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Merge pull request #2359 from aughey/master

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Change for py  code
pull/4477/head
Alexey 6 years ago committed by GitHub
parent 0543278a5b 7587d47c46
commit c999f53e9d
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2 changed files with 31 additions and 225 deletions
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  1. 19
      build/darknet/x64/darknet.py
  2. 237
      build/darknet/x64/darknet_video.py

19
build/darknet/x64/darknet.py
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@ -125,6 +125,15 @@ lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int
copy_image_from_bytes = lib.copy_image_from_bytes
copy_image_from_bytes.argtypes = [IMAGE,c_char_p]
def network_width(net):
return lib.network_width(net)
def network_height(net):
return lib.network_height(net)
predict = lib.network_predict_ptr
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)
@ -223,6 +232,13 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
"""
#pylint: disable= C0321
im = load_image(image, 0, 0)
if debug: print("Loaded image")
ret = detect_image(net, meta, im, thresh, hier_thresh, nms, debug)
free_image(im)
if debug: print("freed image")
return ret
def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
#import cv2
#custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,)
#custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
@ -230,7 +246,6 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
#import scipy.misc
#custom_image = scipy.misc.imread(image)
#im, arr = array_to_image(custom_image) # you should comment line below: free_image(im)
if debug: print("Loaded image")
num = c_int(0)
if debug: print("Assigned num")
pnum = pointer(num)
@ -267,8 +282,6 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
if debug: print("did range")
res = sorted(res, key=lambda x: -x[1])
if debug: print("did sort")
free_image(im)
if debug: print("freed image")
free_detections(dets, num)
if debug: print("freed detections")
return res

237
build/darknet/x64/darknet_video.py
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@ -5,222 +5,7 @@ import os
import cv2
import numpy as np
import time
def sample(probs):
s = sum(probs)
probs = [a/s for a in probs]
r = random.uniform(0, 1)
for i in range(len(probs)):
r = r - probs[i]
if r <= 0:
return i
return len(probs)-1
def c_array(ctype, values):
arr = (ctype*len(values))()
arr[:] = values
return arr
class BOX(Structure):
_fields_ = [("x", c_float),
("y", c_float),
("w", c_float),
("h", c_float)]
class DETECTION(Structure):
_fields_ = [("bbox", BOX),
("classes", c_int),
("prob", POINTER(c_float)),
("mask", POINTER(c_float)),
("objectness", c_float),
("sort_class", c_int)]
class IMAGE(Structure):
_fields_ = [("w", c_int),
("h", c_int),
("c", c_int),
("data", POINTER(c_float))]
class METADATA(Structure):
_fields_ = [("classes", c_int),
("names", POINTER(c_char_p))]
hasGPU = True
lib = CDLL("yolo_cpp_dll.dll", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int
predict = lib.network_predict_ptr
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)
if hasGPU:
set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]
make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE
get_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = \
[c_void_p, c_int, c_int, c_float, c_float, POINTER(
c_int), c_int, POINTER(c_int), c_int]
get_network_boxes.restype = POINTER(DETECTION)
make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)
free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]
free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
network_predict = lib.network_predict_ptr
network_predict.argtypes = [c_void_p, POINTER(c_float)]
reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]
load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p
load_net_custom = lib.load_network_custom
load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int]
load_net_custom.restype = c_void_p
do_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
free_image = lib.free_image
free_image.argtypes = [IMAGE]
letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE
load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA
load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE
rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]
predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)
def array_to_image(arr):
import numpy as np
arr = arr.transpose(2, 0, 1)
c = arr.shape[0]
h = arr.shape[1]
w = arr.shape[2]
arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
data = arr.ctypes.data_as(POINTER(c_float))
im = IMAGE(w, h, c, data)
return im, arr
def classify(net, meta, im):
out = predict_image(net, im)
res = []
for i in range(meta.classes):
if altNames is None:
nameTag = meta.names[i]
else:
nameTag = altNames[i]
res.append((nameTag, out[i]))
res = sorted(res, key=lambda x: -x[1])
return res
def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False):
im, arr = array_to_image(image)
if debug:
print("Loaded image")
num = c_int(0)
if debug:
print("Assigned num")
pnum = pointer(num)
if debug:
print("Assigned pnum")
predict_image(net, im)
if debug:
print("did prediction")
# dets = get_network_boxes(
# net, image.shape[1], image.shape[0],
# thresh, hier_thresh,
# None, 0, pnum, 0) # OpenCV
dets = get_network_boxes(net, im.w, im.h,
thresh, hier_thresh, None, 0, pnum, 0)
if debug:
print("Got dets")
num = pnum[0]
if debug:
print("got zeroth index of pnum")
if nms:
do_nms_sort(dets, num, meta.classes, nms)
if debug:
print("did sort")
res = []
if debug:
print("about to range")
for j in range(num):
if debug:
print("Ranging on "+str(j)+" of "+str(num))
if debug:
print("Classes: "+str(meta), meta.classes, meta.names)
for i in range(meta.classes):
if debug:
print("Class-ranging on "+str(i)+" of " +
str(meta.classes)+"= "+str(dets[j].prob[i]))
if dets[j].prob[i] > 0:
b = dets[j].bbox
if altNames is None:
nameTag = meta.names[i]
else:
nameTag = altNames[i]
if debug:
print("Got bbox", b)
print(nameTag)
print(dets[j].prob[i])
print((b.x, b.y, b.w, b.h))
res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h)))
if debug:
print("did range")
res = sorted(res, key=lambda x: -x[1])
if debug:
print("did sort")
# free_image(im)
if debug:
print("freed image")
free_detections(dets, num)
if debug:
print("freed detections")
return res
import darknet
def convertBack(x, y, w, h):
xmin = int(round(x - (w / 2)))
@ -255,6 +40,7 @@ altNames = None
def YOLO():
global metaMain, netMain, altNames
configPath = "./cfg/yolov3.cfg"
weightPath = "./yolov3.weights"
@ -269,10 +55,10 @@ def YOLO():
raise ValueError("Invalid data file path `" +
os.path.abspath(metaPath)+"`")
if netMain is None:
netMain = load_net_custom(configPath.encode(
netMain = darknet.load_net_custom(configPath.encode(
"ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1
if metaMain is None:
metaMain = load_meta(metaPath.encode("ascii"))
metaMain = darknet.load_meta(metaPath.encode("ascii"))
if altNames is None:
try:
with open(metaPath) as metaFH:
@ -299,17 +85,24 @@ def YOLO():
cap.set(4, 720)
out = cv2.VideoWriter(
"output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0,
(lib.network_width(netMain), lib.network_height(netMain)))
(darknet.network_width(netMain), darknet.network_height(netMain)))
print("Starting the YOLO loop...")
# Create an image we reuse for each detect
darknet_image = darknet.make_image(darknet.network_width(netMain),
darknet.network_height(netMain),3)
while True:
prev_time = time.time()
ret, frame_read = cap.read()
frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb,
(lib.network_width(netMain),
lib.network_height(netMain)),
(darknet.network_width(netMain),
darknet.network_height(netMain)),
interpolation=cv2.INTER_LINEAR)
detections = detect(netMain, metaMain, frame_resized, thresh=0.25)
darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes())
detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25)
image = cvDrawBoxes(detections, frame_resized)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
print(1/(time.time()-prev_time))

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