def process(): session = getUserInSession() ext = request.form.get('ext') #dropping table with the same number as the current session has if it somehow exists engine = create_engine(db_url) sql = text('DROP TABLE IF EXISTS table_' + session + ';') engine.execute(sql) #calling tracker from cmd that may be not correct (perhaps having the entire tracker code as an imported module would be better) dir_path = os.path.dirname(os.path.realpath(__file__)) os.chdir(dir_path) os.system( "conda activate project & python object_tracker.py --video static/" + session + "." + ext + " --output data/video/output.mp4") #calling text detector start.main(project_path + "static/" + session + "/", project_path + "static/" + session + "/") #calling text recogniser, 2nd parameter helps us to get db name etc demo.main(project_path + "static/" + session + "/", len(project_path) + 7, db_url) #reading table and writing data to html df = read_table(session, engine) write_html('templates/' + session + '.html', df) df.to_excel('static/' + session + '/results.xlsx') return render_template(session + ".html")
def run_demo(picked_datasets_index): # First clear some folders remove_folder_content(demo_input_path) remove_folder_content(demo_output_path) # Now pick some random images from dataset random_files = [] folder_path = datasets[picked_datasets_index] for _ in range(files_to_pick): random_files.append(folder_path + "/" + random.choice(os.listdir(folder_path))) for random_file in random_files: shutil.copy2(random_file, demo_input_path) # Now generate names for them generate_demo_file_names.main() import demo demo.main() # Now save results with renamed name to results folder results_folder = '../results' N = 10 file_index = 0 for file in os.listdir(demo_output_path): # 0 - gt # 1 - black and white # 2 - out file_extension = file.split('.')[-1] if file_index % 3 == 0: dataset_name = datasets[picked_datasets_index].split('/')[-1] random_name = ''.join( random.choices(string.ascii_uppercase + string.digits, k=N)) gt = f"{random_name}_gt.{file_extension}" bandw = f"{random_name}_black_and_white.{file_extension}" out = f"{random_name}_out.{file_extension}" shutil.copy2(f"{demo_output_path}/{file}", f"{results_folder}/{dataset_name}/{gt}") elif file_index % 3 == 1: # Black and white shutil.copy2(f"{demo_output_path}/{file}", f"{results_folder}/{dataset_name}/{bandw}") else: # out shutil.copy2(f"{demo_output_path}/{file}", f"{results_folder}/{dataset_name}/{out}") file_index += 1
def models2df(model_names, opt, save_dir=None): """ Applies provided models to provided stimuli and outputs network activation data as a DataFrame for further processing. """ activation_data = [] columns = [ "StimulusName", "ImageMode", "ModelName", "Region", "Activations" ] for img_mode in opt.feeding_modes: stim_source, mode = str.split(img_mode, "-") img_paths = [] if stim_source == "original": img_dir = opt.stimuli_dir img_paths = sorted(os.listdir(img_dir)) elif stim_source == "rendered": if os.path.exists(opt.stimuli_dir + '-trunk-centric'): img_dir = opt.stimuli_dir + "-trunk-centric" else: img_dir = opt.stimuli_dir + "-trunk-centric-uncentered" img_paths = sorted(os.listdir(img_dir)) img_paths = [path for path in img_paths if "-" not in path] else: print( "Source in image mode {} is not implemented.".format(img_mode)) # loop through all stimuli/images in this mode make_path('tmp') for img_path in img_paths: stimulus_name = str.split(os.path.basename(img_path), ".")[0] # loop through image modes (e.g. "raw" or "cropped") if mode == "raw": img_path = os.path.join(img_dir, img_path) elif mode == "cropped": img_path = os.path.join(img_dir + '-cropped', img_path) # elif mode == "attended": # img = attend_crop_img(img, attend_size=opt.attend_size) else: print("Mode in image mode {} is not implemented.".format(mode)) # loop through models for model_name in model_names: print('\nComputing output for model {} and image {}.\n'.format( model_name, stimulus_name)) output = main(img_path, model_type=model_name) # loop through labels for model_region in opt.model_regions[model_name]: out = output[model_region].flatten() activation_data.append([ stimulus_name, img_mode, model_name, model_region, out ]) return pd.DataFrame(activation_data, columns=columns)
