test.py

from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import cv2
import time
import numpy as np
import pickle
import os
import matplotlib.pyplot as plt

from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model

from thundernet.utils.np_opr import rpn_to_roi, non_max_suppression_fast, apply_regr
from thundernet.layers.snet import snet_146
from thundernet.layers.detector import rpn_layer, classifier_layer


os.environ['CUDA_VISIBLE_DEVICES'] = '2'


# ----------------------------- Path_config ------------------------------ #
base_path = '/data2/intern/TF-Keras-ThunderNet/'
#test_path = '/data2/intern/TF-Keras-ThunderNet/data/train_list.txt'
test_base_path = '/data2/intern/TF-Keras-ThunderNet/data/test_cube'
config_output_filename = os.path.join(base_path, 'model/model_snet_config.pickle')

# ------------------------------- Config ----------------------------------- #
with open(config_output_filename, 'rb') 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


def format_img_size(img, C):
    """ formats the image size based on config """
    (height, width, _) = img.shape
    print(height, width)
    ratio_h = height / 320
    ratio_w = width / 320
    img = cv2.resize(img, (320, 320), interpolation=cv2.INTER_CUBIC)
    return img, ratio_h, ratio_w    


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_h, ratio_w = format_img_size(img, C)
    img = format_img_channels(img, C)
    return img, ratio_h, ratio_w


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

    real_x1 = int(round(x1 * ratio_w))
    real_y1 = int(round(y1 * ratio_h))
    real_x2 = int(round(x2 * ratio_w))
    real_y2 = int(round(y2 * ratio_h))

    return (real_x1, real_y1, real_x2 ,real_y2)


num_features = 245

input_shape_img = (320, 320, 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 (VGG here, can be Resnet50, Inception, etc)
shared_layers = snet_146(img_input)

# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn_layers = rpn_layer(shared_layers, num_anchors)

classifier = classifier_layer(feature_map_input, roi_input, C.num_rois, nb_classes=len(C.class_mapping))

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)

# --------------------------------------------------------#
#                  Print class mapping                    #
# --------------------------------------------------------#
# Switch key value for class mapping
class_mapping = C.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}

test_imgs = os.listdir(test_base_path)

imgs_path = []
for i in range(485):
    idx = np.random.randint(len(test_imgs))
    imgs_path.append(test_imgs[idx])

all_imgs = []

classes = {}

# -------------------------------------------------------- #
#                     Start Testing                     #
# -------------------------------------------------------- #
# If the box classification value is less than this, we ignore this box
bbox_threshold = 0.0

for idx, img_name in enumerate(imgs_path):
    if not img_name.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')):
        continue
    print(img_name)
    st = time.time()
    filepath = os.path.join(test_base_path, img_name)

    img = cv2.imread(filepath)

    X, ratio_h, ratio_w = format_img(img, C)

    X = np.transpose(X, (0, 2, 3, 1))

    # get output layer Y1, Y2 from the RPN and the feature maps F
    # Y1: y_rpn_cls
    # Y2: y_rpn_regr
    #     for layer in model_rpn.layers:
    #         print(layer.get_config(), layer.get_weights())
    [Y1, Y2, F] = model_rpn.predict(X)
    #     print(Y1.shape)
    # Get bboxes by applying NMS
    # R.shape = (300, 4)
    R = rpn_to_roi(Y1, Y2, C, 'tf', overlap_thresh=0.7)
    print(len(R))
    # 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.predict([F, ROIs])
        #         print([P_cls, P_regr])
        # Calculate bboxes coordinates on resized image
        for ii in range(P_cls.shape[1]):
            # Ignore 'bg' class
            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 = 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])
        print(key)
        new_boxes, new_probs = non_max_suppression_fast(bbox, np.array(probs[key]), overlap_thresh=0.5)
        for jk in range(new_boxes.shape[0]):
            #(y1, x1, y2, x2) = new_boxes[jk, :]
            (x1, y1, x2, y2) = new_boxes[jk, :]
            print((x1, y1, x2, y2))
            # Calculate real coordinates on original image
            (real_x1, real_y1, real_x2, real_y2) = get_real_coordinates(ratio_h, ratio_w, x1, y1, x2, 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])), 4)

            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 = (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), 1)
            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, 1, (0, 0, 0), 1)
    cv2.imwrite('./data/res/{}'.format(img_name), img)
    print('Elapsed time = {}'.format(time.time() - st))
    #print(all_dets)
    '''  
    plt.figure(figsize=(10, 10))
    plt.grid()
    plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    plt.show()
    '''