def main(mode, tiny, iou_threshold, confidence_threshold, path):
class_names, n_classes = load_class_names()
if tiny:
model = YOLOv3_tiny(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
else:
model = YOLOv3(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
inputs = tf.placeholder(tf.float32, [1, *model.input_size, 3])
detections = model(inputs)
saver = tf.train.Saver(tf.global_variables(scope=model.scope))
with tf.Session() as sess:
saver.restore(sess, './weights/model-tiny.ckpt' if tiny else './weights/model.ckpt')
if mode == 'image':
image = load_image(path, input_size=model.input_size)
result = sess.run(detections, feed_dict={inputs: image})
draw_boxes(path, boxes_dict=result[0], class_names=class_names, input_size=model.input_size)
return
elif mode == 'video':
cv2.namedWindow("Detections")
video = cv2.VideoCapture(path)
fourcc = int(video.get(cv2.CAP_PROP_FOURCC))
fps = video.get(cv2.CAP_PROP_FPS)
frame_size = (int(video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter('./detections/video_output.mp4', fourcc, fps, frame_size)
print("Video being saved at \"" + './detections/video_output.mp4' + "\"")
print("Press 'q' to quit")
while True:
retval, frame = video.read()
if not retval:
break
resized_frame = cv2.resize(frame, dsize=tuple((x) for x in model.input_size[::-1]), interpolation=cv2.INTER_NEAREST)
result = sess.run(detections, feed_dict={inputs: [resized_frame]})
draw_boxes_frame(frame, frame_size, result, class_names, model.input_size)
cv2.imshow("Detections", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
out.write(frame)
cv2.destroyAllWindows()
video.release()
return
elif mode == 'webcam':
while True:
frame, addr = receive()
frame_size = (frame.shape[1], frame.shape[0])
resized_frame = cv2.resize(frame, dsize=tuple((x) for x in model.input_size[::-1]), interpolation=cv2.INTER_NEAREST)
result = sess.run(detections, feed_dict={inputs: [resized_frame]})
draw_boxes_frame(frame, frame_size, result, class_names, model.input_size)
jpgstring = cv2.imencode(".jpg", frame)
packet = jpgstring[1].tostring()
udpServSock.sendto(packet, addr)
return
def run_model(self, frame):
if frame is None:
print("No image! Wait a Seconds...!")
return frame
else:
#####TF MODEL#####
image = Image.fromarray(frame)
img_resized = np.array(image.resize(size=tuple(self.SIZE)),
dtype=np.float32)
img_resized = img_resized / 255
boxes, scores, labels = self.sess.run(self.output_tensors,
feed_dict={
self.input_tensor:
np.expand_dims(
img_resized, axis=0)
})
image, bbox_list = utils.draw_boxes(image,
boxes,
scores,
labels,
self.classes,
self.SIZE,
show=False,
target=self.TARGET)
self.result = np.asarray(image)
self.calculate_pitch_yaw_vertical(bbox_list)
return self.result
def save_result(self, path, PIL_image=None, boxes=None, scores=None, labels=None):
PIL_image = PIL_image or self.last_PIL_image
boxes = boxes or self.last_boxes
scores = scores or self.last_scores
labels = labels or self.last_labels
PIL_image_result = utils.draw_boxes(PIL_image, boxes, scores, labels, self.classes, [self.IMAGE_H, self.IMAGE_W], show=False)
PIL_image_result.save(path)
def visualize_result(self, PIL_image=None, boxes=None, scores=None, labels=None):
PIL_image = PIL_image or self.last_PIL_image
boxes = boxes or self.last_boxes
scores = scores or self.last_scores
labels = labels or self.last_labels
PIL_image_result = utils.draw_boxes(PIL_image, boxes, scores, labels, self.classes, [self.IMAGE_H, self.IMAGE_W])
np_image = np.array(PIL_image_result, dtype=np.int32)
# plt.figure(figsize=(10, 10))
plt.imshow(np_image)
plt.show()
def visualize_result(self, PIL_image=None, boxes=None, scores=None, labels=None):
PIL_image = PIL_image or self.last_PIL_image
boxes = boxes or self.last_boxes
scores = scores or self.last_scores
labels = labels or self.last_labels
PIL_image_result = utils.draw_boxes(PIL_image, boxes, scores, labels, self.classes, [self.IMAGE_H, self.IMAGE_W])
np_image = np.array(PIL_image_result, dtype=np.int32)
# plt.figure(figsize=(10, 10))
plt.imshow(np_image)
plt.show()
# if __name__ == '__main__':
# yolo = YoloTest()
# boxes, scores, labels = yolo.predict_from_path("./data/demo_data/car1.jpg")
#
# print(boxes, scores, labels)
# yolo.visualize_result()
dtype=np.float32)
img_resized = img_resized / 255.
