mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
157 lines
4.3 KiB
Python
157 lines
4.3 KiB
Python
from ctypes import *
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import math
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import random
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def sample(probs):
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s = sum(probs)
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probs = [a/s for a in probs]
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r = random.uniform(0, 1)
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for i in range(len(probs)):
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r = r - probs[i]
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if r <= 0:
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return i
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return len(probs)-1
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def c_array(ctype, values):
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arr = (ctype*len(values))()
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arr[:] = values
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return arr
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class BOX(Structure):
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_fields_ = [("x", c_float),
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("y", c_float),
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("w", c_float),
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("h", c_float)]
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class DETECTION(Structure):
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_fields_ = [("bbox", BOX),
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("classes", c_int),
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("prob", POINTER(c_float)),
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("mask", POINTER(c_float)),
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("objectness", c_float),
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("sort_class", c_int)]
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class IMAGE(Structure):
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_fields_ = [("w", c_int),
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("h", c_int),
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("c", c_int),
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("data", POINTER(c_float))]
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class METADATA(Structure):
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_fields_ = [("classes", c_int),
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("names", POINTER(c_char_p))]
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#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
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lib = CDLL("libdarknet.so", RTLD_GLOBAL)
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lib.network_width.argtypes = [c_void_p]
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lib.network_width.restype = c_int
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lib.network_height.argtypes = [c_void_p]
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lib.network_height.restype = c_int
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predict = lib.network_predict
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predict.argtypes = [c_void_p, POINTER(c_float)]
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predict.restype = POINTER(c_float)
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set_gpu = lib.cuda_set_device
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set_gpu.argtypes = [c_int]
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make_image = lib.make_image
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make_image.argtypes = [c_int, c_int, c_int]
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make_image.restype = IMAGE
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get_network_boxes = lib.get_network_boxes
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get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
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get_network_boxes.restype = POINTER(DETECTION)
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make_network_boxes = lib.make_network_boxes
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make_network_boxes.argtypes = [c_void_p]
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make_network_boxes.restype = POINTER(DETECTION)
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free_detections = lib.free_detections
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free_detections.argtypes = [POINTER(DETECTION), c_int]
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free_ptrs = lib.free_ptrs
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free_ptrs.argtypes = [POINTER(c_void_p), c_int]
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network_predict = lib.network_predict
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network_predict.argtypes = [c_void_p, POINTER(c_float)]
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reset_rnn = lib.reset_rnn
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reset_rnn.argtypes = [c_void_p]
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load_net = lib.load_network
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load_net.argtypes = [c_char_p, c_char_p, c_int]
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load_net.restype = c_void_p
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do_nms_obj = lib.do_nms_obj
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do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
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do_nms_sort = lib.do_nms_sort
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do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
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free_image = lib.free_image
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free_image.argtypes = [IMAGE]
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letterbox_image = lib.letterbox_image
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letterbox_image.argtypes = [IMAGE, c_int, c_int]
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letterbox_image.restype = IMAGE
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load_meta = lib.get_metadata
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lib.get_metadata.argtypes = [c_char_p]
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lib.get_metadata.restype = METADATA
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load_image = lib.load_image_color
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load_image.argtypes = [c_char_p, c_int, c_int]
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load_image.restype = IMAGE
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rgbgr_image = lib.rgbgr_image
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rgbgr_image.argtypes = [IMAGE]
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predict_image = lib.network_predict_image
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predict_image.argtypes = [c_void_p, IMAGE]
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predict_image.restype = POINTER(c_float)
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def classify(net, meta, im):
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out = predict_image(net, im)
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res = []
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for i in range(meta.classes):
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res.append((meta.names[i], out[i]))
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res = sorted(res, key=lambda x: -x[1])
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return res
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def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
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im = load_image(image, 0, 0)
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num = c_int(0)
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pnum = pointer(num)
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predict_image(net, im)
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dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
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num = pnum[0]
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if (nms): do_nms_obj(dets, num, meta.classes, nms);
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res = []
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for j in range(num):
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for i in range(meta.classes):
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if dets[j].prob[i] > 0:
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b = dets[j].bbox
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res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
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res = sorted(res, key=lambda x: -x[1])
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free_image(im)
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free_detections(dets, num)
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return res
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if __name__ == "__main__":
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#net = load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0)
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#im = load_image("data/wolf.jpg", 0, 0)
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#meta = load_meta("cfg/imagenet1k.data")
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#r = classify(net, meta, im)
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#print r[:10]
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net = load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
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meta = load_meta("cfg/coco.data")
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r = detect(net, meta, "data/dog.jpg")
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print r
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