import os import cv2 import numpy as np import sys import pickle from optparse import OptionParser import time from keras_frcnn import config import keras_frcnn.resnet as nn from keras import backend as K from keras.layers import Input from keras.models import Model from keras_frcnn import roi_helpers sys.setrecursionlimit(40000) parser = OptionParser() parser.add_option("-p", "--path", dest="test_path", help="Path to test data.") parser.add_option("-n", "--num_rois", dest="num_rois", help="Number of ROIs per iteration. Higher means more memory use.", default=32) parser.add_option("--config_filename", dest="config_filename", help= "Location to read the metadata related to the training (generated when training).", default="config.pickle") (options, args) = parser.parse_args() if not options.test_path: # if filename is not given parser.error('Error: path to test data must be specified. Pass --path to command line') config_output_filename = options.config_filename with open(config_output_filename, 'r') as f_in: C = pickle.load(f_in) # turn off any data augmentation at test time C.use_horizontal_flips = False C.use_vertical_flips = False C.rot_90 = False img_path = options.test_path def format_img(img, C): img_min_side = float(C.im_size) (height,width,_) = img.shape if width <= height: f = img_min_side/width new_height = int(f * height) new_width = int(img_min_side) else: f = img_min_side/height new_width = int(f * width) new_height = int(img_min_side) img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC) img = img[:, :, (2, 1, 0)] img = img.astype(np.float32) img[:, :, 0] -= C.img_channel_mean[0] img[:, :, 1] -= C.img_channel_mean[1] img[:, :, 2] -= C.img_channel_mean[2] img /= C.img_scaling_factor img = np.transpose(img, (2, 0, 1)) img = np.expand_dims(img, axis=0) return img class_mapping = C.class_mapping if 'bg' not in class_mapping: class_mapping['bg'] = len(class_mapping) class_mapping = {v: k for k, v in class_mapping.iteritems()} print(class_mapping) class_to_color = {class_mapping[v]: np.random.randint(0, 255, 3) for v in class_mapping} C.num_rois = int(options.num_rois) if K.image_dim_ordering() == 'th': input_shape_img = (3, None, None) input_shape_features = (1024, None, None) else: input_shape_img = (None, None, 3) input_shape_features = (None, None, 1024) img_input = Input(shape=input_shape_img) roi_input = Input(shape=(C.num_rois, 4)) feature_map_input = Input(shape=input_shape_features) # define the base network (resnet here, can be VGG, Inception, etc) shared_layers = nn.nn_base(img_input, trainable=True) # define the RPN, built on the base layers num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios) rpn_layers = nn.rpn(shared_layers, num_anchors) classifier = nn.classifier(feature_map_input, roi_input, C.num_rois, nb_classes=len(class_mapping), trainable=True) model_rpn = Model(img_input, rpn_layers) model_classifier_only = Model([feature_map_input, roi_input], classifier) model_classifier = Model([feature_map_input, roi_input], classifier) model_rpn.load_weights(C.model_path, by_name=True) model_classifier.load_weights(C.model_path, by_name=True) model_rpn.compile(optimizer='sgd', loss='mse') model_classifier.compile(optimizer='sgd', loss='mse') all_imgs = [] classes = {} bbox_threshold = 0.8 visualise = True for idx, img_name in enumerate(sorted(os.listdir(img_path))): if not img_name.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')): continue print(img_name) st = time.time() filepath = os.path.join(img_path,img_name) img = cv2.imread(filepath) X = format_img(img, C) img_scaled = np.transpose(X.copy()[0, (2, 1, 0), :, :], (1, 2, 0)).copy() img_scaled[:, :, 0] += 123.68 img_scaled[:, :, 1] += 116.779 img_scaled[:, :, 2] += 103.939 img_scaled = img_scaled.astype(np.uint8) if K.image_dim_ordering() == 'tf': X = np.transpose(X, (0, 2, 3, 1)) # get the feature maps and output from the RPN [Y1, Y2, F] = model_rpn.predict(X) R = roi_helpers.rpn_to_roi(Y1, Y2, C, K.image_dim_ordering(), overlap_thresh=0.7) # convert from (x1,y1,x2,y2) to (x,y,w,h) R[:, 2] -= R[:, 0] R[:, 3] -= R[:, 1] # apply the spatial pyramid pooling to the proposed regions bboxes = {} probs = {} for jk in range(R.shape[0]//C.num_rois + 1): ROIs = np.expand_dims(R[C.num_rois*jk:C.num_rois*(jk+1), :], axis=0) if ROIs.shape[1] == 0: break if jk == R.shape[0]//C.num_rois: #pad R curr_shape = ROIs.shape target_shape = (curr_shape[0],C.num_rois,curr_shape[2]) ROIs_padded = np.zeros(target_shape).astype(ROIs.dtype) ROIs_padded[:, :curr_shape[1], :] = ROIs ROIs_padded[0, curr_shape[1]:, :] = ROIs[0, 0, :] ROIs = ROIs_padded [P_cls, P_regr] = model_classifier_only.predict([F, ROIs]) for ii in range(P_cls.shape[1]): if np.max(P_cls[0, ii, :]) < bbox_threshold or np.argmax(P_cls[0, ii, :]) == (P_cls.shape[2] - 1): continue cls_name = class_mapping[np.argmax(P_cls[0, ii, :])] if cls_name not in bboxes: bboxes[cls_name] = [] probs[cls_name] = [] (x, y, w, h) = ROIs[0, ii, :] cls_num = np.argmax(P_cls[0, ii, :]) try: (tx, ty, tw, th) = P_regr[0, ii, 4*cls_num:4*(cls_num+1)] tx /= C.classifier_regr_std[0] ty /= C.classifier_regr_std[1] tw /= C.classifier_regr_std[2] th /= C.classifier_regr_std[3] x, y, w, h = roi_helpers.apply_regr(x, y, w, h, tx, ty, tw, th) except: pass bboxes[cls_name].append([16*x, 16*y, 16*(x+w), 16*(y+h)]) probs[cls_name].append(np.max(P_cls[0, ii, :])) all_dets = [] for key in bboxes: bbox = np.array(bboxes[key]) new_boxes, new_probs = roi_helpers.non_max_suppression_fast(bbox, np.array(probs[key]), overlap_thresh=0.5) for jk in range(new_boxes.shape[0]): (x1, y1, x2, y2) = new_boxes[jk,:] cv2.rectangle(img_scaled,(x1, y1), (x2, y2), class_to_color[key],2) textLabel = '{}: {}'.format(key,int(100*new_probs[jk])) all_dets.append((key,100*new_probs[jk])) (retval,baseLine) = cv2.getTextSize(textLabel,cv2.FONT_HERSHEY_COMPLEX,1,1) textOrg = (x1, y1-0) cv2.rectangle(img_scaled, (textOrg[0] - 5, textOrg[1]+baseLine - 5), (textOrg[0]+retval[0] + 5, textOrg[1]-retval[1] - 5), (0, 0, 0), 2) cv2.rectangle(img_scaled, (textOrg[0] - 5,textOrg[1]+baseLine - 5), (textOrg[0]+retval[0] + 5, textOrg[1]-retval[1] - 5), (255, 255, 255), -1) cv2.putText(img_scaled, textLabel, textOrg, cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 0), 1) print('Elapsed time = {}'.format(time.time() - st)) cv2.imshow('img', img_scaled) cv2.waitKey(0) #cv2.imwrite('./imgs/{}.png'.format(idx),img_scaled) print(all_dets)