| | |
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| | | #lib = CDLL("darknet.so", RTLD_GLOBAL) |
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| | | hasGPU = True |
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| | | if os.name == "nt": |
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| | | winGPUdll = "yolo_cpp_dll.dll" |
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| | | winNoGPUdll = "yolo_cpp_dll_nogpu.dll" |
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| | | cwd = os.path.dirname(__file__) |
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| | | os.environ['PATH'] = cwd + ';' + os.environ['PATH'] |
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| | | winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll") |
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| | | winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll") |
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| | | envKeys = list() |
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| | | for k, v in os.environ.items(): |
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| | | envKeys.append(k) |
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| | | try: |
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| | | tmp = os.environ["CUDA_HOME"] |
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| | | try: |
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| | | tmp = os.environ["FORCE_CPU"].lower() |
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| | | if tmp in ["1", "true", "yes", "on"]: |
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| | |
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| | | if int(os.environ['CUDA_VISIBLE_DEVICES']) < 0: |
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| | | raise ValueError("ForceCPU") |
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| | | try: |
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| | | # Check a global |
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| | | global DARKNET_FORCE_CPU |
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| | | if DARKNET_FORCE_CPU: |
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| | | raise ValueError("ForceCPU") |
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| | | except NameError: |
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| | | pass |
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| | | #print(os.environ.keys()) |
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| | | #print("FORCE_CPU flag undefined, proceeding with GPU") |
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| | | # print(os.environ.keys()) |
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| | | # print("FORCE_CPU flag undefined, proceeding with GPU") |
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| | | if not os.path.exists(winGPUdll): |
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| | | raise ValueError("NoDLL") |
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| | | lib = CDLL(winGPUdll, RTLD_GLOBAL) |
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| | | except (KeyError, ValueError): |
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| | | print("Notice: GPU-free mode") |
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| | | hasGPU = False |
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| | | if os.path.exists(winNoGPUdll): |
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| | | lib = CDLL(winNoGPUdll, RTLD_GLOBAL) |
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| | | print("Notice: CPU-only mode") |
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| | | else: |
|---|
| | | # Try the other way, in case no_gpu was |
|---|
| | | # compile but not renamed |
|---|
| | | lib = CDLL(winGPUdll, RTLD_GLOBAL) |
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| | | 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) |
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| | | lib.network_width.argtypes = [c_void_p] |
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| | | lib.network_width.restype = c_int |
|---|
| | | lib.network_height.argtypes = [c_void_p] |
|---|
| | |
|---|
| | | load_net.argtypes = [c_char_p, c_char_p, c_int] |
|---|
| | | load_net.restype = c_void_p |
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| | | |
|---|
| | | load_net_custom = lib.load_network_custom |
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| | | 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] |
|---|
| | | |
|---|
| | |
|---|
| | | 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 = [] |
|---|
| | |
|---|
| | | """ |
|---|
| | | #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") |
|---|
| | |
|---|
| | | 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") |
|---|
| | |
|---|
| | | 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: |
|---|
| | |
|---|
| | | 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: |
|---|