prev_time = time.time()
boxes, scores, probs = sess.run(
output_tensors,
feed_dict={input_tensor: np.expand_dims(img_resized, axis=0)})
boxes, scores, labels, _ = utils.cpu_nms(boxes,
scores,
probs,
num_classes,
score_thresh=0.4,
iou_thresh=0.5)
image = utils.draw_boxes(image,
boxes,
scores,
labels,
classes, (IMAGE_H, IMAGE_W),
show=False)
curr_time = time.time()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "time: %.2f ms" % (1000 * exec_time)
cv2.putText(result,
text=info,
org=(50, 70),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(255, 0, 0),
thickness=2)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
import numpy as np
import matplotlib.pyplot as plt
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
# 在推断模式中，将使用running_mean和running_variance作为bn层的mean和variance
tf.keras.backend.set_learning_phase(False)
# 加载模型
model = tf.saved_model.load('saved_model/overfit_model-1000e')
# 80个类别的名称
class_name = read_class_name('./model_data/coco_classes_chinese.txt')
# 图片名称
file = 'car2.png'
# 图片放缩成608,608,像素值归一化
image, image_data, image_shape = preprocess_image(img_path='train-set/origin/' + file)
# 进行目标检测算法
res_class, res_score, res_boxes = detect(image_data, model)
if len(res_boxes) > 0:
# image_shape 为原图大小高宽格式，
# 还原成相对于原图的位置
res_boxes = res_boxes * np.tile(list(image_shape), 2)
# 把框画在图片上
draw_boxes(image, res_score, res_boxes, res_class, class_name, generate_colors(class_name))
plt.imshow(image)
plt.show()
image.save('train-set/detected/'+file, quality=100)
else:
print('未检测到任何目标')
#
# Editor : VIM
# File name : f**k.py
# Author : YunYang1994
# Created date: 2019-01-23 10:21:50
# Description :
#
#================================================================
import numpy as np
import tensorflow as tf
from PIL import Image
from core import utils
IMAGE_H, IMAGE_W = 416, 416
classes = utils.read_coco_names('./data/raccoon.names')
num_classes = len(classes)
image_path = "./raccoon_dataset/images/raccoon-182.jpg" # 181,
img = Image.open(image_path)
img_resized = np.array(img.resize(size=(IMAGE_W, IMAGE_H)), dtype=np.float32)
img_resized = img_resized / 255.
