test_frcnn_count.py

import os
import cv2
import numpy as np
import sys
import pickle
from optparse import OptionParser
import time
import itertools
import operator
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
import datetime
import re
import subprocess

parser = OptionParser()
parser.add_option("-i", "--input_file", dest="input_file", help="Path to input video file.")
parser.add_option("-o", "--output_file", dest="output_file", help="Path to output video file.", default="output.mp4")
parser.add_option("-d", "--input_dir", dest="input_dir", help="Path to input working directory.", default="/home/ubuntu/input")
parser.add_option("-u", "--output_dir", dest="output_dir", help="Path to output working directory.", default="/home/ubuntu/output")
parser.add_option("-r", "--frame_rate", dest="frame_rate", help="Frame rate of the output video.", default=25.0)

(options, args) = parser.parse_args()
if not options.input_file:   # if filename is not given
	parser.error('Error: path to video input_file must be specified. Pass --input-file to command line')

input_video_file = options.input_file
output_video_file = options.output_file
img_path = os.path.join(options.input_dir, '')
output_path = os.path.join(options.output_dir, '')
num_rois = 32
frame_rate = float(options.frame_rate)

def cleanup():
	print("cleaning up...")
	os.popen('rm -f ' + img_path + '*')
	os.popen('rm -f ' + output_path + '*')

def get_file_names(search_path):
	for (dirpath, _, filenames) in os.walk(search_path):
		for filename in filenames:
			yield filename#os.path.join(dirpath, filename)

def convert_to_images():
	cam = cv2.VideoCapture(input_video_file)
	counter = 0
	while True:
		flag, frame = cam.read()
		if flag:
			cv2.imwrite(os.path.join(img_path, str(counter) + '.jpg'),frame)
			counter = counter + 1
		else:
			break
		if cv2.waitKey(1) == 27:
			break
			# press esc to quit
	cv2.destroyAllWindows()

def save_to_video():
	list_files = sorted(get_file_names(output_path), key=lambda var:[int(x) if x.isdigit() else x for x in re.findall(r'[^0-9]|[0-9]+', var)])
	img0 = cv2.imread(os.path.join(output_path,'0.jpg'))
	height , width , layers =  img0.shape

	# fourcc = cv2.cv.CV_FOURCC(*'mp4v')
	fourcc = cv2.VideoWriter_fourcc(*'mp4v')
	#fourcc = cv2.cv.CV_FOURCC(*'XVID')
	videowriter = cv2.VideoWriter(output_video_file,fourcc, frame_rate, (width,height))
	for f in list_files:
		print("saving..." + f)
		img = cv2.imread(os.path.join(output_path, f))
		videowriter.write(img)
	videowriter.release()
	cv2.destroyAllWindows()

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

def accumulate(l):
    it = itertools.groupby(l, operator.itemgetter(0))
    for key, subiter in it:
		yield key, sum(item[1] for item in subiter)

def main():
	cleanup()
	sys.setrecursionlimit(40000)
	config_output_filename = 'config.pickle'

	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
	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 = 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

	print("Converting video to images..")
	convert_to_images()
	print("anotating...")

	list_files = sorted(get_file_names(img_path), key=lambda var:[int(x) if x.isdigit() else x for x in re.findall(r'[^0-9]|[0-9]+', var)])
	for img_name in list_files:
		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 = []
		all_objects = []

		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]))
				all_objects.append((key, 1))

				(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))
		height, width, channels = img_scaled.shape
		cv2.rectangle(img_scaled, (0,0), (width, 30), (0, 0, 0), -1)
		cv2.putText(img_scaled, "Obj count: " + str(list(accumulate(all_objects))), (5, 19), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 255, 255), 1)
		cv2.imwrite(os.path.join(output_path, img_name), img_scaled)
		print(all_dets)
	print("saving to video..")
	save_to_video()

if __name__ == '__main__':
	main()