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debug_utils.py
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debug_utils.py
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import tensorflow as tf
import numpy as np
import cv2
FLAGS = tf.app.flags.FLAGS
coco_color_map = [
0x00, 0x55, 0xFF,
0xFF, 0x11, 0x00,
0xFF, 0x1A, 0x00,
0xFF, 0x22, 0x00,
0xFF, 0x2A, 0x00,
0xFF, 0x33, 0x00,
0xFF, 0x3C, 0x00,
0xFF, 0x44, 0x00,
0xFF, 0x4C, 0x00, #// 29
0xFF, 0x55, 0x00, #// 30
0xFF, 0x5E, 0x00, #// 31
0xFF, 0x66, 0x00, #// 32
0xFF, 0x6E, 0x00, #// 33
0xFF, 0x77, 0x00, #// 34
0xFF, 0x77, 0x00, #// 34
0xFF, 0x80, 0x00, #// 35
0xFF, 0x88, 0x00, #// 36
0xFF, 0x90, 0x00, #// 37
0xFF, 0x99, 0x00, #// 38
0xFF, 0xA2, 0x00, #// 39
0xFF, 0xAA, 0x00, #// 40
0xF6, 0xB2, 0x00, #// 41
0xEE, 0xBB, 0x00, #// 42
0xE6, 0xC4, 0x00, #// 43
0xDD, 0xCC, 0x00, #// 44
0xD4, 0xD4, 0x00, #// 45
0xCC, 0xDD, 0x00, #// 46
0xC4, 0xE6, 0x00, #// 47
0xBB, 0xEE, 0x00, #// 48
0xB2, 0xF6, 0x00, #// 49
0xAA, 0xFF, 0x00, #// 50
0xA2, 0xFF, 0x00, #// 51
0x99, 0xFF, 0x00, #// 52
0x90, 0xFF, 0x00, #// 53
0x88, 0xFF, 0x00, #// 54
0x80, 0xFF, 0x00, #// 55
0x77, 0xFF, 0x00, #// 56
0x6E, 0xFF, 0x00, #// 57
0x66, 0xFF, 0x00, #// 58
0x5E, 0xFF, 0x00, #// 59
0x55, 0xFF, 0x00, #// 61
0x44, 0xFF, 0x11, #// 62
0x3C, 0xFF, 0x1A, #// 63
0x33, 0xFF, 0x22, #// 64
0x2A, 0xFF, 0x2A, #// 65
0x22, 0xFF, 0x33, #// 66
0x1A, 0xFF, 0x3C, #// 67
0x11, 0xFF, 0x44, #// 68
0x08, 0xFF, 0x4C, #// 69
0x00, 0xFF, 0x55, #// 70
0x00, 0xFF, 0x5E, #// 71
0x00, 0xFF, 0x66, #// 72
0x00, 0xFF, 0x6E, #// 73
0x00, 0xFF, 0x77, #// 74
0x00, 0xFF, 0x80, #// 75
0x00, 0xFF, 0x88, #// 76
0x00, 0xFF, 0x90, #// 77
0x00, 0xFF, 0x99, #// 78
0x00, 0xFF, 0xA2, #// 79
0x00, 0xFF, 0xAA, #// 80
0x00, 0xF6, 0xB2,
0x00, 0xEE, 0xBB,
0x00, 0xE6, 0xC4,
0x00, 0xDD, 0xCC,
0x00, 0xD4, 0xD4,
0x00, 0xCC, 0xDD,
0x00, 0xC4, 0xE6,
0x00, 0xBB, 0xEE,
0x00, 0xB2, 0xF6,
0x00, 0xAA, 0xFF,
0x00, 0xA2, 0xFF,
0x00, 0x99, 0xFF,
0x00, 0x90, 0xFF,
0x00, 0x88, 0xFF,
0x00, 0x80, 0xFF,
0x00, 0x77, 0xFF,
0x00, 0x6E, 0xFF,
0x00, 0x66, 0xFF,
0x00, 0x5E, 0xFF,
0xFF, 0x08, 0x00,
0x00, 0xA2, 0xFF,
0x00, 0x99, 0xFF,
0x00, 0x90, 0xFF,
0x00, 0x88, 0xFF,
0x00, 0x80, 0xFF,
0x00, 0x77, 0xFF,
0x00, 0x6E, 0xFF,
0x00, 0x66, 0xFF,
0x00, 0x5E, 0xFF,
0xFF, 0x08, 0x00,
0x00, 0x66, 0xFF,
0x00, 0x5E, 0xFF,
0xFF, 0x08, 0x00,
]
coco_class_dict = {0: u'__background__',
1: u'person',
2: u'bicycle',
3: u'car',
4: u'motorcycle',
5: u'airplane',
6: u'bus',
7: u'train',
8: u'truck',
9: u'boat',
10: u'traffic light',
11: u'fire hydrant',
12: u'stop sign',
13: u'parking meter',
14: u'bench',
15: u'bird',
16: u'cat',
17: u'dog',
18: u'horse',
19: u'sheep',
20: u'cow',
21: u'elephant',
22: u'bear',
23: u'zebra',
24: u'giraffe',
25: u'backpack',
26: u'umbrella',
27: u'handbag',
28: u'tie',
29: u'suitcase',
30: u'frisbee',
31: u'skis',
32: u'snowboard',
33: u'sports ball',
34: u'kite',
35: u'baseball bat',
36: u'baseball glove',
37: u'skateboard',
38: u'surfboard',
39: u'tennis racket',
40: u'bottle',
41: u'wine glass',
42: u'cup',
43: u'fork',
44: u'knife',
45: u'spoon',
46: u'bowl',
47: u'banana',
48: u'apple',
49: u'sandwich',
