Spaces:
Running
Running
File size: 4,654 Bytes
95f8bbc aa34300 95f8bbc aa34300 95f8bbc aa34300 95f8bbc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from .util import *
from .darknet import Darknet
from .preprocess import prep_image, inp_to_image
import pandas as pd
import random
import argparse
import pickle as pkl
def get_test_input(input_dim, CUDA):
img = cv2.imread("imgs/messi.jpg")
img = cv2.resize(img, (input_dim, input_dim))
img_ = img[:,:,::-1].transpose((2,0,1))
img_ = img_[np.newaxis,:,:,:]/255.0
img_ = torch.from_numpy(img_).float()
img_ = Variable(img_)
if CUDA:
img_ = img_
return img_
def prep_image(img, inp_dim):
"""
Prepare image for inputting to the neural network.
Returns a Variable
"""
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = cv2.resize(orig_im, (inp_dim, inp_dim))
img_ = img[:,:,::-1].transpose((2,0,1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
def write(x, img):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
cls = int(x[-1])
label = "{0}".format(classes[cls])
color = random.choice(colors)
cv2.rectangle(img, c1, c2,color, 1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
cv2.rectangle(img, c1, c2,color, -1)
cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
return img
def arg_parse():
"""
Parse arguements to the detect module
"""
parser = argparse.ArgumentParser(description='YOLO v3 Cam Demo')
parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.25)
parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
parser.add_argument("--reso", dest = 'reso', help =
"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
default = "160", type = str)
return parser.parse_args()
if __name__ == '__main__':
cfgfile = "cfg/yolov3-spp.cfg"
weightsfile = "yolov3-spp.weights"
num_classes = 80
args = arg_parse()
confidence = float(args.confidence)
nms_thesh = float(args.nms_thresh)
start = 0
CUDA = torch.cuda.is_available()
num_classes = 80
bbox_attrs = 5 + num_classes
model = Darknet(cfgfile)
model.load_weights(weightsfile)
model.net_info["height"] = args.reso
inp_dim = int(model.net_info["height"])
assert inp_dim % 32 == 0
assert inp_dim > 32
if CUDA:
model
model.eval()
videofile = 'video.avi'
cap = cv2.VideoCapture(0)
assert cap.isOpened(), 'Cannot capture source'
frames = 0
start = time.time()
while cap.isOpened():
ret, frame = cap.read()
if ret:
img, orig_im, dim = prep_image(frame, inp_dim)
# im_dim = torch.FloatTensor(dim).repeat(1,2)
if CUDA:
im_dim = im_dim
img = img
output = model(Variable(img), CUDA)
output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
if type(output) == int:
frames += 1
print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
continue
output[:,1:5] = torch.clamp(output[:,1:5], 0.0, float(inp_dim))/inp_dim
# im_dim = im_dim.repeat(output.size(0), 1)
output[:,[1,3]] *= frame.shape[1]
output[:,[2,4]] *= frame.shape[0]
classes = load_classes('data/coco.names')
colors = pkl.load(open("pallete", "rb"))
list(map(lambda x: write(x, orig_im), output))
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
frames += 1
print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
else:
break
|