python stuff

examples/detector-scipy-opencv.py

@@ -38,14 +38,18 @@ import darknet as dn
 # Darknet
 net = dn.load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
 meta = dn.load_meta("cfg/coco.data")
-r = dn.detect(net, meta, "data/dog.jpg")
-print r
+import time
+tStart = time.time()
+for i in range(10):
+    r = dn.detect(net, meta, "data/dog.jpg")
+print(time.time() - tStart)
 
 # scipy
 arr= imread('data/dog.jpg')
-im = array_to_image(arr)
-r = detect2(net, meta, im)
-print r
+tStart = time.time()
+for i in range(10):
+    r = dn.detect(net, meta, arr)
+print(time.time() - tStart)
 
 # OpenCV
 arr = cv2.imread('data/dog.jpg')

examples/detector.py

@@ -9,20 +9,11 @@ sys.path.append(os.path.join(os.getcwd(),'python/'))
 import darknet as dn
 import pdb
 
-<<<<<<< HEAD
-net = dn.load_net("cfg/yolo-tag.cfg", "yolo-tag_final.weights", 0)
-meta = dn.load_meta("cfg/openimages.data")
-pdb.set_trace()
-rr = dn.detect(net, meta, 'data/dog.jpg')
-print rr
-pdb.set_trace()
-=======
 dn.set_gpu(0)
 net = dn.load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
 meta = dn.load_meta("cfg/coco.data")
 r = dn.detect(net, meta, "data/dog.jpg")
 print r
->>>>>>> 16686cec576580489ab3c7c78183e6efeafae780
 
 # And then down here you could detect a lot more images like:
 rr = dn.detect(net, meta, "data/eagle.jpg")

python/darknet.py

@@ -13,7 +13,10 @@ def sample(probs):
     return len(probs)-1
 
 def c_array(ctype, values):
-    return (ctype * len(values))(*values)
+    arr = (ctype * len(values))()
+    arr[:] = values
+    return arr
+
 
 class BOX(Structure):
     _fields_ = [("x", c_float),
@@ -31,7 +34,7 @@ class METADATA(Structure):
     _fields_ = [("classes", c_int),
                 ("names", POINTER(c_char_p))]
 
-    
+
 
 #lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
 lib = CDLL("libdarknet.so", RTLD_GLOBAL)
@@ -101,6 +104,18 @@ predict_image.restype = POINTER(c_float)
 network_detect = lib.network_detect
 network_detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]
 
+import numpy
+def array_to_image(arr):
+    arr = arr.copy()
+    arr = arr.transpose(2,0,1)
+    c = arr.shape[0]
+    h = arr.shape[1]
+    w = arr.shape[2]
+    arr = (arr.astype(numpy.float32)/255.0).flatten()
+    data = c_array(c_float, arr)
+    im = IMAGE(w,h,c,data)
+    return im
+
 def classify(net, meta, im):
     out = predict_image(net, im)
     res = []
@@ -110,7 +125,10 @@ def classify(net, meta, im):
     return res
 
 def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
-    im = load_image(image, 0, 0)
+    if type(image) == numpy.ndarray:
+        im = array_to_image(image)
+    else:
+        im = load_image(image, 0, 0)
     boxes = make_boxes(net)
     probs = make_probs(net)
     num =   num_boxes(net)
@@ -121,10 +139,12 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
             if probs[j][i] > 0:
                 res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)))
     res = sorted(res, key=lambda x: -x[1])
-    free_image(im)
+
+    if type(image) != numpy.ndarray:
+        free_image(im)
     free_ptrs(cast(probs, POINTER(c_void_p)), num)
     return res
-    
+
 if __name__ == "__main__":
     #net = load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0)
     #im = load_image("data/wolf.jpg", 0, 0)
@@ -135,5 +155,5 @@ if __name__ == "__main__":
     meta = load_meta("cfg/coco.data")
     r = detect(net, meta, "data/dog.jpg")
     print(r)
-    
+