cpu_nms_graph = tf.Graph()
input_tensor, output_tensors = utils.read_pb_return_tensors(cpu_nms_graph, "./checkpoint/yolov3_cpu_nms.pb",
["Placeholder:0", "concat_9:0", "mul_6:0"])
with tf.Session(graph=cpu_nms_graph) as sess:
boxes, scores = sess.run(output_tensors, feed_dict={input_tensor: np.expand_dims(img_resized, axis=0)})
boxes, scores, labels = utils.cpu_nms(boxes, scores, num_classes, score_thresh=0.3, iou_thresh=0.5)
image = utils.draw_boxes(img, boxes, scores, labels, classes, [IMAGE_H, IMAGE_W], show=True)
saver = tf.train.Saver()
saver.restore(sess, "data/SVHN/checkpoint5/yolov3.ckpt-4000")
acc = 0
STEPS = 13068
for step in range(STEPS):
run_items = sess.run([y_pred, y_true], feed_dict={is_training: False})
if step == 5:
acc = utils.compute_accuracy(run_items[0], run_items[1])
y_pred_data = run_items[0]
pred_boxes = y_pred_data[0][0]
pred_confs = y_pred_data[1][0]
pred_probs = y_pred_data[2][0]
pred_boxes, pred_scores, pred_labels = utils.cpu_nms(pred_boxes, pred_confs * pred_probs, NUM_CLASSES,
score_thresh=0.3, iou_thresh=0.5)
img = Image.open("data/SVHN/PaddingTest/" + str(step + 1) + ".png")
image = utils.draw_boxes(img, pred_boxes, pred_scores, pred_labels, CLASSES, [IMAGE_H, IMAGE_W], show=False)
# if acc == 1:
# image.save("data/SVHN/RightRecognition/" + str(step + 1) + ".png")
# else:
# image.save("data/SVHN/WrongRecognition/" + str(step + 1) + ".png")
print("=> STEP %10d [VALID]:\tacc:%7.4f" % (step+1, acc))
acc += acc * BATCH_SIZE
acc /= 13068
print("精度为%7.4f" % acc)
dtype=np.float32)
img_resized = img_resized / 255.
prev_time = time.time()
boxes, scores = sess.run(
output_tensors,
feed_dict={input_tensor: np.expand_dims(img_resized, axis=0)})
boxes, scores, labels = utils.cpu_nms(boxes,
scores,
num_classes,
score_thresh=0.4,
iou_thresh=0.5)
image = utils.draw_boxes(boxes,
scores,
labels,
image,
classes,
SIZE,
show=False)
curr_time = time.time()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "time: %.2f ms" % (1000 * exec_time)
cv2.putText(result,
text=info,
org=(50, 70),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(255, 0, 0),
thickness=2)
def main(mode, tiny, iou_threshold, confidence_threshold, path):
rectangleColor = (0, 255, 0)
frameCounter = 0
currentCarID = 0
fps = 0
carTracker = {}
carNumbers = {}
carLocation1 = {}
carLocation2 = {}
speed = [None] * 1000
class_names, n_classes = load_class_names()
if tiny:
model = YOLOv3_tiny(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
else:
model = YOLOv3(n_classes=n_classes,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
inputs = tf.placeholder(tf.float32, [1, *model.input_size, 3])
detections = model(inputs)
saver = tf.train.Saver(tf.global_variables(scope=model.scope))
with tf.Session() as sess:
saver.restore(
sess, './weights/model-tiny.ckpt' if tiny else './weights/model.ckpt')
if mode == 'image':
image = load_image(path, input_size=model.input_size)
result = sess.run(detections, feed_dict={inputs: image})
draw_boxes(
path, boxes_dict=result[0], class_names=class_names, input_size=model.input_size)
return
cv2.namedWindow("Detections")
print("Video being saved at \"" + './detections/video_output.mp4' + "\"")
print("Press 'q' to quit")
pathIn = './data/PETS_2000_Frames/'
files = [f for f in os.listdir(pathIn)]
files.sort(key= lambda x: int(x.split('.')[0].split('_')[1]))
frame=cv2.imread(pathIn+files[0])
frame_size = frame.shape[:2][::-1]
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(
'./detections/video_output.mp4', fourcc, 20, frame_size)
for i in range(len(files)):
frame = cv2.imread(pathIn+files[i])
resized_frame = cv2.resize(frame, dsize=tuple(
(x) for x in model.input_size[::-1]), interpolation=cv2.INTER_NEAREST)
result = sess.run(detections, feed_dict={inputs: [resized_frame]})
draw_boxes_frame(frame, frame_size, result,
class_names, model.input_size)
start_time = time.time()
# rc, image = video.read()
if type(frame) == type(None):
break
frame = cv2.resize(frame, frame_size)
resultImage = frame
frameCounter = frameCounter + 1
carIDtoDelete = []
for carID in carTracker.keys():
trackingQuality = carTracker[carID].update(frame)
if trackingQuality < 7:
carIDtoDelete.append(carID)
for carID in carIDtoDelete:
print('Removing carID ' + str(carID) + \
' from list of trackers.')