50: u'orange',
51: u'broccoli',
52: u'carrot',
53: u'hot dog',
54: u'pizza',
55: u'donut',
56: u'cake',
57: u'chair',
58: u'couch',
59: u'potted plant',
60: u'bed',
61: u'dining table',
62: u'toilet',
63: u'tv',
64: u'laptop',
65: u'mouse',
66: u'remote',
67: u'keyboard',
68: u'cell phone',
69: u'microwave',
70: u'oven',
71: u'toaster',
72: u'sink',
73: u'refrigerator',
74: u'book',
75: u'clock',
76: u'vase',
77: u'scissors',
78: u'teddy bear',
79: u'hair drier',
80: u'toothbrush'}
def getcolor(score):
r = 1.0
g = 1.0
b = 1.0
if score < 0.25:
r = 0
g = 4 * score
elif score < 0.5:
r = 0
b = 1 + 4 * (0.25 - score)
elif score < 0.75:
r = 4*(score - 0.5)
b = 0
else:
g = 1 + 4 * (0.75 - score)
b = 0
r *= 255
g *= 255
b *= 255
return (int(r), int(g), int(b))
def checkpoly(ind_box, w, h):
if np.min(ind_box[:, 0]) < 0:
return 0
elif np.max(ind_box[:, 0]) > w:
return 0
elif np.min(ind_box[:, 1]) < 0:
return 0
elif np.max(ind_box[:, 1]) > h:
return 0
return 1
def makepoly_with_score(batch, boxes, scores, h, w, img, thres=0.1):
w = w.astype(np.int32)
h = h.astype(np.int32)
img = img[:, :, :, ::-1]
img = img.astype(np.uint8)
for i in range(batch):
for box, score in zip(boxes[i], scores[i]):
if score > thres:
color = getcolor(score)
valid_box = box.reshape([-1, 4, 2]).astype(np.int32)
for ind_box in valid_box:
if checkpoly(ind_box, w, h):
img[i] = cv2.drawContours(img[i], [ind_box], 0, color, 1)
img = img.astype(np.float32)
img = img / 255
return img
def makepoly_with_score_color(batch, boxes, scores, h, w, img, color, thres=0.0):
w = int(w)
h = int(h)
img = img.astype(np.uint8)
for i in range(batch):
for box, score in zip(boxes[i], scores[i]):
if score > thres:
valid_box = box.reshape([-1, 4, 2]).astype(np.int32)
for ind_box in valid_box:
ind_box[:, 0] = np.clip(ind_box[:, 0], 0, w)
ind_box[:, 1] = np.clip(ind_box[:, 1], 0, h)
img[i] = cv2.drawContours(img[i], [ind_box], 0, color, 3)
img = img
return img
def debugclassifier(quadboxes, gt_boxes, roi_idx, offset_ch, mask, classifier_ch):
"""
:param quadboxes: (B, 300, 8)
:param gt_boxes: (N',8) gathered
:param roi_idx: (N', 2) idx
:param offset_ch: (N' 8) gatherd
:parma classifier_ch : (N')
:return:
"""
geo_map = np.zeros((quadboxes.shape[0], FLAGS.input_size, FLAGS.input_size, 3), dtype=np.uint8)
for i,(idx,bin) in enumerate(zip(roi_idx, mask)):
b, n = idx
quad = quadboxes[b,n,:].reshape([4, 2]).astype(np.int32)
gt_box = gt_boxes[i].reshape([4, 2]).astype(np.int32)
offset = offset_ch[i].reshape([4, 2]).astype(np.int32)
score = classifier_ch[i]
color = getcolor(score)
if checkpoly(quad, FLAGS.input_size, FLAGS.input_size) and score > 0.5:
geo_map[b] = cv2.drawContours(geo_map[b], [offset], 0, (255, 255, 255), 1)
geo_map[b] = cv2.circle(geo_map[b], (offset[0, 0], offset[0, 1]), 4, (153, 151, 89), -1)
geo_map[b] = cv2.circle(geo_map[b], (offset[1, 0], offset[1, 1]), 4, (153, 89, 151), -1)
geo_map[b] = cv2.circle(geo_map[b], (offset[2, 0], offset[2, 1]), 4, (91, 241, 241), -1)
geo_map[b] = cv2.circle(geo_map[b], (offset[3, 0], offset[3, 1]), 4, (241, 91, 134), -1)
geo_map[b] = cv2.drawContours(geo_map[b], [quad], 0, color, 1)
