Spaces:
Runtime error
Runtime error
File size: 5,608 Bytes
80f1cdc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
import time
import torch
from torch.backends import cudnn
from backbone import HybridNetsBackbone
import cv2
import numpy as np
from glob import glob
from utils.utils import letterbox, scale_coords, postprocess, BBoxTransform, ClipBoxes, restricted_float, boolean_string
from utils.plot import STANDARD_COLORS, standard_to_bgr, get_index_label, plot_one_box
import os
from torchvision import transforms
import argparse
parser = argparse.ArgumentParser('HybridNets: End-to-End Perception Network - DatVu')
parser.add_argument('-c', '--compound_coef', type=int, default=3, help='Coefficient of efficientnet backbone')
parser.add_argument('--source', type=str, default='demo/video', help='The demo video folder')
parser.add_argument('--output', type=str, default='demo_result', help='Output folder')
parser.add_argument('-w', '--load_weights', type=str, default='weights/hybridnets.pth')
parser.add_argument('--nms_thresh', type=restricted_float, default='0.25')
parser.add_argument('--iou_thresh', type=restricted_float, default='0.3')
parser.add_argument('--cuda', type=boolean_string, default=True)
parser.add_argument('--float16', type=boolean_string, default=True, help="Use float16 for faster inference")
args = parser.parse_args()
compound_coef = args.compound_coef
source = args.source
if source.endswith("/"):
source = source[:-1]
output = args.output
if output.endswith("/"):
output = output[:-1]
weight = args.load_weights
video_src = glob(f'{source}/*.mp4')[0]
os.makedirs(output, exist_ok=True)
video_out = f'{output}/output.mp4'
input_imgs = []
shapes = []
# replace this part with your project's anchor config
anchor_ratios = [(0.62, 1.58), (1.0, 1.0), (1.58, 0.62)]
anchor_scales = [2 ** 0, 2 ** 0.70, 2 ** 1.32]
threshold = args.nms_thresh
iou_threshold = args.iou_thresh
use_cuda = args.cuda
use_float16 = args.float16
cudnn.fastest = True
cudnn.benchmark = True
obj_list = ['car']
color_list = standard_to_bgr(STANDARD_COLORS)
resized_shape = 640
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
transform = transforms.Compose([
transforms.ToTensor(),
normalize,
])
# print(x.shape)
model = HybridNetsBackbone(compound_coef=compound_coef, num_classes=len(obj_list),
ratios=anchor_ratios, scales=anchor_scales, seg_classes=2)
try:
model.load_state_dict(torch.load(weight, map_location='cuda' if use_cuda else 'cpu'))
except:
model.load_state_dict(torch.load(weight, map_location='cuda' if use_cuda else 'cpu')['model'])
model.requires_grad_(False)
model.eval()
if use_cuda:
model = model.cuda()
if use_float16:
model = model.half()
cap = cv2.VideoCapture(video_src)
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out_stream = cv2.VideoWriter(video_out, fourcc, 30.0,
(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
t1 = time.time()
frame_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h0, w0 = frame.shape[:2] # orig hw
r = resized_shape / max(h0, w0) # resize image to img_size
input_img = cv2.resize(frame, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_AREA)
h, w = input_img.shape[:2]
(input_img, _, _), ratio, pad = letterbox((input_img, input_img.copy(), input_img.copy()), resized_shape, auto=True,
scaleup=False)
shapes = ((h0, w0), ((h / h0, w / w0), pad))
if use_cuda:
x = transform(input_img).cuda()
else:
x = transform(input_img)
x = x.to(torch.float32 if not use_float16 else torch.float16)
x.unsqueeze_(0)
with torch.no_grad():
features, regression, classification, anchors, seg = model(x)
seg = seg[:, :, 12:372, :]
da_seg_mask = torch.nn.functional.interpolate(seg, size=[h0, w0], mode='nearest')
_, da_seg_mask = torch.max(da_seg_mask, 1)
da_seg_mask_ = da_seg_mask[0].squeeze().cpu().numpy().round()
color_area = np.zeros((da_seg_mask_.shape[0], da_seg_mask_.shape[1], 3), dtype=np.uint8)
color_area[da_seg_mask_ == 1] = [0, 255, 0]
color_area[da_seg_mask_ == 2] = [0, 0, 255]
color_seg = color_area[..., ::-1]
# cv2.imwrite('seg_only_{}.jpg'.format(i), color_seg)
color_mask = np.mean(color_seg, 2)
frame[color_mask != 0] = frame[color_mask != 0] * 0.5 + color_seg[color_mask != 0] * 0.5
frame = frame.astype(np.uint8)
# cv2.imwrite('seg_{}.jpg'.format(i), ori_img)
regressBoxes = BBoxTransform()
clipBoxes = ClipBoxes()
out = postprocess(x,
anchors, regression, classification,
regressBoxes, clipBoxes,
threshold, iou_threshold)
out = out[0]
out['rois'] = scale_coords(frame[:2], out['rois'], shapes[0], shapes[1])
for j in range(len(out['rois'])):
x1, y1, x2, y2 = out['rois'][j].astype(int)
obj = obj_list[out['class_ids'][j]]
score = float(out['scores'][j])
plot_one_box(frame, [x1, y1, x2, y2], label=obj, score=score,
color=color_list[get_index_label(obj, obj_list)])
out_stream.write(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
frame_count += 1
t2 = time.time()
print("frame: {}".format(frame_count))
print("second: {}".format(t2-t1))
print("fps: {}".format((t2-t1)/frame_count))
cap.release()
out_stream.release()
|