print('Removing carID ' + str(carID) + ' previous location.')
print('Removing carID ' + str(carID) + ' current location.')
carTracker.pop(carID, None)
carLocation1.pop(carID, None)
carLocation2.pop(carID, None)
if not (frameCounter % 10):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cars = carCascade.detectMultiScale(
gray, 1.1, 13, 18, (24, 24))
for (_x, _y, _w, _h) in cars:
x = int(_x)
y = int(_y)
w = int(_w)
h = int(_h)
x_bar = x + 0.5 * w
y_bar = y + 0.5 * h
matchCarID = None
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
t_x_bar = t_x + 0.5 * t_w
t_y_bar = t_y + 0.5 * t_h
if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
matchCarID = carID
if matchCarID is None:
print('Creating new tracker ' + str(currentCarID))
tracker = dlib.correlation_tracker()
tracker.start_track(
frame, dlib.rectangle(x, y, x + w, y + h))
carTracker[currentCarID] = tracker
carLocation1[currentCarID] = [x, y, w, h]
currentCarID = currentCarID + 1
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
carLocation2[carID] = [t_x, t_y, t_w, t_h]
end_time = time.time()
if not (end_time == start_time):
# print("Reached at 168")
fps = 1.0/(end_time - start_time)
for i in carLocation1.keys():
if frameCounter % 1 == 0:
[x1, y1, w1, h1] = carLocation1[i]
[x2, y2, w2, h2] = carLocation2[i]
# print 'previous location: ' + str(carLocation1[i]) + ', current location: ' + str(carLocation2[i])
carLocation1[i] = [x2, y2, w2, h2]
# print("Reached at 177")
# print 'new previous location: ' + str(carLocation1[i])
if [x1, y1, w1, h1] != [x2, y2, w2, h2]:
# print("Reached at 181")
if (speed[i] == None or speed[i] == 0):
speed[i] = estimateSpeed(
[x1, y1, w1, h1], [x2, y2, w2, h2])
# if y1 > 275 and y1 < 285:
if speed[i] != None:
print(str(int(speed[i])) + " km/hr")
cv2.putText(resultImage, str(int(speed[i])) + " km/hr", (int(x1 + w1/2), int(y1-5)), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2)
cv2.imshow("Detections", resultImage)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
out.write(resultImage)
cv2.destroyAllWindows()
#
#================================================================
from PIL import Image
import numpy as np
from core import utils
classes = utils.read_coco_names('./data/coco.names')
data = open('./data/train_data/quick_train_data.txt', 'r').readlines()
example = data[5].split(' ')
image_path = example[0]
boxes_num = len(example[1:]) // 5
bboxes = np.zeros([boxes_num, 4], dtype=np.float64)
labels = np.zeros([
boxes_num,
], dtype=np.int32)
for i in range(boxes_num):
labels[i] = example[1 + i * 5]
bboxes[i] = [float(x) for x in example[2 + i * 5:6 + i * 5]]
scores = np.array([1] * boxes_num)
image = Image.open(image_path)
utils.draw_boxes(bboxes, scores, labels, image, classes, image.size)
import cv2
from PIL import Image
import numpy as np
input_image = "../data/train_dome_data/images/raccoon-4.jpg"
image = Image.open(input_image)
# image = cv2.imread(input_image)
# image = Image.fromarray(image)
image_resize = cv2.resize(np.array(image) / 255., (416, 416))
image_place = tf.placeholder(dtype=tf.float32, shape=(None, 416, 416, 3))