geo_map[b] = cv2.circle(geo_map[b], (quad[0, 0], quad[0, 1]), 4, (255, 0, 0), -1)
geo_map[b] = cv2.circle(geo_map[b], (quad[1, 0], quad[1, 1]), 4, (0, 255, 0), -1)
geo_map[b] = cv2.circle(geo_map[b], (quad[2, 0], quad[2, 1]), 4, (0, 0, 255), -1)
geo_map[b] = cv2.circle(geo_map[b], (quad[3, 0], quad[3, 1]), 4, (255, 255, 255), -1)
geo_map = geo_map.astype(np.float32)
geo_map = geo_map / 255
return geo_map
def debugclass(quadboxes, gt_label, roi_idx, gt_boxes, mask, classifier_ch):
"""
:param quadboxes: (B, 300, 8)
:param gt_label: (B, 300)
:param roi_idx: (N', 2) idx
:param gt_boxes: (B, 300, 8) gatherd
:parma classifier_ch : (N')
:return:
"""
geo_map = np.zeros((quadboxes.shape[0], FLAGS.input_size, FLAGS.input_size, 3), dtype=np.uint8)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 0.5
lineType = 2
for i, (idx, bin) in enumerate(zip(roi_idx, mask)):
b, n = idx
quad = quadboxes[b, n, :].reshape([4, 2]).astype(np.int32)
class_info = classifier_ch[i]
class_idx = np.argmax(class_info, axis= -1)
class_score = np.amax(class_info, axis=-1)
rgb = (coco_color_map[3*class_idx], coco_color_map[3*class_idx+1], coco_color_map[3*class_idx+2])
if checkpoly(quad, FLAGS.input_size, FLAGS.input_size) and class_score > 0.5:
geo_map[b] = cv2.drawContours(geo_map[b], [quad], 0, rgb, 1)
#cv2.putText(geo_map[b], (coco_class_dict[class_idx + 1] + u" " + str(class_score)),
# (quad[0][0],quad[0][1]),
# font,
# fontScale,
# rgb,
# lineType)
label_shape = gt_label.shape
fontcolor = (255, 0, 0)
for b in range(label_shape[0]):
for i in range(label_shape[1]):
if gt_label[b, i] != 0:
quad = gt_boxes[b, i, :].reshape([4, 2]).astype(np.int32)
geo_map[b] = cv2.drawContours(geo_map[b], [quad], 0, fontcolor, 1)
#cv2.putText(geo_map[b], (coco_class_dict[gt_label[b,i]]),
# (quad[0][0], quad[0][1]),
# font,
# fontScale,
# fontColor,
# lineType)
geo_map = geo_map.astype(np.float32)
geo_map = geo_map / 255
return geo_map
def viz_pos_neg_anchor(concat_anchor_box, concat_score_box, pos, mask, input_shape):
"""
:param concat_anchor_box: (B, N', 8)
:param concat_score_box: (B, N', 1)
:param pos: (B, N', 1)
:param mask: (B, N', 1)
:return:
"""
viz_anchor_box_pos_img = np.zeros(input_shape, dtype=np.uint8)
viz_anchor_box_neg_img = np.zeros(input_shape, dtype=np.uint8)
for b, (n_concat_anchor_box, n_concat_score_box, n_pos, n_mask) in enumerate(zip(concat_anchor_box, concat_score_box, pos, mask)):
for each_concat_anchor_box, each_concat_score_box, each_pos, each_mask in zip(n_concat_anchor_box,
n_concat_score_box,
n_pos, n_mask):
if each_mask[0] == 1:
quad = each_concat_anchor_box.reshape([4, 2]).astype(np.int32)
color = getcolor(each_concat_score_box[0])
if each_pos[0] == 1:
viz_anchor_box_pos_img[b] = cv2.drawContours(viz_anchor_box_pos_img[b], [quad], 0, color, 1)
else:
viz_anchor_box_neg_img[b] = cv2.drawContours(viz_anchor_box_neg_img[b], [quad], 0, color, 1)
viz_anchor_box_pos_img = viz_anchor_box_pos_img.astype(np.float32)
viz_anchor_box_pos_img = viz_anchor_box_pos_img / 255
viz_anchor_box_neg_img = viz_anchor_box_neg_img.astype(np.float32)
viz_anchor_box_neg_img = viz_anchor_box_neg_img / 255
return viz_anchor_box_pos_img, viz_anchor_box_neg_img