def main():
import sys
import cv2
namesfile = None
if len(sys.argv) == 6:
n_classes = int(sys.argv[1])
weightfile = sys.argv[2]
imgfile = sys.argv[3]
height = int(sys.argv[4])
width = int(sys.argv[5])
elif len(sys.argv) == 7:
n_classes = int(sys.argv[1])
weightfile = sys.argv[2]
imgfile = sys.argv[3]
height = sys.argv[4]
width = int(sys.argv[5])
namesfile = int(sys.argv[6])
else:
print('Usage: ')
print(' python models.py num_classes weightfile imgfile namefile')
model = Yolov4(yolov4conv137weight=None, n_classes=n_classes, inference=True)
pretrained_dict = torch.load(weightfile, map_location=torch.device('cuda'))
model.load_state_dict(pretrained_dict)
use_cuda = True
if use_cuda:
model.cuda()
img = cv2.imread(imgfile)
# Inference input size is 416*416 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (width, height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
from tool.utils import load_class_names, plot_boxes_cv2
from tool.torch_utils import do_detect
for i in range(2): # This 'for' loop is for speed check
# Because the first iteration is usually longer
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda)
if namesfile == None:
if n_classes == 20:
namesfile = 'data/voc.names'
elif n_classes == 80:
namesfile = 'data/coco.names'
elif n_classes == 1:
namesfile = 'data/smoke.names'
else:
print("please give namefile")
class_names = load_class_names(namesfile)
plot_boxes_cv2(img, boxes[0], 'predictions.jpg', class_names)
def main(self):
frame = cv2.imread(id)
try:
img, orig_im, dim = prep_image(frame, 160)
im_dim = torch.FloatTensor(dim).repeat(1, 2)
if self.CUDA: #### If you have a gpu properly installed then it will run on the gpu
im_dim = im_dim.cuda()
img = img.cuda()
frame = cv2.imread(self.id)
output = self.model(img)
from tool.utils import post_processing, plot_boxes_cv2
bounding_boxes = post_processing(img, self.confidence,
self.nms_thesh, output)
frame = plot_boxes_cv2(frame,
bounding_boxes[0],
savename=None,
class_names=self.classes,
color=None,
colors=self.colors)
except:
pass
cv2.imwrite('IMG_0748-1.JPG', frame)
torch.cuda.empty_cache()
def predict(self, image, session_id):
''' predicts objects on image and return an image with bounding box on it '''
out_path = f'./out/{session_id}.jpg'
t1 = time.time()
image_resized_arr = self.preprocess(image)
boxes = do_detect(self.model, image_resized_arr, 0.4, 0.6,
self.use_cuda)
plot_boxes_cv2(image_resized_arr, boxes[0], out_path, self.class_names)
t2 = time.time()
print("----------")
print(f"total latency: {str((t2-t1)*1000)} ms")
print("----------")
return out_path
def predict(self, image, session_id):
''' predicts objects on image and return an image with bounding box on it '''
out_path = f'./out/{session_id}.jpg'
t1 = time.time()
img = Image.open(io.BytesIO(image))
boxes_arr = self.detect_boxes(image)
plot_boxes_cv2(np.array(img), boxes_arr, out_path, self.class_names)
t2 = time.time()
print("----------")
print(f"total latency: {str((t2-t1)*1000)} ms")
print("----------")
return out_path
def main(self):
q = queue.Queue()
while True:
def frame_render(queue_from_cam):
frame = self.cap.read(
) # If you capture stream using opencv (cv2.VideoCapture()) the use the following line
# ret, frame = self.cap.read()
frame = cv2.resize(frame, (self.width, self.height))
queue_from_cam.put(frame)
cam = threading.Thread(target=frame_render, args=(q, ))
cam.start()
cam.join()
frame = q.get()
q.task_done()
fps = FPS().start()
try:
img, orig_im, dim = prep_image(frame, 160)
im_dim = torch.FloatTensor(dim).repeat(1, 2)
if self.CUDA: #### If you have a gpu properly installed then it will run on the gpu
im_dim = im_dim.cuda()
img = img.cuda()
# with torch.no_grad(): #### Set the model in the evaluation mode
output = self.model(img)
from tool.utils import post_processing, plot_boxes_cv2
bounding_boxes = post_processing(img, self.confidence,
self.nms_thesh, output)
frame = plot_boxes_cv2(frame,
bounding_boxes[0],
savename=None,
class_names=self.classes,
color=None,
colors=self.colors)
except:
pass
fps.update()
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.1f}".format(fps.fps()))
cv2.imshow("Object Detection Window", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
continue
torch.cuda.empty_cache()
def detect(img):
namesfile = 'data/coco.names'
n_classes = 80
weightfile = './yolov4.pth'
imgfile = img
height = 512
width = 512
model = Yolov4(yolov4conv137weight=None, n_classes=n_classes, inference=True)
pretrained_dict = torch.load(weightfile, map_location=torch.device('cuda'))
model.load_state_dict(pretrained_dict)
use_cuda = True
