def application_openFile_(self, app, f):
     extension = os.path.splitext(f)[1].lower()
     if extension not in ('.nno', '.json', '.cadnano'):
         print "Could not open file %s (bad extension %s)"%(f, extension)
         return
     doc = decode(file(str(f)).read())
     DocumentController(doc, str(f))
     return None
Exemplo n.º 2
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 def application_openFile_(self, app, f):
     if f == "main.py":  # ignore
         return
     extension = os.path.splitext(f)[1].lower()
     if extension not in ('.nno', '.json', '.cadnano'):
         print "Could not open file %s (bad extension %s)" % (f, extension)
         return
     doc = decode(file(str(f)).read())
     DocumentController(doc, str(f))
     return None
 def application_openFile_(self, app, f):
     if f == "main.py":  # ignore
         return
     extension = os.path.splitext(f)[1].lower()
     if extension not in (".nno", ".json", ".cadnano"):
         print "Could not open file %s (bad extension %s)" % (f, extension)
         return
     doc = decode(file(str(f)).read())
     DocumentController(doc, str(f))
     return None
Exemplo n.º 4
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 def newDocument(self, isFirstNewDoc=False):
     from controllers.documentcontroller import DocumentController
     defaultFile = environ.get('CADNANO_DEFAULT_DOCUMENT', None)
     if defaultFile and isFirstNewDoc:
         defaultFile = path.expanduser(defaultFile)
         defaultFile = path.expandvars(defaultFile)
         from model.decoder import decode
         doc = decode(file(defaultFile).read())
         print "Loaded default document: %s" % doc
         dc = DocumentController(doc, defaultFile)
     else:
         dc = DocumentController()  # DocumentController is responsible
                                    # for adding itself to
                                    # app.documentControllers
     return dc.document()
Exemplo n.º 5
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 def openFile(self, selected):
     if isinstance(selected, QStringList) or isinstance(selected, list):
         fname = selected[0]
     else:
         fname = selected
     if not fname or os.path.isdir(fname):
         return False
     fname = str(fname)
     doc = decode(file(fname).read())
     doc.finalizeImport()  # updates staple highlighting
     DocumentController(doc, fname)
     if self.filesavedialog != None:
         self.filesavedialog.filesSelected.disconnect(self.openFile)
         # manual garbage collection to prevent hang (in osx)
         del self.filesavedialog
Exemplo n.º 6
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 def newDocument(self, isFirstNewDoc=False):
     from controllers.documentcontroller import DocumentController
     defaultFile = environ.get('CADNANO_DEFAULT_DOCUMENT', None)
     if defaultFile and isFirstNewDoc:
         defaultFile = path.expanduser(defaultFile)
         defaultFile = path.expandvars(defaultFile)
         from model.decoder import decode
         doc = decode(file(defaultFile).read())
         print "Loaded default document: %s" % doc
         dc = DocumentController(doc, defaultFile)
     else:
         docCtrlrCount = len(self.documentControllers)
         if docCtrlrCount == 0:  # first dc
             # dc adds itself to app.documentControllers
             dc = DocumentController()
         elif docCtrlrCount == 1:  # dc already exists
             dc = list(self.documentControllers)[0]
             dc.newDocument()  # tell it to make a new doucment
     return dc.document()
Exemplo n.º 7
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    def test():

        print("testing...")

        Config.batch_size = 1

        Config.model_path = "testmodel_last.pt"
        print("mAP files output path: " + Config.mAP_path)

        model_path = Config.model_path

        print("model: ", model_path)
        print("conf: ", Config.conf_thresh)
        print("iou thresh:  ", Config.conf_thresh)

        if Config.dataset == "VOC":
            print("dataset: ", Config.voc_dataset_dir)
            dataset = DatasetJNN_VOC(Config.voc_dataset_dir,
                                     mode="test",
                                     year="2007",
                                     is_training=False)
        elif Config.dataset == "coco":
            print("dataset: ", Config.coco_dataset_dir)
            dataset = DatasetJNN_COCO(Config.coco_dataset_dir,
                                      is_training=False)
        else:
            print("dataset: ", Config.testing_dir)
            folder_dataset = dset.ImageFolder(root=Config.testing_dir)
            dataset = DatasetJNN(imageFolderDataset=folder_dataset,
                                 is_training=False)