def main(file_list = ["PID026.JPG"]): from time import clock predicted_symbols, classes, size = Dectection.main(file_list, DEBUG) ret_list = [] t_start = clock() for item in zip(predicted_symbols, file_list): keys = item[0].keys() img = Image.open(item[1]) coordinate_list = [] #draw = ImageDraw.Draw(img)eog if 0 in keys: for bound in item[0][0]: #Valve symbol_dict = {} coordinate = bound[0] symbol_dict['coord'] = coordinate symbol_dict['type'] = 0 img_cv = PIL2CV(img.crop(coordinate)) #Feature_matching(img_cv) symbol_dict['detail'] = Specific_Valve(img_cv) if symbol_dict['detail'] is None: symbol_dict['detail'] = "None" coordinate_list.append(symbol_dict) if 2 in keys: for bound in item[0][2]: symbol_dict = {} coordinate = bound[0] symbol_dict['coord'] = coordinate symbol_dict['type'] = 2 symbol_dict['detail'] = "None" coordinate_list.append(symbol_dict) if 1 in keys: # for bound in item[0][1]: symbol_dict = {} coordinate = bound[0] symbol_dict['coord'] = coordinate symbol_dict['type'] = 1 for i in range(9, 3, -1): temp = [] image_cv = convert_PIL_cv(img,coordinate,i) OCR_str = pytesseract.image_to_string(image_cv, config=tess_CONFIG) if len(OCR_str) != 1: OCR_str = OCR_str.replace(' ','').split("\n") temp = _OCR_string(OCR_str[0]) if len(OCR_str) != 1: temp += _OCR_digit(OCR_str[1]) if len(temp) !=0: break symbol_dict['detail'] = temp coordinate_list.append(symbol_dict) ret_list.append(coordinate_list) print("Execution Time : {}".format(clock() - t_start)) return ret_list, classes
def main(): """Starts the application and creates a virtualenv in case of failure.""" _dir, _script = osp.split(osp.abspath(__file__)) _venv = osp.join(_dir, 'venv') _bin = osp.join(_venv, 'Scripts' if sys.platform == 'win32' else 'bin') _py = osp.join(_bin, 'python') try: # We try to import our application module import demo except ImportError: # Our application fails to import dependencies if not osp.exists(_venv): # Creates a virtual environment and installs # dependencies using pip venv.create(_venv, with_pip=True) pip.main(['install', '-r', 'requirements.txt', '--prefix', _venv]) # Execute the script with the venv python executable subprocess.run([_py, _script]) else: demo.main()
def stopRecording(self): self.out.release() self.capture = not self.capture self.btn_snapshot["text"] = "Record" self.class_label["text"] = "Loading label" # self.playSound(self.class_label["text"]) print("Should extract") extract_files.extract_file() print("extraction done") prediction = demo.main() self.playSound(prediction) self.class_label["text"] = prediction
def main(_): trainig_folder = setup_logging_and_exp_folder() FLAGS.cuda = FLAGS.cuda and torch.cuda.is_available() logging.info('Use Cuda: {}'.format(FLAGS.cuda)) logging.info('Current git SHA: ' + CURR_VERSION) # save options fpath = os.path.join(trainig_folder, 'flagfile') with open(fpath, 'w') as f: f.write(FLAGS.flags_into_string()) if FLAGS.mode == 'IL': IL.run(training_folder=trainig_folder) elif FLAGS.mode == 'demo': demo.main() elif FLAGS.mode == 'compile': train_compile.main(training_folder=trainig_folder) elif FLAGS.mode == 'taco': train_taco.main(training_folder=trainig_folder) else: logging.fatal('Improper Mode {}'.format(FLAGS.mode)) logging.info('Done')
def score(): #print("请求已开始") #theimg1 = str(json.loads(request.values.get("theimg1"))) #theimg2=str(json.loads(request.values.get("theimg2"))) theimg1 = str(json.loads(request.values.get("temp1"))) theimg2 = str(json.loads(request.values.get("temp2"))) img1 = base64.b64decode(theimg1) img2 = base64.b64decode(theimg2) f1 = open( 'E:/meizhuanghouduan/PSGAN/assets/images/non-makeup/xfsy_0106.png', 'wb') f1.write(img1) f1.close() f2 = open('E:/meizhuanghouduan/PSGAN/assets/images/makeup/makeup.png', 'wb') f2.write(img2) f2.close() demo.main() f = open('E:/meizhuanghouduan/transferred_image.png', 'rb') img = base64.b64encode(f.read()).decode('utf-8') f.close() #res='返回成功' print("文件已加载") return json.dumps(img)
def __init__(self, *args, **kw): info = main() info_string = "" for line in info: info_string += str(line) info_string += "\n" super(HelloFrame, self).__init__(*args, **kw) pnl = wx.Panel(self) vbox = wx.BoxSizer(wx.VERTICAL) st = wx.StaticText(pnl, label=info_string, style=wx.ALIGN_LEFT) font = wx.Font(10, wx.ROMAN, wx.NORMAL, wx.NORMAL) st.SetFont(font) vbox.Add(st, flag=wx.ALL, border=15) pnl.SetSizer(vbox) self.Centre() self.SetSize((800, 800)) self.makeMenuBar() self.CreateStatusBar() self.SetStatusText("Welcome to wxPython!")
