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Merge pull request #534 from danielgordon10/yolov3
PYTHON3 IS GARBAGE GO AWAY DANIEL
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commit
13413e9c0a
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.gitignore
vendored
2
.gitignore
vendored
@ -25,3 +25,5 @@ ehthumbs.db
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Icon?
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Thumbs.db
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*.swp
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*.a
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*.so
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@ -39,18 +39,17 @@ import darknet as dn
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net = dn.load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
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meta = dn.load_meta("cfg/coco.data")
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r = dn.detect(net, meta, "data/dog.jpg")
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print r
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print(r)
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# scipy
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arr= imread('data/dog.jpg')
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im = array_to_image(arr)
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r = detect2(net, meta, im)
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print r
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print(r)
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# OpenCV
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arr = cv2.imread('data/dog.jpg')
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im = array_to_image(arr)
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dn.rgbgr_image(im)
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r = detect2(net, meta, im)
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print r
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print(r)
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@ -13,15 +13,14 @@ dn.set_gpu(0)
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net = dn.load_net("cfg/yolo-thor.cfg", "/home/pjreddie/backup/yolo-thor_final.weights", 0)
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meta = dn.load_meta("cfg/thor.data")
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r = dn.detect(net, meta, "data/bedroom.jpg")
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print r
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print(r)
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# And then down here you could detect a lot more images like:
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r = dn.detect(net, meta, "data/eagle.jpg")
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print r
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print(r)
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r = dn.detect(net, meta, "data/giraffe.jpg")
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print r
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print(r)
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r = dn.detect(net, meta, "data/horses.jpg")
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print r
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print(r)
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r = dn.detect(net, meta, "data/person.jpg")
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print r
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print(r)
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@ -13,9 +13,21 @@ def sample(probs):
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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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new_values = values.ctypes.data_as(POINTER(ctype))
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return new_values
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def array_to_image(arr):
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import numpy as np
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# need to return old values to avoid python freeing memory
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arr = arr.transpose(2,0,1)
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c = arr.shape[0]
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h = arr.shape[1]
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w = arr.shape[2]
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arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
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data = arr.ctypes.data_as(POINTER(c_float))
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im = IMAGE(w,h,c,data)
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return im, arr
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class BOX(Structure):
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_fields_ = [("x", c_float),
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@ -43,7 +55,6 @@ class METADATA(Structure):
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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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@ -142,6 +153,26 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
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free_detections(dets, num)
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return res
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def detect_numpy(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
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im, arr = array_to_image(image)
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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_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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@ -150,7 +181,27 @@ if __name__ == "__main__":
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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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import scipy.misc
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import time
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'''
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t_start = time.time()
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for ii in range(100):
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r = detect(net, meta, 'data/dog.jpg')
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print(time.time() - t_start)
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print(r)
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image = scipy.misc.imread('data/dog.jpg')
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for ii in range(100):
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scipy.misc.imsave('/tmp/image.jpg', image)
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r = detect(net, meta, '/tmp/image.jpg')
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print(time.time() - t_start)
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print(r)
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'''
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image = scipy.misc.imread('data/dog.jpg')
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t_start = time.time()
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for ii in range(100):
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r = detect_numpy(net, meta, image)
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print(time.time() - t_start)
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print(r)
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