        dataloader = DataLoader(dataset,
                                shuffle=False,
                                num_workers=0,
                                batch_size=1)

        model = DarkJNN()

        checkpoint = torch.load(model_path)
        model.load_state_dict(checkpoint['model'])

        model.cuda()
        model.eval()

        with torch.no_grad():

            for i, data in enumerate(dataloader, 0):

                if (i % 1000 == 0):
                    print(str(i) + "/" + str(len(dataset)))  # progress

                img0, img1, targets, label, im_infos = data
                img0, img1, targets = Variable(img0).cuda(), Variable(
                    img1).cuda(), targets.cuda()

                model_output = model(img0, img1, targets)

                im_info = {
                    'width': im_infos[0].item(),
                    'height': im_infos[1].item()
                }
                output = [item[0].data for item in model_output]

                detections = decode(output,
                                    im_info,
                                    conf_threshold=Config.conf_thresh,
                                    nms_threshold=Config.nms_thresh)

                if len(detections) > 0:

                    # mAP files
                    pair_id = im_infos[2][0].split(
                        '.')[0] + "_" + im_infos[3][0].split('.')[0]

                    detection_str = ""
                    gt_str = ""

                    f = open(
                        Config.mAP_path + "groundtruths/" + pair_id + ".txt",
                        "a+")
                    for box_idx in range(len(targets)):

                        gt_str += label[0] + " " \
                                  + str(targets[0][box_idx][0].item()) + " " \
                                  + str(targets[0][box_idx][1].item()) + " " \
                                  + str(targets[0][box_idx][2].item()) + " " \
                                  + str(targets[0][box_idx][3].item()) + "\n"

                    f.seek(0)
                    if not (gt_str in f.readlines()):
                        f.write(gt_str)
                    f.close()

                    f = open(
                        Config.mAP_path + "detections/" + pair_id + ".txt",
                        "a+")
                    for detection in detections:
                        detection_str += label[0] + " " \
                                      + str(detection[4].item()) + " "\
                                      + str(detection[0].item()) + " "\
                                      + str(detection[1].item()) + " "\
                                      + str(detection[2].item()) + " "\
                                      + str(detection[3].item()) + "\n"

                    f.seek(0)
                    if not (detection_str in f.readlines()):
                        f.write(detection_str)
                    f.close()
Exemplo n.º 8
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    def test_one_COCO():
        """ Tests a a pair of images """

        print("testing one image...")

        Config.model_path = "/home/mmv/Documents/2.projects/JNN_detection/trained_models/dJNN_COCOsplit2/testmodel_last_split2.pt"
        model_path = Config.model_path

        model = DarkJNN()

        checkpoint = torch.load(model_path)
        model.load_state_dict(checkpoint['model'])
        model.cuda()
        model.eval()

        coco_dataset = dset.CocoDetection(
            Config.coco_dataset_dir,
            Config.coco_dataset_dir + "annotations/instances_val2017.json")

        # (3m1, 3m6), (rbc1, rbc43), hp(33971473, 70609284), blizzard(1, 6), gen_electric(7, 31), warner(10, 18)
        # goodyear(13, 20), airhawk(12, 1), gap(34, 36), levis(14, 30)
        q_name = "000000024144"
        t_name = "000000306700"
        q_im = Image.open("/home/mmv/Documents/3.datasets/coco/val2017/" +
                          q_name + ".jpg")
        t_im = Image.open("/home/mmv/Documents/3.datasets/coco/val2017/" +
                          t_name + ".jpg")

        # find image id and (first) annotation
        for id in coco_dataset.coco.imgs:
            if coco_dataset.coco.imgs[id]['file_name'] == q_name + ".jpg":
                break
        for ann_id in coco_dataset.coco.anns:
            if coco_dataset.coco.anns[ann_id]['image_id'] == id:
                print(coco_dataset.coco.anns[ann_id])
                break
        qbox = coco_dataset.coco.anns[ann_id]['bbox']
        qbox = [qbox[0], qbox[1], qbox[0] + qbox[2], qbox[1] + qbox[3]]
        q_im = q_im.crop((qbox[0], qbox[1], qbox[2], qbox[3]))

        w, h = t_im.size[0], t_im.size[1]
        im_infos = (w, h, q_name, t_name)

        qcv_im = np.array(q_im)
        qcv_im = qcv_im[:, :, ::-1].copy()
        cv_im = np.array(t_im)
        cv_im = cv_im[:, :, ::-1].copy()

        q_im = q_im.resize((Config.imq_w, Config.imq_h))
        t_im = t_im.resize((Config.im_w, Config.im_h))

