test_frcnn.py

from __future__ import division
import os
import cv2
import numpy as np
import sys
import pickle
from optparse import OptionParser
import time
from keras_frcnn import config
from keras import backend as K
from keras.layers import Input
from keras.models import Model
from keras_frcnn import roi_helpers
import pandas
########################### I created 5 list to contain all bounding box coordinates and image name
img_name_list = []
x1_list = []
x2_list = []
y1_list = []
y2_list = []

sys.setrecursionlimit(40000)

parser = OptionParser()

parser.add_option("-p", "--path", dest="test_path", help="Path to test data.")
parser.add_option("-n", "--num_rois", type="int", 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")
parser.add_option("--network", dest="network", help="Base network to use. Supports vgg or resnet50.", default='resnet50')

(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, 'rb') as f_in:
        C = pickle.load(f_in)

if C.network == 'resnet50':
        import keras_frcnn.resnet as nn
elif C.network == 'vgg':
        import keras_frcnn.vgg as nn

# 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_size(img, C):
        """ formats the image size based on config """
        img_min_side = float(C.im_size)
        (height,width,_) = img.shape
                
        if width <= height:
                ratio = img_min_side/width
                new_height = int(ratio * height)
                new_width = int(img_min_side)
        else:
                ratio = img_min_side/height
                new_width = int(ratio * width)
                new_height = int(img_min_side)
        img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
        return img, ratio       

def format_img_channels(img, C):
        """ formats the image channels based on config """
        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

def format_img(img, C):
        """ formats an image for model prediction based on config """
        img, ratio = format_img_size(img, C)
        img = format_img_channels(img, C)
        return img, ratio

# Method to transform the coordinates of the bounding box to its original size
def get_real_coordinates(ratio, x1, y1, x2, y2):

        real_x1 = int(round(x1 // ratio))
        real_y1 = int(round(y1 // ratio))
        real_x2 = int(round(x2 // ratio))
        real_y2 = int(round(y2 // ratio))

        return (real_x1, real_y1, real_x2 ,real_y2)

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.items()}
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 C.network == 'resnet50':
        num_features = 1024
elif C.network == 'vgg':
        num_features = 512

if K.common.image_dim_ordering() == 'th':
        input_shape_img = (3, None, None)
        input_shape_features = (num_features, None, None)
else:
        input_shape_img = (None, None, 3)
        input_shape_features = (None, None, num_features)


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)

print('Loading weights from {}'.format(C.model_path))
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 = 0.9

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, ratio = format_img(img, C)

        if K.common.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.common.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([C.rpn_stride*x, C.rpn_stride*y, C.rpn_stride*(x+w), C.rpn_stride*(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,:]
                        (real_x1, real_y1, real_x2, real_y2) = get_real_coordinates(ratio,x1,y1,x2,y2)
#Here I created a list of 4 entries that contains the real coordinates
                        img_name_list.append(img_name)
                        x1_list.append(real_x1)
                        x2_list.append(real_x2)
                        y1_list.append(real_y1)
                        y2_list.append(real_y2)
                        
                        cv2.rectangle(img,(real_x1, real_y1), (real_x2, real_y2), (int(class_to_color[key][0]), int(class_to_color[key][1]), int(class_to_color[key][2])),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,fontScale = 1, thickness=1)
                        textOrg = (real_x1, real_y1-0)

                        cv2.rectangle(img, (textOrg[0] - 5, textOrg[1]+baseLine - 5), (textOrg[0]+retval[0] + 5, textOrg[1]-retval[1] - 5), (0, 0, 0), 2)
                        cv2.rectangle(img, (textOrg[0] - 5,textOrg[1]+baseLine - 5), (textOrg[0]+retval[0] + 5, textOrg[1]-retval[1] - 5), (255, 255, 255), -1)
                        cv2.putText(img, textLabel, textOrg, cv2.FONT_HERSHEY_DUPLEX, fontScale = 1, color = (0, 0, 0), thickness = 1)

        print('Elapsed time = {}'.format(time.time() - st))
        print(all_dets)
        
        #cv2.imshow('img', img)
        #cv2.waitKey(0)
        cv2.imwrite('./results_imgs/{}.png'.format(img_name),img)


        

################# saving the results(bounding boxes) in a csv 

df = pandas.DataFrame(data={"img_name": img_name_list, "x1": x1_list, "y1": y1_list, "x2": x2_list, "y2": y2_list})
df.to_csv("bounding_box_coordinates_ans.csv", sep=',',index=False)