def compute_rectangels(H,
confidences,
boxes,
use_stitching=False,
rnn_len=1,
min_conf=0.1,
show_removed=True,
tau=0.25):
num_cells = H["grid_height"] * H["grid_width"]
boxes_r = np.reshape(boxes,
(-1, H["grid_height"], H["grid_width"], rnn_len, 4))
confidences_r = np.reshape(
confidences,
(-1, H["grid_height"], H["grid_width"], rnn_len, H['num_classes']))
cell_pix_size = H['region_size']
all_rects = [[[] for _ in range(H["grid_width"])]
for _ in range(H["grid_height"])]
for n in range(rnn_len):
for y in range(H["grid_height"]):
for x in range(H["grid_width"]):
bbox = boxes_r[0, y, x, n, :]
abs_cx = int(bbox[0]) + cell_pix_size / 2 + cell_pix_size * x
abs_cy = int(bbox[1]) + cell_pix_size / 2 + cell_pix_size * y
w = bbox[2]
h = bbox[3]
conf = np.max(confidences_r[0, y, x, n, 1:])
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
all_rects_r = [r for row in all_rects for cell in row for r in cell]
if use_stitching:
from utils.stitch_wrapper import stitch_rects
acc_rects = stitch_rects(all_rects, tau)
else:
acc_rects = all_rects_r
def add_rectangles(H, orig_image, confidences, boxes, use_stitching=False, rnn_len=1, min_conf=0.1, show_removed=True, tau=0.25, show_suppressed=True):
image = np.copy(orig_image[0])
num_cells = H["grid_height"] * H["grid_width"]
boxes_r = np.reshape(boxes, (-1,
H["grid_height"],
H["grid_width"],
rnn_len,
4))
confidences_r = np.reshape(confidences, (-1,
H["grid_height"],
H["grid_width"],
rnn_len,
H['num_classes']))
cell_pix_size = H['region_size']
all_rects = [[[] for _ in range(H["grid_width"])] for _ in range(H["grid_height"])]
for n in range(rnn_len):
for y in range(H["grid_height"]):
for x in range(H["grid_width"]):
bbox = boxes_r[0, y, x, n, :]
abs_cx = int(bbox[0]) + cell_pix_size/2 + cell_pix_size * x
abs_cy = int(bbox[1]) + cell_pix_size/2 + cell_pix_size * y
w = bbox[2]
h = bbox[3]
conf = np.max(confidences_r[0, y, x, n, 1:])
all_rects[y][x].append(Rect(abs_cx,abs_cy,w,h,conf))
all_rects_r = [r for row in all_rects for cell in row for r in cell]
if use_stitching:
from utils.stitch_wrapper import stitch_rects
acc_rects = stitch_rects(all_rects, tau)
else:
acc_rects = all_rects_r
if show_suppressed:
pairs = [(all_rects_r, (255, 0, 0))]
else:
pairs = []
pairs.append((acc_rects, (0, 255, 0)))
for rect_set, color in pairs:
for rect in rect_set:
if rect.confidence > min_conf:
cv2.rectangle(image,
(int(rect.cx)-int(rect.width/2), int(rect.cy)-int(rect.height/2)),
(int(rect.cx)+int(rect.width/2), int(rect.cy)+int(rect.height/2)),
color,
2)
rects = []
for rect in acc_rects:
r = al.AnnoRect()
r.x1 = rect.cx - rect.width/2.
r.x2 = rect.cx + rect.width/2.
r.y1 = rect.cy - rect.height/2.
r.y2 = rect.cy + rect.height/2.
r.score = rect.true_confidence
rects.append(r)
return image, rects
def add_rectangles(H,
orig_image,
confidences,
boxes,
arch,
use_stitching=False,
rnn_len=1,
min_conf=0.5,
tau=0.25):
from utils.rect import Rect
from utils.stitch_wrapper import stitch_rects
import numpy as np
image = np.copy(orig_image[0])
boxes_r = np.reshape(
boxes, (-1, arch["grid_height"], arch["grid_width"], rnn_len, 4))
confidences_r = np.reshape(
confidences, (-1, arch["grid_height"], arch["grid_width"], rnn_len, 2))
cell_pix_size = H['arch']['region_size']
all_rects = [[[] for _ in range(arch["grid_width"])]
for _ in range(arch["grid_height"])]
for n in range(0, H['arch']['rnn_len']):
for y in range(arch["grid_height"]):
for x in range(arch["grid_width"]):
bbox = boxes_r[0, y, x, n, :]
conf = confidences_r[0, y, x, n, 1]
abs_cx = int(bbox[0]) + cell_pix_size / 2 + cell_pix_size * x
abs_cy = int(bbox[1]) + cell_pix_size / 2 + cell_pix_size * y
h = max(1, bbox[3])
w = max(1, bbox[2])
#w = h * 0.4
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
if use_stitching:
acc_rects = stitch_rects(all_rects, tau)
else:
acc_rects = [
r for row in all_rects for cell in row for r in cell
if r.confidence > 0.1
]
for rect in acc_rects:
if rect.confidence > min_conf:
cv2.rectangle(image, (rect.cx - int(rect.width / 2),
rect.cy - int(rect.height / 2)),
(rect.cx + int(rect.width / 2),
rect.cy + int(rect.height / 2)), (0, 255, 0), 2)
rects = []
for rect in acc_rects:
r = al.AnnoRect()
r.x1 = rect.cx - rect.width / 2.
r.x2 = rect.cx + rect.width / 2.
r.y1 = rect.cy - rect.height / 2.
r.y2 = rect.cy + rect.height / 2.
