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| | | import math |
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| | | import random |
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| | | import os |
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| | | import cv2 |
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| | | import numpy as np |
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| | | |
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| | | def sample(probs): |
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| | | s = sum(probs) |
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| | |
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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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| | | 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.") |
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| | | else: |
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| | | lib = CDLL("./libdarknet.so", RTLD_GLOBAL) |
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| | | 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 |
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| | | lib.network_height.argtypes = [c_void_p] |
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| | |
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| | | load_net.argtypes = [c_char_p, c_char_p, c_int] |
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| | | load_net.restype = c_void_p |
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| | | |
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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] |
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| | | load_net_custom.restype = c_void_p |
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| | | |
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| | | do_nms_obj = lib.do_nms_obj |
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| | | do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] |
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| | | |
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| | |
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| | | predict_image.argtypes = [c_void_p, IMAGE] |
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| | | predict_image.restype = POINTER(c_float) |
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| | | |
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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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| | | |
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| | | def classify(net, meta, im): |
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| | | out = predict_image(net, im) |
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| | | res = [] |
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| | |
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| | | Performs the meat of the detection |
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| | | """ |
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| | | #pylint: disable= C0321 |
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| | | im = load_image(image, 0, 0) |
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| | | if isinstance(image, np.ndarray): |
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| | | im = array_to_image(image)[0] |
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| | | else: |
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| | | im = load_image(image, 0, 0) |
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| | | #import cv2 |
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| | | #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) |
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| | | #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) |
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| | | #custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR) |
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| | | #import scipy.misc |
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| | | #custom_image = scipy.misc.imread(image) |
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| | | #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) |
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| | | if debug: print("Loaded image") |
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| | | num = c_int(0) |
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| | | if debug: print("Assigned num") |
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| | |
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| | | if debug: print("Assigned pnum") |
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| | | predict_image(net, im) |
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| | | if debug: print("did prediction") |
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| | | dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 1) |
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| | | #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, 0) # OpenCV |
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| | | dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0) |
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| | | if debug: print("Got dets") |
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| | | num = pnum[0] |
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| | | if debug: print("got zeroth index of pnum") |
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| | |
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| | | if not os.path.exists(metaPath): |
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| | | raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`") |
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| | | if netMain is None: |
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| | | netMain = load_net(configPath.encode("ascii"), weightPath.encode("ascii"), 0) |
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| | | netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 |
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| | | if metaMain is None: |
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| | | metaMain = load_meta(metaPath.encode("ascii")) |
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| | | if altNames is None: |
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| | |
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| | | if not os.path.exists(imagePath): |
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| | | raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`") |
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| | | # Do the detection |
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| | | detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh) |
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| | | #detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image) |
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| | | #detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh) |
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| | | detections = detect(netMain, metaMain, cv2.imread(imagePath), thresh, debug=True) |
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| | | if showImage: |
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| | | try: |
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| | | from skimage import io, draw |
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| | |
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| | | print("Unable to show image: "+str(e)) |
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| | | return detections |
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| | | |
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| | | |
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| | | def capture(thresh=.25, hier_thresh=.5, nms=.45, configPath="./cfg/yolov3.cfg", weightPath="yolov3.weights", |
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| | | metaPath="./data/coco.data", showImage=True, makeImageOnly=False, initOnly=False): |
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| | | global metaMain, netMain, altNames # pylint: disable=W0603 |
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| | | netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 |
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| | | metaMain = load_meta(metaPath.encode("ascii")) |
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| | | |
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| | | num = c_int(0) |
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| | | pnum = pointer(num) |
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| | | num = pnum[0] |
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| | | |
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| | | capture = cv2.VideoCapture('../data/test1.mp4') |
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| | | print(capture.get(cv2.CAP_PROP_FPS)) |
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| | | |
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| | | capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1024) |
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| | | capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 768) |
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| | | |
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| | | while True: |
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| | | ret, frame = capture.read() |
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| | | detections = detect(netMain, metaMain, frame, thresh, debug=True) |
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| | | ''' |
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| | | im, arr = array_to_image(frame) |
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| | | predict_image(netMain, im) |
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| | | dets = get_network_boxes(netMain, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 1) |
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| | | if nms: |
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| | | do_nms_sort(dets, num, metaMain.classes, nms) |
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| | | res = [] |
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| | | for j in range(num): |
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| | | for i in range(metaMain.classes): |
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| | | if dets[j].prob[i] > 0: |
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| | | b = dets[j].bbox |
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| | | nameTag = metaMain.names[i] |
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| | | res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) |
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| | | ''' |
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| | | for detection in detections: |
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| | | label = detection[0] |
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| | | confidence = detection[1] |
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| | | pstring = label + ": " + str(np.rint(100 * confidence)) + "%" |
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| | | imcaption.append(pstring) |
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| | | print(pstring) |
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| | | bounds = detection[2] |
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| | | shape = image.shape |
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| | | # x = shape[1] |
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| | | # xExtent = int(x * bounds[2] / 100) |
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| | | # y = shape[0] |
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| | | # yExtent = int(y * bounds[3] / 100) |
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| | | yExtent = int(bounds[3]) |
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| | | xEntent = int(bounds[2]) |
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| | | # Coordinates are around the center |
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| | | xCoord = int(bounds[0] - bounds[2] / 2) |
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| | | yCoord = int(bounds[1] - bounds[3] / 2) |
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| | | boundingBox = [ |
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| | | [xCoord, yCoord], |
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| | | [xCoord, yCoord + yExtent], |
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| | | [xCoord + xEntent, yCoord + yExtent], |
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| | | [xCoord + xEntent, yCoord] |
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| | | ] |
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| | | # Wiggle it around to make a 3px border |
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| | | rr, cc = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] for x in boundingBox], shape=shape) |
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| | | rr2, cc2 = draw.polygon_perimeter([x[1] + 1 for x in boundingBox], [x[0] for x in boundingBox], shape=shape) |
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| | | rr3, cc3 = draw.polygon_perimeter([x[1] - 1 for x in boundingBox], [x[0] for x in boundingBox], shape=shape) |
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| | | rr4, cc4 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] + 1 for x in boundingBox], shape=shape) |
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| | | rr5, cc5 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] - 1 for x in boundingBox], shape=shape) |
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| | | boxColor = (int(255 * (1 - (confidence ** 2))), int(255 * (confidence ** 2)), 0) |
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| | | draw.set_color(image, (rr, cc), boxColor, alpha=0.8) |
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| | | draw.set_color(image, (rr2, cc2), boxColor, alpha=0.8) |
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| | | draw.set_color(image, (rr3, cc3), boxColor, alpha=0.8) |
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| | | draw.set_color(image, (rr4, cc4), boxColor, alpha=0.8) |
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| | | draw.set_color(image, (rr5, cc5), boxColor, alpha=0.8) |
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| | | print(res) |
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| | | cv2.imshow('frame', frame) |
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| | | if cv2.waitKey(1) & 0xFF == ord('q'): |
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| | | break |
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| | | |
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| | | capture.release() |
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| | | cv2.destroyAllWindows() |
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| | | |
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| | | |
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| | | if __name__ == "__main__": |
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| | | print(performDetect()) |
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| | | performDetect(imagePath='data/scream.jpg') |
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| | | #performDetect(imagePath="../data/test1.jpg", thresh=0.25, configPath="./cfg/tiny_yolo.cfg", |
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| | | # weightPath="./weights/second_general/tiny_yolo_17000.weights", |
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| | | # metaPath="./data/obj.data", showImage=True, makeImageOnly=False, initOnly=False) |
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| | | #print(performDetect(showImage=False)) |
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| | | #capture() |
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