CLASSES = utils.read_coco_names('../data/raccoon.names')
ANCHORE = utils.get_anchors("../data/raccoon_anchors.txt", 416, 416)
model = yolov3.yolov3(len(CLASSES), ANCHORE)
with tf.variable_scope('yolov3'):
pred_feature_map = model.forward(
image_place, is_training=False) #执行前向传播得到3个尺度的feature map
pred = model.predict(pred_feature_map) #根据feature map进行预测
sess = tf.Session()
saver = tf.train.Saver()
model_dir = tf.train.latest_checkpoint("../data/train_dome_data/model/")
saver.restore(sess, model_dir)
boxes, confs, prods = sess.run(
pred, feed_dict={image_place:
np.expand_dims(image_resize,
0)}) #根据预测结果和获取目标预测框，目标类别 以及是否有object
boxes, confs, prods = utils.cpu_nms(boxes, confs * prods,
len(CLASSES)) #根据非最大值抑制算法的原则 筛选出最优的候选框
utils.draw_boxes(image, boxes, confs, prods, CLASSES, (416, 416),
"../data/font/HuaWenXinWei-1.ttf") #在输出图片上绘制目标框，并输出标签识别率结果
print(boxes, confs, prods)
# nms on GPU
input_tensor, output_tensors = utils.read_pb_return_tensors(
gpu_nms_graph, "./checkpoint/yolov3_gpu_nms.pb",
["Placeholder:0", "concat_10:0", "concat_11:0", "concat_12:0"])
with tf.Session(graph=gpu_nms_graph) as sess:
for i in range(5):
start = time.time()
boxes, scores, labels = sess.run(
output_tensors,
feed_dict={input_tensor: np.expand_dims(img_resized, axis=0)})
print("=> nms on gpu the number of boxes= %d time=%.2f ms" %
(len(boxes), 1000 * (time.time() - start)))
image = utils.draw_boxes(boxes,
scores,
labels,
img,
classes,
SIZE,
show=True)
# nms on CPU
input_tensor, output_tensors = utils.read_pb_return_tensors(
cpu_nms_graph, "./checkpoint/yolov3_cpu_nms.pb",
["Placeholder:0", "concat_9:0", "mul_9:0"])
with tf.Session(graph=cpu_nms_graph) as sess:
for i in range(5):
start = time.time()
boxes, scores = sess.run(
output_tensors,
feed_dict={input_tensor: np.expand_dims(img_resized, axis=0)})
boxes, scores, labels = utils.cpu_nms(boxes,
scores,
def test(self, source_dir=None, image_path=None, show=False):
if source_dir:
image_name = choice(os.listdir(source_dir))
image_path = os.path.join(source_dir, image_name)
img = cv2.imread(image_path, 0)
img = cv2.resize(src=img, dsize=(0, 0), fx=1 / 2, fy=1 / 2)
if img.shape[0] < self.img_h and img.shape[1] < self.img_w:
img = cv2.copyMakeBorder(src=img,
top=(self.img_h - img.shape[0]) // 2,
bottom=(self.img_h - img.shape[0]) // 2,
left=(self.img_w - img.shape[1]) // 2,
right=(self.img_w - img.shape[1]) // 2,
borderType=cv2.BORDER_CONSTANT,
value=[255, 255, 255])
img = cv2.resize(img, (self.img_w, self.img_h))
img = expand_dims(img, axis=2)
classes = os.listdir(self.orig_letters)
cpu_nms_graph = tf.Graph()
input_tensor, output_tensors = utils.read_pb_return_tensors(
cpu_nms_graph,
os.path.join(self.checkpoint_dir, "yolov3_cpu_nms.pb"),
["Placeholder:0", "concat_5:0", "mul_2:0"])
with tf.Session(graph=cpu_nms_graph) as sess:
boxes, scores = sess.run(
output_tensors,
feed_dict={input_tensor: np.expand_dims(img, axis=0)})
boxes = boxes.reshape(-1, 4)
scores = scores.reshape(-1, self.num_classes)
min_wh, max_wh = -10000, 10000
min_ratio = 1 / 4 # 0 -- 1
mask = np.logical_and(boxes[:, 0] >= min_wh, boxes[:, 0] <= max_wh)
mask = np.logical_and(mask, boxes[:, 1] >= min_wh)