        # To float tensors
        q_im = torch.from_numpy(np.array(q_im)).float() / 255
        t_im = torch.from_numpy(np.array(t_im)).float() / 255
        img0 = q_im.permute(2, 0, 1)
        img1 = t_im.permute(2, 0, 1)
        img0 = torch.unsqueeze(img0, 0)
        img1 = torch.unsqueeze(img1, 0)

        with torch.no_grad():
            #
            img0, img1 = Variable(img0).cuda(), Variable(img1).cuda()

            model_output = model(img0, img1, [])

            im_info = {'width': im_infos[0], 'height': im_infos[1]}
            output = [item[0].data for item in model_output]

            detections = decode(output,
                                im_info,
                                conf_threshold=Config.conf_thresh,
                                nms_threshold=Config.nms_thresh)

            if len(detections) > 0:

                for detection in detections:
                    start_pt = (int(detection[0].item()),
                                int(detection[1].item()))
                    end_pt = (int(detection[2].item()),
                              int(detection[3].item()))
                    image = cv2.rectangle(cv_im, start_pt, end_pt, (0, 255, 0),
                                          3)
                    print(start_pt, end_pt)

                cv2.imshow("q", qcv_im)
                cv2.imshow("res", image)
                cv2.waitKey()
            else:
                print("No detctions found")
Exemplo n.º 9
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    def test_one_OL():
        """ Tests a a pair of images """

        print("testing one image...")

        Config.model_path = "/home/mmv/Documents/2.projects/JNN_detection/trained_models/dJNN_COCOsplit2/testmodel_last_split2.pt"

        model_path = Config.model_path

        model = DarkJNN()

        checkpoint = torch.load(model_path)
        model.load_state_dict(checkpoint['model'])

        model.cuda()
        model.eval()

        # (3m1, 3m6), (rbc1, rbc43), hp(33971473, 70609284), blizzard(1, 6), gen_electric(7, 31), warner(10, 18)
        # goodyear(13, 20), airhawk(12, 1), gap(34, 36), levis(14, 30)
        q_name = "000000008629"
        t_name = "000000209530"
        q_im = Image.open("/home/mmv/Documents/3.datasets/coco/val2017/" +
                          q_name + ".jpg")
        t_im = Image.open("/home/mmv/Documents/3.datasets/coco/val2017/" +
                          t_name + ".jpg")

        w, h = t_im.size[0], t_im.size[1]
        im_infos = (w, h, q_name, t_name)

        cv_im = np.array(t_im)
        cv_im = cv_im[:, :, ::-1].copy()

        q_im = q_im.resize((Config.imq_w, Config.imq_h))
        t_im = t_im.resize((Config.im_w, Config.im_h))

        # To float tensors
        q_im = torch.from_numpy(np.array(q_im)).float() / 255
        t_im = torch.from_numpy(np.array(t_im)).float() / 255
        img0 = q_im.permute(2, 0, 1)
        img1 = t_im.permute(2, 0, 1)
        img0 = torch.unsqueeze(img0, 0)
        img1 = torch.unsqueeze(img1, 0)

        with torch.no_grad():
            #
            img0, img1 = Variable(img0).cuda(), Variable(img1).cuda()

            model_output = model(img0, img1, [])

            im_info = {'width': im_infos[0], 'height': im_infos[1]}
            output = [item[0].data for item in model_output]

            detections = decode(output,
                                im_info,
                                conf_threshold=Config.conf_thresh,
                                nms_threshold=Config.nms_thresh)

            if len(detections) > 0:

                for detection in detections:
                    start_pt = (int(detection[0].item()),
                                int(detection[1].item()))
                    end_pt = (int(detection[2].item()),
                              int(detection[3].item()))
                    image = cv2.rectangle(cv_im, start_pt, end_pt, (0, 255, 0),
                                          3)
                    print(start_pt, end_pt)

                cv2.imshow("res", image)
                cv2.waitKey()