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import os
import math
import numpy as np
import cv2
from rknn.api import RKNN
import PIL.Image as Image
import PIL.ImageDraw as ImageDraw
import PIL.ImageFont as ImageFont
np.set_printoptions(threshold=np.inf)
CLASSES = ('__background__',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
NUM_CLS = 21
CONF_THRESH = 0.5
NMS_THRESH = 0.45
def IntersectBBox(box1, box2):
if box1[0] > box2[2] or box1[2] < box2[0] or box1[1] > box2[3] or box1[3] < box2[1]:
return 0
else:
area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
xx1 = max(box1[0], box2[0])
yy1 = max(box1[1], box2[1])
xx2 = min(box1[2], box2[2])
yy2 = min(box1[3], box2[3])
w = max(0, xx2-xx1)
h = max(0, yy2-yy1)
ovr = w*h / (area1 + area2 - w*h)
return ovr
def ssd_post_process(conf_data, loc_data):
prior_data = np.loadtxt('mbox_priorbox_97.txt', dtype=np.float32)
prior_bboxes = prior_data[:len(loc_data)]
prior_variances = prior_data[len(loc_data):]
prior_num = int(len(loc_data) / 4) # 8732
conf_data = conf_data.reshape(-1, 21)
idx_class_conf = []
bboxes = []
# conf
for prior_idx in range(0, prior_num):
max_val = np.max(conf_data[prior_idx])
max_idx = np.argmax(conf_data[prior_idx])
if max_val > CONF_THRESH and max_idx != 0:
idx_class_conf.append([prior_idx, max_idx, max_val])
# print(len(idx_class_conf))
# boxes
for i in range(0, prior_num):
prior_w = prior_bboxes[4*i+2] - prior_bboxes[4*i]
prior_h = prior_bboxes[4*i+3] - prior_bboxes[4*i+1]
prior_center_x = (prior_bboxes[4*i+2] + prior_bboxes[4*i]) / 2
prior_center_y = (prior_bboxes[4*i+3] + prior_bboxes[4*i+1]) / 2
bbox_center_x = prior_variances[4*i+0] * loc_data[4*i+0][0] * prior_w + prior_center_x
bbox_center_y = prior_variances[4*i+1] * loc_data[4*i+1][0] * prior_h + prior_center_y
bbox_w = math.exp(prior_variances[4*i+2] * loc_data[4*i+2][0]) * prior_w
bbox_h = math.exp(prior_variances[4*i+3] * loc_data[4*i+3][0]) * prior_h
tmp = []
tmp.append(max(min(bbox_center_x - bbox_w / 2., 1), 0))
tmp.append(max(min(bbox_center_y - bbox_h / 2., 1), 0))
tmp.append(max(min(bbox_center_x + bbox_w / 2., 1), 0))
tmp.append(max(min(bbox_center_y + bbox_h / 2., 1), 0))
bboxes.append(tmp)
# print(len(idx_class_conf))
# nms
cur_class_num = 0
idx_class_conf_ = []
for i in range(0, len(idx_class_conf)):
keep = True
k = 0
while k < cur_class_num:
if keep:
ovr = IntersectBBox(bboxes[idx_class_conf[i][0]], bboxes[idx_class_conf_[k][0]])
if idx_class_conf_[k][1] == idx_class_conf[i][1] and ovr > NMS_THRESH:
if idx_class_conf_[k][2] < idx_class_conf[i][2]:
idx_class_conf_.pop(k)
idx_class_conf_.append(idx_class_conf[i])
keep = False
break
k += 1
else:
break
if keep:
idx_class_conf_.append(idx_class_conf[i])
cur_class_num += 1
# print(idx_class_conf_)
box_class_score = []
for i in range(0, len(idx_class_conf_)):
bboxes[idx_class_conf_[i][0]].append(idx_class_conf_[i][1])
bboxes[idx_class_conf_[i][0]].append(idx_class_conf_[i][2])
box_class_score.append(bboxes[idx_class_conf_[i][0]])
img = cv2.imread('./road_300x300.jpg')
img_pil = Image.fromarray(img)
draw = ImageDraw.Draw(img_pil)
font = ImageFont.load_default()
if len(box_class_score) != 0:
print("{:^12} {:^12} {}".format('class', 'score', 'xmin, ymin, xmax, ymax'))
print('-' * 50)
for i in range(0, len(box_class_score)):
x1 = int(box_class_score[i][0]*img.shape[1])
y1 = int(box_class_score[i][1]*img.shape[0])
x2 = int(box_class_score[i][2]*img.shape[1])
y2 = int(box_class_score[i][3]*img.shape[0])
color = (0, int(box_class_score[i][4]/20.0*255), 255)
draw.line([(x1, y1), (x1, y2), (x2, y2),
(x2, y1), (x1, y1)], width=2, fill=color)
display_str = CLASSES[box_class_score[i][4]] + ":" + str(box_class_score[i][5])
try:
display_str_height = np.ceil((1 + 2 * 0.05) * font.getbbox(display_str)[3])+1
except:
display_str_height = np.ceil((1 + 2 * 0.05) * font.getsize(display_str)[1])+1
if y1 > display_str_height:
text_bottom = y1
else:
text_bottom = y1 + display_str_height
try:
_, _, text_width, text_height = font.getbbox(display_str)
except:
text_width, text_height = font.getsize(display_str)
margin = np.ceil(0.05 * text_height)
draw.rectangle([(x1, text_bottom-text_height-2*margin), (x1+text_width, text_bottom)], fill=color)
draw.text((x1+margin, text_bottom-text_height-margin), display_str, fill='black', font=font)
print("{:^12} {:^12.3f} [{:>4}, {:>4}, {:>4}, {:>4}]".format(CLASSES[box_class_score[i][4]], box_class_score[i][5],
x1, y1, x2, y2))
np.copyto(img, np.array(img_pil))
cv2.imwrite("result.jpg", img)
print('Save results to result.jpg!')
if __name__ == '__main__':
if not os.path.exists('./VGG_VOC0712_SSD_300x300_iter_120000.caffemodel'):
print('!!! Missing VGG_VOC0712_SSD_300x300_iter_120000.caffemodel !!!\n'
'1. Download models_VGGNet_VOC0712_SSD_300x300.tar.gz from https://drive.google.com/file/d/0BzKzrI_SkD1_WVVTSmQxU0dVRzA/view\n'
'2. Extract the VGG_VOC0712_SSD_300x300_iter_120000.caffemodel from models_VGGNet_VOC0712_SSD_300x300.tar.gz\n'
'3. Or you can also download caffemodel from https://ftzr.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/asset/vgg-ssd/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel\n')
exit(-1)
# Create RKNN object
rknn = RKNN(verbose=True)
# Pre-process config
print('--> Config model')
rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[1, 1, 1], quant_img_RGB2BGR=True, target_platform='rk3566')
print('done')
# Load model
print('--> Loading model')
ret = rknn.load_caffe(model='./deploy_rm_detection_output.prototxt',
blobs='./VGG_VOC0712_SSD_300x300_iter_120000.caffemodel')
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export rknn model')
ret = rknn.export_rknn('./deploy_rm_detection_output.rknn')
if ret != 0:
print('Export rknn model failed!')
exit(ret)
print('done')
# Set inputs
img = cv2.imread('./road_300x300.jpg')
img = np.expand_dims(img, 0)
# Init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed!')
exit(ret)
print('done')
# Inference
print('--> Running model')
outputs = rknn.inference(inputs=[img], data_format=['nhwc'])
print('done')
outputs[0] = outputs[0].reshape((-1, 1))
outputs[1] = outputs[1].reshape((-1, 1))
np.save('./caffe_vgg-ssd_0.npy', outputs[0])
np.save('./caffe_vgg-ssd_1.npy', outputs[1])
ssd_post_process(outputs[1], outputs[0])
rknn.release()
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