def parse_network_cfg(cfg_path): # list of section sections = read_cfg(cfg_path) if sections[0].type != '[net]': print 'Error::cfg first section must be [net], %s' %(sections[0].type) exit() net = darknet.network() parse_net_options(sections[0].options, net) params = darknet.network() params.h = net.h params.w = net.w params.c = net.c params.inputs = net.inputs params.batch = net.batch # loop all section except the first one net.layers = [] for s in sections: if s.type == '[net]': continue options = s.options # keras model creation if s.type == '[crop]': l=parse_corp(options, params) elif s.type == '[convolutional]': l=parse_convolutional(options, params) elif s.type == '[maxpool]': l=parse_maxpool(options, params) elif s.type == '[connected]': l=parse_connected(options, params) elif s.type == '[dropout]': l=parse_dropout(options, params) elif s.type == '[detection]': l=parse_detection(options, params) else: print 'Error::Type not recognized: %s' %(s.type) exit() print s.type l.type = s.type net.layers.append(l) params.h = l.outh params.w = l.outw params.c = l.outc params.inputs = l.outputs return net
def detect_image(image_path):
net = dn.network(net_cfg, weights)
meta = dn.metadata(meta_cfg)
img = cv2.imread(image_path)
res = dn.detect(net, meta, img)
img = draw_box(img, res)
cv2.imshow("frame", img)
cv2.waitKey(0)
return res
def detect_video(video_path):
net = dn.network(net_cfg, weights)
meta = dn.metadata(meta_cfg)
capture = cv2.VideoCapture(video_path)
read_flag, frame = capture.read()
while (read_flag):
res = dn.detect(net, meta, frame)
img = draw_box(frame, res)
cv2.imshow("frame", img)
cv2.waitKey(0)
read_flag, frame = capture.read()
capture.release()
import sys, os
import cv2
sys.path.append(os.path.join(os.getcwd(), 'python/'))
import darknet as dn
# net_cfg="cfg/yolo9000.cfg"
# weights="yolo9000.weights"
# meta_cfg = "cfg/combine9k.data"
net_cfg = "cfg/yolov2.cfg"
weights = "yolov2.weights"
meta_cfg = "cfg/coco.data"
net = dn.network(net_cfg, weights)
meta = dn.metadata(meta_cfg)
def draw_box(img, results):
for box in results:
x = box[2][0]
y = box[2][1]
box_width = box[2][2]
box_height = box[2][3]
x1 = int(x - 1 / 2 * box_width)
y1 = int(y - 1 / 2 * box_height)
x2 = int(x + 1 / 2 * box_width)
y2 = int(y + 1 / 2 * box_height)
img = cv2.putText(img, box[0], (x1, y1), cv2.FONT_HERSHEY_PLAIN, 1,