Minor fix

waltercassiano · Feb 7, 2019 · 9bb7455 · 9bb7455
1 parent c999f53
commit 9bb7455
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Showing 5 changed files with 50 additions and 231 deletions.
diff --git a/build/darknet/x64/cfg/imagenet1k.data b/build/darknet/x64/cfg/imagenet1k.data
@@ -1,7 +1,8 @@
 classes=1000
-train  = /data/imagenet/imagenet1k.train.list
-valid  = /data/imagenet/imagenet1k.valid.list
-backup = /home/pjreddie/backup/
+train  = data/imagenet1k.train.list
+#train  = data/inet.val.list
+valid  = data/inet.val.list
+backup = backup
 labels = data/imagenet.labels.list
 names  = data/imagenet.shortnames.list
 top=5

diff --git a/darknet.py b/darknet.py
@@ -125,6 +125,15 @@ class METADATA(Structure):
 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)
@@ -223,14 +232,20 @@ 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)
     #custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR)
     #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

diff --git a/darknet_video.py b/darknet_video.py
@@ -5,222 +5,7 @@
 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("./libdarknet.so", 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 @@ def cvDrawBoxes(detections, img):
 
 
 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))

diff --git a/src/classifier.c b/src/classifier.c
@@ -125,6 +125,8 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     load_thread = load_data(args);
 
     int iter_save = get_current_batch(net);
+    int iter_save_last = get_current_batch(net);
+
     while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
         time=clock();
 
@@ -155,13 +157,23 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
         draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, -1, 0, dont_show, mjpeg_port);
 #endif  // OPENCV
 
-        if (i >= (iter_save + 100)) {
+        if (i >= (iter_save + 1000)) {
             iter_save = i;
 #ifdef GPU
             if (ngpus != 1) sync_nets(nets, ngpus, 0);
 #endif
             char buff[256];
-            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i);
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+
+        if (i >= (iter_save_last + 100)) {
+            iter_save_last = i;
+#ifdef GPU
+            if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+            char buff[256];
+            sprintf(buff, "%s/%s_last.weights", backup_directory, base, i);
             save_weights(net, buff);
         }
         free_data(train);

diff --git a/src/cuda.h b/src/cuda.h
@@ -16,7 +16,7 @@ extern int gpu_index;
 #include "curand.h"
 #include "cublas_v2.h"
 #include "cuda_runtime_api.h"
-#include "driver_types.h"
+//#include "driver_types.h"
 
 #ifdef CUDNN
 #include "cudnn.h"