test_frcnn_video.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 time



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")
parser.add_option("--network", dest="network", help="Base network to use. Supports vgg or resnet50.", default='vgg')

(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')

with open("submit.csv",mode='a',encoding="UTF-8") as f:
    f.write("filename,X1,Y1,X2,Y2,X3,Y3,X4,Y4,type\n")

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 == 'xception':
    import keras_frcnn.xception as nn
elif C.network == 'inception_resnet_v2':
    import keras_frcnn.inception_resnet_v2 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

video_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(255, 0, 3) for v in class_mapping}
class_to_color = {'hat': (255,255,0),'person':(0,255,0)}

# {0: 'Mobile phone', 1: 'Person', 2: 'Car', 3: 'bg'}
C.num_rois = int(options.num_rois)

if C.network == 'resnet50':
    num_features = 1024
elif C.network == 'xception':
    num_features = 1024
elif C.network == 'inception_resnet_v2':
    num_features = 1024
elif C.network == 'vgg':
    num_features = 512

if K.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')


# 模型加载完毕，开始识别了

all_imgs = []

classes = {}

bbox_threshold = 0.8

visualise = True

cap = cv2.VideoCapture(video_path)
fourcc = cv2.VideoWriter_fourcc(*'MP42')
out = cv2.VideoWriter("out.mp4",fourcc,24.0,(704,576))

idx = 0
while cap.isOpened():
    starts = time.clock()
    ret,frame = cap.read()
    if ret == True:
        idx += 1
        img = frame
        print(idx)
        # if idx%5 == 0:

        # img = frame

        
        X, ratio = format_img(img, C)

        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([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=0.2) #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)

                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)
                # 读出结果
                # with open("submit.csv",mode='a',encoding="UTF-8") as f:
                #     f.write(img_name+","+str(real_x1)+","+str(real_y1)+","+str(real_x2)+","+str(real_y1)+","+str(real_x2)+","+str(real_y2)+","+str(real_x1)+","+str(real_y2)+","+str(key)+"\n")
                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)

                cv2.rectangle(img, (textOrg[0], textOrg[1]), (textOrg[0]+retval[0] + 15, textOrg[1]-retval[1] - 5), (0, 255, 0), 1)
                cv2.rectangle(img, (textOrg[0], textOrg[1]), (textOrg[0]+retval[0] + 15, textOrg[1]-retval[1] - 5), (0, 255, 0,0.2), -1)
                cv2.putText(img, textLabel+"%",(real_x1, real_y1-3), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 0), 2)
            
        ends = time.clock()
        fps_ = str(round(1000.0 / ((ends - starts) * 1000),0))
        text_ = "Model: Faster R-CNN(VGG),GPU:GTX 1080Ti 11G.FPS:"+fps_ 
        text1_ = "Trainer: MDK-XuJing"

        cv2.putText(img,text_, (5, 40), cv2.FONT_HERSHEY_DUPLEX, 1, (250,235,215), 1)
        cv2.putText(img,text1_, (5, 80), cv2.FONT_HERSHEY_DUPLEX, 1, (250,235,215), 1)

        # cv2.putText(img,text_, (5, 40), cv2.FONT_HERSHEY_DUPLEX, 1, (250,235,215), 2)

        # frame = cv2.flip(img,0)

        out.write(img)
        cv2.imshow("frame",img)

        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

    else:
        break

#release everything
cap.release()
out.release()
cv2.destroyAllWindows()


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