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import cv2
import numpy as np
import imageio as io
from matplotlib import cm
import time
import PIL
import pycocotools.mask as mask_utils
from . import palette
CLASSES = ['adult', 'baby', 'bag', 'ball', 'ballon', 'basket', 'bat', 'bed',
'bench', 'beverage', 'bike', 'bird', 'blanket', 'board', 'book', 'bottle',
'bowl', 'box', 'bread', 'brush', 'bucket', 'cabinet', 'cake', 'camera', 'can',
'candle', 'car', 'card', 'carpet', 'cart', 'cat', 'cellphone', 'chair',
'child', 'chopstick', 'cloth', 'computer', 'condiment', 'cookie', 'countertop',
'cover', 'cup', 'curtain', 'dog', 'door', 'drawer', 'dustbin', 'egg', 'fan',
'faucet', 'fence', 'flower', 'fork', 'fridge', 'fruit', 'gift', 'glass',
'glasses', 'glove', 'grain', 'guitar', 'hat', 'helmet', 'horse', 'iron',
'knife', 'light', 'lighter', 'mat', 'meat', 'microphone', 'microwave', 'mop',
'net', 'noodle', 'others', 'oven', 'pan', 'paper', 'piano', 'pillow', 'pizza',
'plant', 'plate', 'pot', 'powder', 'rack', 'racket', 'rag', 'ring', 'scissor',
'shelf', 'shoe', 'simmering', 'sink', 'slide', 'sofa', 'spatula', 'sponge',
'spoon', 'spray', 'stairs', 'stand', 'stove', 'switch', 'table', 'teapot',
'towel', 'toy', 'tray', 'tv', 'vaccum', 'vegetable', 'washer', 'window',
'ceiling', 'floor', 'grass', 'ground', 'rock', 'sand', 'sky', 'snow',
'tree', 'wall', 'water']
def dump_predictions(pred, lbl_set, img, prefix):
'''
Save:
1. Predicted labels for evaluation
2. Label heatmaps for visualization
'''
lbl_set = palette.tensor.astype(np.uint8)
sz = img.shape[:-1]
# Upsample predicted soft label maps
# pred_dist = pred.copy()
pred_dist = cv2.resize(pred, sz[::-1])[:]
# Argmax to get the hard label for index
pred_lbl = np.argmax(pred_dist, axis=-1)
pred_lbl = np.array(lbl_set, dtype=np.int32)[pred_lbl]
mask = np.float32(pred_lbl.sum(2) > 0)[:,:,None]
alpha = 0.5
img_with_label = mask * (np.float32(img) * alpha + \
np.float32(pred_lbl) * (1-alpha)) + (1-mask) * np.float32(img)
# Visualize label distribution for object 1 (debugging/analysis)
pred_soft = pred_dist[..., 1]
pred_soft = cv2.resize(pred_soft, (img.shape[1], img.shape[0]),
interpolation=cv2.INTER_NEAREST)
pred_soft = cm.jet(pred_soft)[..., :3] * 255.0
img_with_heatmap1 = np.float32(img) * 0.5 + np.float32(pred_soft) * 0.5
# Save blend image for visualization
io.imwrite('%s_blend.jpg' % prefix, np.uint8(img_with_label))
if prefix[-4] != '.': # Super HACK-y
imname2 = prefix + '_mask.png'
else:
imname2 = prefix.replace('jpg','png')
# Save predicted labels for evaluation
io.imwrite(imname2, np.uint8(pred_lbl))
return img_with_label, pred_lbl, img_with_heatmap1
def make_gif(video, outname='/tmp/test.gif', sz=256):
if hasattr(video, 'shape'):
video = video.cpu()
if video.shape[0] == 3:
video = video.transpose(0, 1)
video = video.numpy().transpose(0, 2, 3, 1)
video = (video*255).astype(np.uint8)
video = [cv2.resize(vv, (sz, sz)) for vv in video]
if outname is None:
return np.stack(video)
io.mimsave(outname, video, duration = 0.2)
def get_color(idx):
idx = idx * 17
color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
return color
def plot_tracking(image, obs, obj_ids, scores=None, frame_id=0, fps=0.):
im = np.ascontiguousarray(np.copy(image))
im_h, im_w = im.shape[:2]
text_scale = max(1, image.shape[1] / 1600.)
text_thickness = 1 if text_scale > 1.1 else 1
line_thickness = max(1, int(image.shape[1] / 150.))