if use_cuda:
model.cuda()
img = cv2.imread(imgfile)
# Inference input size is 416*416 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (width, height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
from tool.utils import load_class_names, plot_boxes_cv2
from tool.torch_utils import do_detect
for i in range(2): # This 'for' loop is for speed check
# Because the first iteration is usually longer
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda) # 输出框位置和名字
class_names = load_class_names(namesfile)
plot_boxes_cv2(img, boxes[0], 'predictions.jpg', class_names)
return boxes[0]
def detect(self):
"""Detect if image is Aadhaar and save image if detected"""
logger.info(f"The model expects input shape: {model.get_inputs()[0].shape}")
image_src = cv2.imread(self.image_path)
IN_IMAGE_H = model.get_inputs()[0].shape[2]
IN_IMAGE_W = model.get_inputs()[0].shape[3]
# Input
resized = cv2.resize(
image_src, (IN_IMAGE_W, IN_IMAGE_H), interpolation=cv2.INTER_LINEAR
)
img_in = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
img_in = np.transpose(img_in, (2, 0, 1)).astype(np.float32)
img_in = np.expand_dims(img_in, axis=0)
img_in /= 255.0
logger.info(f"Shape of the network input after preprocessing: {img_in.shape}")
# Compute
input_name = model.get_inputs()[0].name
outputs = model.run(None, {input_name: img_in})
boxes = post_processing(img_in, 0.4, 0.6, outputs)
logger.info(f"Post Processing output : {boxes}")
if np.array(boxes).size:
namesfile = cfg.NAMESFILE
class_names = load_class_names(namesfile)
if plot_boxes_cv2(
image_src, boxes[0], savename=self.filename, class_names=class_names
): # Detect image and save image with bounding boxes if Aadhaar card detected
return 1
else:
return 0
else:
logger.info("Uploaded Image is not Aadhaar")
return 0
2);
# Display FPS on frame
cv2.putText(img, "FPS : " + str(int(fps)), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50),
2);
# Display result
cv2.imshow("Tracking", img)
cv2.waitKey(1)
start = time.time()
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda)
finish = time.time()
print('Predicted in %f seconds.' % (finish - start))
print('Fps: %d' % int(1 / (finish - start)))
class_names = load_class_names(namesfile)
result_img = plot_boxes_cv2(img, boxes[0], savename=None, class_names=class_names)
if(len(boxes[0])!=0):
print("b")
box = boxes[0][0]
x1 = int(box[0] * 640)
y1 = int(box[1] * 480)
x2 = int(box[2] * 640)
y2 = int(box[3] * 480)
w=x2-x1
h=y2-y1
bbox=(x1,y1,w,h)
nbox=(x1,y1,int(w/2),int(h/2))
ok = tracker.init(img, bbox)
tr=1
def demo_tensorflow(tfpb_file="./weight/yolov4.pb",
image_path=None,
print_sensor_name=False):
graph_name = 'yolov4'
tf.compat.v1.disable_eager_execution()
with tf.compat.v1.Session() as persisted_sess:
print("loading graph...")
with gfile.FastGFile(tfpb_file, 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
persisted_sess.graph.as_default()
tf.import_graph_def(graph_def, name=graph_name)
# print all sensor_name
if print_sensor_name:
tensor_name_list = [
tensor.name for tensor in
tf.compat.v1.get_default_graph().as_graph_def().node
]
for tensor_name in tensor_name_list:
print(tensor_name)
inp = persisted_sess.graph.get_tensor_by_name(graph_name + '/' +
'input:0')
print(inp.shape)
out1 = persisted_sess.graph.get_tensor_by_name(graph_name + '/' +
'output_1:0')
out2 = persisted_sess.graph.get_tensor_by_name(graph_name + '/' +
'output_2:0')
out3 = persisted_sess.graph.get_tensor_by_name(graph_name + '/' +
'output_3:0')
print(out1.shape, out2.shape, out3.shape)
# image_src = np.random.rand(1, 3, 608, 608).astype(np.float32) # input image
# Input
image_src = cv2.imread(image_path)
resized = cv2.resize(image_src, (inp.shape[2], inp.shape[3]),
interpolation=cv2.INTER_LINEAR)
img_in = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
img_in = np.transpose(img_in, (2, 0, 1)).astype(np.float32)
img_in = np.expand_dims(img_in, axis=0)
img_in /= 255.0
print("Shape of the network input: ", img_in.shape)
feed_dict = {inp: img_in}
outputs = persisted_sess.run([out1, out2, out3], feed_dict)
print(outputs[0].shape)
print(outputs[1].shape)
print(outputs[2].shape)
boxes = post_processing(img_in, 0.4, outputs)
num_classes = 80
if num_classes == 20:
namesfile = 'data/voc.names'
elif num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/names'
class_names = load_class_names(namesfile)
result = plot_boxes_cv2(image_src,
boxes,
savename=None,
class_names=class_names)
cv2.imshow("tensorflow predicted", result)
cv2.waitKey()
width, height = (darknet_model.width, darknet_model.height)
darknet_model.load_weights(weightfile)