if __name__ == "__main__": # solver_init() cfg_from_list(['CONST.BATCH_SIZE', 1]) for checkpoint_dir, pre_dir, model_name in zip(Checkpoint_dir, Pre_dir, Model_names): # print(checkpoint_dir, pre_dir) weights = os.path.join(checkpoint_dir, 'checkpoint.tar') set_weights(weights) set_model_name(model_name) # if pre_dir == './prediction/ResidualGRUNet_No_Regularition/': # cfg_from_list(['TEST.VOXEL_THRESH', [0.4]]) for index in range(1, 11): checkpoint_path = os.path.join( checkpoint_dir, 'checkpoint.%d.tar' % (index * 2000)) # print(save_path) symlink_path = os.path.join(checkpoint_dir, 'checkpoint.tar') if os.path.lexists(symlink_path): os.remove(symlink_path) os.symlink("%s" % os.path.abspath(checkpoint_path), symlink_path) # set_pred_file_name name = os.path.join(pre_dir, 'prediction.%d.obj' % (index * 2000)) set_pred_file_name(name) main() # Make a symlink to the latest network params # os.symlink("%s" % os.path.abspath(checkpoint_path), symlink_path)
import demo if __name__ == "__main__": demo.main()
def script_call(): return redirect(demo.main(app.config['SRC']))
mask_input[np.where(np.logical_and(mask_input == -1, np.logical_or(result >= 2.5, target >= 2.5)))] = 0 tracker2 = IndexTracker(ax2, mask_input) fig.canvas.mpl_connect('scroll_event', tracker2.onscroll) ax3 = fig.add_subplot(223) ax3.set_title('Prediction') tracker3 = IndexTracker(ax3, result) fig.canvas.mpl_connect('scroll_event', tracker3.onscroll) if target is not None: ax4 = fig.add_subplot(224) ax4.set_title('Target') tracker4 = IndexTracker(ax4, target) fig.canvas.mpl_connect('scroll_event', tracker4.onscroll) plt.tight_layout() plt.show() if __name__ == '__main__': parser = argparse.ArgumentParser(description='Slicer') parser.add_argument('--model', type=str, default='../models/checkpoint.pth', help='trained model path') parser.add_argument('--input', type=str, default='../datasets/sample/overfit.h5', help='uses file as input') args = parser.parse_args() main(args.model, args.input, n_samples=1, cb=plot_slicer)
# #print("back type = ",type(back)) #print("image type = ",type(np.asarray(image))) #print("masksDL type = ",type(255*mask_sel.astype(np.uint8))) # # #print("back shape = ", back.shape) #print("image shape = ",np.asarray(image).shape) #print("masksDL shape = ",255*mask_sel.astype(np.uint8).shape) #back_align = alignImages(back, np.asarray(image), cv2.cvtColor(255*mask_sel.astype(np.uint8),cv2.COLOR_GRAY2RGB)) #bg_removed = remove_bg(np.asarray(image), back_align,cv2.cvtColor(255*mask_sel.astype(np.uint8),cv2.COLOR_GRAY2RGB)) #config = flags.FLAGS #config(sys.argv) # Using pre-trained model, change this to use your own. #config.load_path = src.config.PRETRAINED_MODEL # #config.batch_size = 1 #cv2.imwrite(dir_name.replace('img','back'),remove_bg) main(bg_removed, args.height, None) #name= dir_name.replace('img','masksDL') #cv2.imwrite(name,(255*mask_sel).astype(np.uint8)) #cv2.imwrite(dir_name.replace('img','back'),back_align) #str_msg='\nDone: ' + dir_name #print(str_msg)
def test_main(): main()
def modify_doc(doc): demo.main(doc)
import demo demo.main()
def test_demo(args: List[str]): demo.main(demo.parser.parse_args(args))
def test_runs(): demo.main()
# #print("back type = ",type(back)) #print("image type = ",type(np.asarray(image))) #print("masksDL type = ",type(255*mask_sel.astype(np.uint8))) # # #print("back shape = ", back.shape) #print("image shape = ",np.asarray(image).shape) #print("masksDL shape = ",255*mask_sel.astype(np.uint8).shape) #back_align = alignImages(back, np.asarray(image), cv2.cvtColor(255*mask_sel.astype(np.uint8),cv2.COLOR_GRAY2RGB)) #bg_removed = remove_bg(np.asarray(image), back_align,cv2.cvtColor(255*mask_sel.astype(np.uint8),cv2.COLOR_GRAY2RGB)) #config = flags.FLAGS #config(sys.argv) # Using pre-trained model, change this to use your own. #config.load_path = src.config.PRETRAINED_MODEL # #config.batch_size = 1 #cv2.imwrite(dir_name.replace('img','back'),remove_bg) main(bg_removed, None) #name= dir_name.replace('img','masksDL') #cv2.imwrite(name,(255*mask_sel).astype(np.uint8)) #cv2.imwrite(dir_name.replace('img','back'),back_align) #str_msg='\nDone: ' + dir_name #print(str_msg)
from demo import main main(['walWalkNext', 'NK', 'rLS', 'fit', '0', '1', '10', '2', '0']) #main(['walWalkNext', 'NK', 'rLS', 'fit', '0', '1', '1000', '2', '0']) #main(['walWalkNext', 'NK', 'rLS', 'fit', '0', '1', '1000', '4', '0']) #main(['walWalkNext', 'NK', 'rLS', 'fit', '0', '1', '1000', '8', '0'])