still must pass an image id, but it does nothing.'
)
args = vars(ap.parse_args())
# load the trained convolutional neural network
print("[INFO] loading network...")
# Load in the custom loss function
get_custom_objects().update({"QueueTime_loss": QueueTime_loss})
model = load_model(args["model"])
img_ids = args["image_ids"]
if args['all']:
img_ids = filter(is_not_greyscale, get_downloaded_ids())
for img_id in img_ids:
image = pad_image(get_image(img_id), PADDED_SIZE)
image = np.expand_dims(image, axis=0)
# classify the input image
print("[INFO] classifying image...")
y_pred = model.predict(image)[0]
# post_pred = QueueTime_post_process(y_pred)
post_pred = cnn_y_to_absolute(CELL_WIDTH, CELL_HEIGHT, y_pred)
# filter out all scores below threshold:
post_pred_filtered = list(
filter(lambda ann: ann['score'] > 0.001, post_pred))
#!/usr/bin/env python3 -i from pycocotools.coco import COCO from file_management import ANNOTATION_FILE, get_downloaded_ids from annotations import get_image_annotations, plot_annotations coco = COCO(ANNOTATION_FILE) img_ids = get_downloaded_ids() anns = [get_image_annotations(coco, img) for img in img_ids] view_img = lambda index: plot_annotations(img_ids[index], anns[index])