alpha = 0.4
for i, ob in enumerate(obs):
obj_id = int(obj_ids[i])
id_text = '{}_{}'.format(CLASSES[ob['class_id']], str(obj_id))
_line_thickness = 1 if obj_id <= 0 else line_thickness
color = get_color(obj_id)
if len(ob) == 4:
x1, y1, w, h = ob
intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
cv2.rectangle(im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)
cv2.putText(im, id_text, (intbox[0], intbox[1] + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255),
thickness=text_thickness)
elif isinstance(ob, dict):
mask = mask_utils.decode(ob)
mask = cv2.resize(mask, (im_w, im_h), interpolation=cv2.INTER_LINEAR)
mask = (mask > 0.5).astype(np.uint8)[:,:,None]
mask_color = mask * color
im = (1 - mask) * im + mask * (alpha*im + (1-alpha)*mask_color)
# put the id tag at the mask centroid
M = cv2.moments(mask)
if M['m00'] != 0:
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
else:
cx, cy = mask.shape[1]//2, mask.shape[0]//2
(text_width, text_height), _ = cv2.getTextSize(id_text, cv2.FONT_HERSHEY_PLAIN, text_scale, text_thickness)
tag = np.zeros((text_height + 10, text_width + 10, 3), dtype=np.uint8)
cv2.putText(tag, id_text, (5, text_height + 5), cv2.FONT_HERSHEY_PLAIN, text_scale, (255,255,255), thickness=text_thickness)
# put the tag on image
start_x = cx - tag.shape[1] // 2
start_y = cy - tag.shape[0] // 2
tag_height, tag_width = tag.shape[:2]
start_x = max(start_x, 0)
start_y = max(start_y, 0)
end_x = start_x + tag_width
end_y = start_y + tag_height
if end_x > im.shape[1]:
end_x = im.shape[1]
tag_width = im.shape[1] - start_x
if end_y > im.shape[0]:
end_y = im.shape[0]
tag_height = im.shape[0] - start_y
im[start_y:end_y, start_x:end_x] = tag[:tag_height, :tag_width]
else:
raise ValueError('Observation format not supported.')
return im
def vis_pose(oriImg, points):
pa = np.zeros(15)
pa[2] = 0
pa[12] = 8
pa[8] = 4
pa[4] = 0
pa[11] = 7
pa[7] = 3
pa[3] = 0
pa[0] = 1
pa[14] = 10
pa[10] = 6
pa[6] = 1
pa[13] = 9
pa[9] = 5
pa[5] = 1
colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0],
[0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
[170,0,255],[255,0,255]]
canvas = oriImg
stickwidth = 4
x = points[0, :]
y = points[1, :]
for n in range(len(x)):
pair_id = int(pa[n])
x1 = int(x[pair_id])
y1 = int(y[pair_id])
x2 = int(x[n])
y2 = int(y[n])
if x1 >= 0 and y1 >= 0 and x2 >= 0 and y2 >= 0:
cv2.line(canvas, (x1, y1), (x2, y2), colors[n], 8)
return canvas
def draw_skeleton(aa, kp, color, show_skeleton_labels=False, dataset= "PoseTrack"):
if dataset == "COCO":
skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13],
[6, 12], [7, 13], [6, 7], [6, 8], [7, 9], [8, 10],
[9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
kp_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder',
'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist',
'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle']
elif dataset == "PoseTrack":
skeleton = [[10, 11], [11, 12], [9,8], [8,7],
[10, 13], [9, 13], [13, 15], [10,4],
[4,5], [5,6], [9,3], [3,2], [2,1]]
kp_names = ['right_ankle', 'right_knee', 'right_pelvis',
'left_pelvis', 'left_knee', 'left_ankle',
'right_wrist', 'right_elbow', 'right_shoulder',
'left_shoulder', 'left_elbow', 'left_wrist',
'upper_neck', 'nose', 'head']
for i, j in skeleton:
if kp[i-1][0] >= 0 and kp[i-1][1] >= 0 and kp[j-1][0] >= 0 and kp[j-1][1] >= 0 and \
(len(kp[i-1]) <= 2 or (len(kp[i-1]) > 2 and kp[i-1][2] > 0.1 and kp[j-1][2] > 0.1)):
st = (int(kp[i-1][0]), int(kp[i-1][1]))
ed = (int(kp[j-1][0]), int(kp[j-1][1]))
cv2.line(aa, st, ed, color, max(1, int(aa.shape[1]/150.)))
for j in range(len(kp)):
if kp[j][0] >= 0 and kp[j][1] >= 0:
pt = (int(kp[j][0]), int(kp[j][1]))
if len(kp[j]) <= 2 or (len(kp[j]) > 2 and kp[j][2] > 1.1):
cv2.circle(aa, pt, 2, tuple((0,0,255)), 2)
elif len(kp[j]) <= 2 or (len(kp[j]) > 2 and kp[j][2] > 0.1):
cv2.circle(aa, pt, 2, tuple((255,0,0)), 2)
if show_skeleton_labels and (len(kp[j]) <= 2 or (len(kp[j]) > 2 and kp[j][2] > 0.1)):
cv2.putText(aa, kp_names[j], tuple(kp[j][:2]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0))
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