mask = np.logical_and(mask, boxes[:, 2] >= min_wh)
mask = np.logical_and(mask, boxes[:, 3] >= min_wh)
mask = np.logical_and(mask, boxes[:, 1] <= max_wh)
mask = np.logical_and(mask, boxes[:, 2] <= max_wh)
mask = np.logical_and(mask, boxes[:, 3] <= max_wh)
mask = np.logical_and(mask, boxes[:, 0] < boxes[:, 2])
mask = np.logical_and(mask, boxes[:, 1] < boxes[:, 3])
boxes = boxes[mask]
scores = scores[mask]
h = abs(boxes[:, 2] - boxes[:, 0])
w = abs(boxes[:, 3] - boxes[:, 1])
mask = np.logical_and(w / h > min_ratio, h / w > min_ratio)
boxes = boxes[mask]
scores = scores[mask]
if self.filters:
# Harder filters
print(f"Test: Boxes before filtering:\t{boxes.shape[0]}")
mask = np.logical_and(boxes[:, 0] >= 0,
boxes[:, 0] <= img.shape[1])
mask = np.logical_and(mask, boxes[:, 1] >= 0)
mask = np.logical_and(mask, boxes[:, 2] >= 0)
mask = np.logical_and(mask, boxes[:, 3] >= 0)
mask = np.logical_and(mask, boxes[:, 1] <= img.shape[0])
mask = np.logical_and(mask, boxes[:, 2] <= img.shape[1])
mask = np.logical_and(mask, boxes[:, 3] <= img.shape[0])
mask = np.logical_and(mask, boxes[:, 0] < boxes[:, 2])
mask = np.logical_and(mask, boxes[:, 1] < boxes[:, 3])
mask = np.logical_and(
mask,
abs(boxes[:, 2] - boxes[:, 0]) >= self.size_threshold[0])
mask = np.logical_and(
mask,
abs(boxes[:, 3] - boxes[:, 1]) >= self.size_threshold[1])
boxes = boxes[mask]
scores = scores[mask]
print(f"Test: Boxes after filtering:\t{boxes.shape[0]}")
if boxes.shape[0] == 0:
print(
f"Try changing the filters/thresholds in the parameters."
)
boxes, scores, labels = utils.cpu_nms(boxes=boxes,
scores=scores,
num_classes=self.num_classes,
max_boxes=self.max_boxes)
(image,
results) = utils.draw_boxes(img,
boxes,
scores,
labels,
classes, [self.img_h, self.img_w],
show=show,
size_threshold=self.size_threshold)
return results
BATCH_SIZE,
shuffle=SHUFFLE_SIZE,
multi_image_size=False)
testset = dataset(parser, TEST_TFRECORD, BATCH_SIZE, shuffle=None)
example = trainset.get_next()
images, *y_true = example
model = yolov3.yolov3(NUM_CLASSES, ANCHORS, basic_net=MobilenetV2)
with tf.variable_scope('yolov3'):
model.set_anchor(images)
pred_feature_map = model.forward(images, is_training=False)
y_pred = model.predict(pred_feature_map)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, "./checkpoint/yolov3.ckpt-25000")
run_items = sess.run([images, y_pred])
for i in range(8):
image = run_items[0][i]
pred_boxes = run_items[1][0][i:i + 1]
pred_confs = run_items[1][1][i:i + 1]
pred_probs = run_items[1][2][i:i + 1]
pred_boxes, pred_scores, pred_labels = utils.cpu_nms(
pred_boxes, pred_confs * pred_probs, len(CLASSES))
im = utils.draw_boxes(image * 255,
pred_boxes,
pred_scores,
pred_labels,
CLASSES, (416, 416),
show=True)
iou_thresh=self.iou_thresh)
return {'boxes': boxes, 'probs': probs, 'labels': labels}
def _process_image(self, img):
"""
Image pre-processing
:param img: Single image records
:type img: [HxWx3] numpy array
:return: Pre-processed image
:rtype: [self.img_h x self.img_w x 3] numpy array
"""
img_resized = np.array(np.resize(img, [self.img_h, self.img_w, 3]),
dtype=np.float32)
img_resized = img_resized / 255.