r.score = rect.true_confidence
rects.append(r)
return image, rects
def add_rectangles(H, orig_image, confidences, boxes, arch, use_stitching=False, rnn_len=1, min_conf=0.5, tau=0.25):
from utils.rect import Rect
from utils.stitch_wrapper import stitch_rects
import numpy as np
image = np.copy(orig_image[0])
boxes_r = np.reshape(boxes, (-1,
arch["grid_height"],
arch["grid_width"],
rnn_len,
4))
confidences_r = np.reshape(confidences, (-1,
arch["grid_height"],
arch["grid_width"],
rnn_len,
2))
cell_pix_size = H['arch']['region_size']
all_rects = [[[] for _ in range(arch["grid_width"])] for _ in range(arch["grid_height"])]
for n in range(0, H['arch']['rnn_len']):
for y in range(arch["grid_height"]):
for x in range(arch["grid_width"]):
bbox = boxes_r[0, y, x, n, :]
conf = confidences_r[0, y, x, n, 1]
abs_cx = int(bbox[0]) + cell_pix_size/2 + cell_pix_size * x
abs_cy = int(bbox[1]) + cell_pix_size/2 + cell_pix_size * y
h = max(1, bbox[3])
w = max(1, bbox[2])
#w = h * 0.4
all_rects[y][x].append(Rect(abs_cx,abs_cy,w,h,conf))
if use_stitching:
acc_rects = stitch_rects(all_rects, tau)
else:
acc_rects = [r for row in all_rects for cell in row for r in cell if r.confidence > 0.1]
for rect in acc_rects:
if rect.confidence > min_conf:
cv2.rectangle(image,
(rect.cx-int(rect.width/2), rect.cy-int(rect.height/2)),
(rect.cx+int(rect.width/2), rect.cy+int(rect.height/2)),
(0,255,0),
2)
rects = []
for rect in acc_rects:
r = al.AnnoRect()
r.x1 = rect.cx - rect.width/2.
r.x2 = rect.cx + rect.width/2.
r.y1 = rect.cy - rect.height/2.
r.y2 = rect.cy + rect.height/2.
r.score = rect.true_confidence
rects.append(r)
return image, rects
def _assign_rects(self, H, confidences, boxes, use_stitching=False,
rnn_len=1, min_conf=0.1, tau=0.25, min_area=100):
boxes_r = np.reshape(boxes, (-1,
H["grid_height"],
H["grid_width"],
rnn_len,
4))
confidences_r = np.reshape(confidences, (-1,
H["grid_height"],
H["grid_width"],
rnn_len,
H['num_classes']))
cell_pix_size = H['region_size']
all_rects = [[[] for _ in range(H["grid_width"])]
for _ in range(H["grid_height"])]
for n in range(rnn_len):
for y in range(H["grid_height"]):
for x in range(H["grid_width"]):
bbox = boxes_r[0, y, x, n, :]
abs_cx = int(bbox[0]) + cell_pix_size/2 + cell_pix_size * x
abs_cy = int(bbox[1]) + cell_pix_size/2 + cell_pix_size * y
w = bbox[2]
h = bbox[3]
conf = np.max(confidences_r[0, y, x, n, 1:])
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
all_rects_r = [r for row in all_rects for cell in row for r in cell]
if use_stitching:
from utils.stitch_wrapper import stitch_rects
acc_rects = stitch_rects(all_rects, tau)
else:
acc_rects = all_rects_r
bounding_boxes = [r for r in acc_rects
if r.score > min_conf and r.area > min_area]
return bounding_boxes
def add_rectangles(H,
orig_image,
confidences,
boxes,
use_stitching=False,
rnn_len=1,
min_conf=0.1,
show_removed=True,
tau=0.25,
color_removed=(0, 0, 255),
color_acc=(0, 0, 255)):
image = np.copy(orig_image[0])
num_cells = H["grid_height"] * H["grid_width"]
boxes_r = np.reshape(boxes,
(-1, H["grid_height"], H["grid_width"], rnn_len, 4))
confidences_r = np.reshape(
confidences,
(-1, H["grid_height"], H["grid_width"], rnn_len, H['num_classes']))
cell_pix_size = H['region_size']
all_rects = [[[] for _ in range(H["grid_width"])]
for _ in range(H["grid_height"])]
for n in range(rnn_len):
for y in range(H["grid_height"]):
for x in range(H["grid_width"]):
bbox = boxes_r[0, y, x, n, :]
abs_cx = int(bbox[0]) + cell_pix_size / 2 + cell_pix_size * x
abs_cy = int(bbox[1]) + cell_pix_size / 2 + cell_pix_size * y
w = bbox[2]
h = bbox[3]
conf = np.max(confidences_r[0, y, x, n, 1:])
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
all_rects_r = [r for row in all_rects for cell in row for r in cell]
if use_stitching:
from utils.stitch_wrapper import stitch_rects
acc_rects = stitch_rects(all_rects, tau)
else:
acc_rects = all_rects_r
if not show_removed:
all_rects_r = []
pairs = [(all_rects_r, color_removed), (acc_rects, color_acc)]
im = Image.fromarray(image.astype('uint8'))
draw = ImageDraw.Draw(im)
for rect_set, color in pairs:
for rect in rect_set:
if rect.confidence > min_conf:
_draw_rect(draw, rect, color)
image = np.array(im).astype('float32')
rects = []
for rect in acc_rects:
r = al.AnnoRect()
r.x1 = rect.cx - rect.width / 2.
r.x2 = rect.cx + rect.width / 2.
r.y1 = rect.cy - rect.height / 2.
r.y2 = rect.cy + rect.height / 2.
r.score = rect.true_confidence
rects.append(r)
return image, rects