From b8e6e80c6d411d05a9e09f1e3676eb9a7f3ea0e8 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Fri, 03 Aug 2018 11:35:03 +0000
Subject: [PATCH] Added spatial Yolo v3 yolov3-spp.cfg
---
darknet.py | 48 +++++++++++++++++++++++++++++++++++++-----------
1 files changed, 37 insertions(+), 11 deletions(-)
diff --git a/darknet.py b/darknet.py
index 2f77bad..4bca5f5 100644
--- a/darknet.py
+++ b/darknet.py
@@ -78,13 +78,14 @@
#lib = CDLL("darknet.so", RTLD_GLOBAL)
hasGPU = True
if os.name == "nt":
- winGPUdll = "yolo_cpp_dll.dll"
- winNoGPUdll = "yolo_cpp_dll_nogpu.dll"
+ cwd = os.path.dirname(__file__)
+ os.environ['PATH'] = cwd + ';' + os.environ['PATH']
+ winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll")
+ winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll")
envKeys = list()
for k, v in os.environ.items():
envKeys.append(k)
try:
- tmp = os.environ["CUDA_HOME"]
try:
tmp = os.environ["FORCE_CPU"].lower()
if tmp in ["1", "true", "yes", "on"]:
@@ -97,28 +98,28 @@
if int(os.environ['CUDA_VISIBLE_DEVICES']) < 0:
raise ValueError("ForceCPU")
try:
- # Check a global
global DARKNET_FORCE_CPU
if DARKNET_FORCE_CPU:
raise ValueError("ForceCPU")
except NameError:
pass
- print(os.environ.keys())
- print("FORCE_CPU flag undefined, proceeding with GPU")
+ # print(os.environ.keys())
+ # print("FORCE_CPU flag undefined, proceeding with GPU")
if not os.path.exists(winGPUdll):
raise ValueError("NoDLL")
lib = CDLL(winGPUdll, RTLD_GLOBAL)
except (KeyError, ValueError):
- print("Notice: GPU-free mode")
hasGPU = False
if os.path.exists(winNoGPUdll):
lib = CDLL(winNoGPUdll, RTLD_GLOBAL)
+ print("Notice: CPU-only mode")
else:
# Try the other way, in case no_gpu was
# compile but not renamed
lib = CDLL(winGPUdll, RTLD_GLOBAL)
+ print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.")
else:
- lib = CDLL("./libdarknet.so", RTLD_GLOBAL)
+ lib = CDLL("./darknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
@@ -160,6 +161,10 @@
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]
@@ -188,6 +193,18 @@
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)
+def array_to_image(arr):
+ import numpy as np
+ # need to return old values to avoid python freeing memory
+ 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 = []
@@ -206,6 +223,13 @@
"""
#pylint: disable= C0321
im = load_image(image, 0, 0)
+ #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")
@@ -213,7 +237,8 @@
if debug: print("Assigned pnum")
predict_image(net, im)
if debug: print("did prediction")
- dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 1)
+ #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.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")
@@ -253,7 +278,7 @@
metaMain = None
altNames = None
-def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./data/coco.data", showImage= True, makeImageOnly = False, initOnly= False):
+def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./data/coco.data", showImage= True, makeImageOnly = False, initOnly= False):
"""
Convenience function to handle the detection and returns of objects.
@@ -310,7 +335,7 @@
if not os.path.exists(metaPath):
raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`")
if netMain is None:
- netMain = load_net(configPath.encode("ascii"), weightPath.encode("ascii"), 0)
+ netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1
if metaMain is None:
metaMain = load_meta(metaPath.encode("ascii"))
if altNames is None:
@@ -340,6 +365,7 @@
if not os.path.exists(imagePath):
raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`")
# Do the detection
+ #detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image)
detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh)
if showImage:
try:
--
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