return img_resized
if __name__ == "__main__":
model = ObjectRecognition(use_gpu=False)
image_path = "data/OpenImages/test/Apple/0da61cd490c57814.jpg"
img = np.asarray(Image.open(image_path))
predictions = model.predict_image(img)
image = utils.draw_boxes(img,
predictions['boxes'],
predictions['scores'],
predictions['labels'],
model.classes, [model.img_h, model.img_w],
show=True)
import tensorflow as tf
from core import utils, yolov3
import cv2
from PIL import Image
import numpy as np
input_image = "../data/raccoon_data/images/raccoon-4.jpg"
image = Image.open(input_image)
# image = cv2.imread(input_image)
# image = Image.fromarray(image)
image_resize = cv2.resize(np.array(image) / 255., (416, 416))
image_place = tf.placeholder(dtype=tf.float32, shape=(None, 416, 416, 3))
CLASSES = utils.read_coco_names('../data/objects.names')
ANCHORE = utils.get_anchors("../data/objects.txt", 416, 416)
model = yolov3.yolov3(len(CLASSES), ANCHORE)
with tf.variable_scope('yolov3'):
pred_feature_map = model.forward(image_place, is_training=False)
pred = model.predict(pred_feature_map)
sess = tf.Session()
saver = tf.train.Saver()
model_dir = tf.train.latest_checkpoint("../data/raccoon_data/model/")
saver.restore(sess, model_dir)
boxes, confs, prods = sess.run(pred, feed_dict={image_place: np.expand_dims(image_resize, 0)})
boxes, confs, prods = utils.cpu_nms(boxes, confs * prods, len(CLASSES))
utils.draw_boxes(image, boxes, confs, prods, CLASSES, (416, 416), "../data/font/HuaWenXinWei-1.ttf")
print(boxes, confs, prods)
with tf.variable_scope("yoloAt"):
p_feature_map = model.forward(inputs_image, is_training=False)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(var_list=tf.global_variables())
saver.restore(sess, Config.CKPT)
print(Config.F_Yellow + "starting predicting" + Config.F_Init)
for pic in os.listdir('./data/test_pic'):
img = Image.open('./data/test_pic/' + pic)
img_resized = np.array(img.resize(size=(Config.IMAGE_H,
Config.IMAGE_W)),
dtype=np.float32)
img_resized = img_resized / 255
boxes, confs, probs = sess.run(
model.predict(p_feature_map),
feed_dict={inputs_image: np.expand_dims(img_resized, axis=0)})
scores = confs * probs
boxes, scores, labels = utils.cpu_nms(boxes, scores,
Config.NUM_CLASSES)
image = utils.draw_boxes(img,
boxes,
scores,
labels,
Config.CLASSES,
[Config.IMAGE_H, Config.IMAGE_W],
show=False)
image.save('./jpg/pre_{}'.format(pic))
print("finished predicting {}".format(Config.F_Red + pic +
Config.F_Init))