if use_cuda:
darknet_model.cuda()
class_names = load_class_names(namesfile)
t1 = time.time()
total_time = round(t1 - t0, 2)
print("1 - Initiated DepthModel. -- {} minutes {} seconds".format(
total_time // 60, total_time % 60))
print("====================================")
print("====================================")
print("====================================")
# Inference
img = cv2.imread(imgfile)
sized = cv2.resize(img, (width, height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
start = time.time()
boxes = do_detect(darknet_model, sized, 0.4, 0.6, use_cuda)
finish = time.time()
print('%s: Predicted in %f seconds.' % (imgfile, (finish - start)))
print(boxes)
plot_boxes_cv2(img,
boxes[0],
savename='demo_yolo.jpg',
class_names=class_names)
print("====================================")
print("====================================")
print("====================================")
y2 / float(height), None, "", id
])
############################ detect: start ##################################
img = cv2.imread(os.path.join(dataset_dir, imgfile))
# Inference input size is 416*416 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (width, height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
to_plot = plot_boxes_cv2(img, gt_boxes, class_names=class_names)
if eval_model:
with torch.no_grad():
# for i in range(2): # This 'for' loop is for speed check
# # Because the first iteration is usually longer
# boxes = do_detect(model, sized, 0.4, 0.6, use_cuda)
boxes = do_detect(model, sized, 0.4, 0.6, use_device)
pred = plot_boxes_cv2(img,
boxes[0],
class_names=class_names)
to_plot = np.concatenate([to_plot, pred], axis=1)
# to_plot = (255 * np.clip(to_plot, 0, 1)).astype(np.uint8)
############################ detect: end ##################################
def visualize_boxes_cv2(self, boxes, cv_image, output_file_name):
plot_boxes_cv2(cv_image, boxes[0], output_file_name, self.class_names)
def eval_mskim():
import os
import cv2
import pickle
import argparse
import numpy as np
from tool.utils import load_class_names, plot_boxes_cv2
from tool.torch_utils import do_detect
parser = argparse.ArgumentParser()
parser.add_argument('--n_classes', default=1)
parser.add_argument('--weightfile', default='checkpoints/Yolov4_epoch10.pth')
parser.add_argument('--imgfile', default='data/mskim/test90_10percent.txt')
parser.add_argument('--size', type=int, nargs='+', default=[608, 608])
parser.add_argument('--namesfile', default='data/smoke.names')
parser.add_argument('--nms_th', default=0.6)
parser.add_argument('--conf_th', default=0.4)
parser.add_argument('--save_path', default='predictions/')
parser.add_argument('--save_image', default=False, action='store_true')
args = parser.parse_args()
target_h, target_w = args.size
model = Yolov4(yolov4conv137weight=None, n_classes=args.n_classes, inference=True)
pretrained_dict = torch.load(args.weightfile, map_location=torch.device('cuda'))
model.load_state_dict(pretrained_dict)
use_cuda = True
if use_cuda:
model.cuda()
with open(args.imgfile, 'r') as f:
img_file_lst = [line.strip() for line in f.readlines()]
common_path = os.path.commonpath(img_file_lst)
prediction_results = {}
class_names = load_class_names(args.namesfile)
for img_file in img_file_lst:
# origin image
img = cv2.imread(img_file)
origin_h, origin_w = img.shape[:2]
sized = cv2.resize(img, (target_w, target_h))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
# argument of do_detect: (model, img, conf_thresh, nms_thresh, use_cuda=1)
boxes = do_detect(model, sized, args.conf_th, args.nms_th, use_cuda)
boxes_np = np.asarray(boxes) # shape: (1, 0) or (1, N, 7) [7: xmin, ymin, xmax, ymax, score, id]
if boxes_np.shape[1] == 0:
pred = np.zeros((1, 5))
else:
# box coordinates are normalized to 0 ~ 1
pred = boxes_np[0, :, :-1]
# resize to origin image scale
pred[:, 0] = pred[:, 0] * origin_w
pred[:, 1] = pred[:, 1] * origin_h
pred[:, 2] = pred[:, 2] * origin_w
pred[:, 3] = pred[:, 3] * origin_h
prediction_results[os.path.relpath(img_file, common_path)] = pred
# save prediction results
if args.save_image:
filename = os.path.basename(img_file)
dirname = os.path.dirname(img_file)
rel_dir = os.path.relpath(dirname, common_path)
_save_path = os.path.join(args.save_path, rel_dir)
os.makedirs(_save_path, exist_ok=True)
plot_boxes_cv2(img, boxes[0], os.path.join(_save_path, filename), class_names)
with open(os.path.join(args.save_path, 'prediction_results.pickle'), 'wb') as f:
pickle.dump(prediction_results, f)
for box in boxes:
x = (box[2] + box[0]) / 2 * width
y = (box[3] + box[1]) / 2 * height
print()
return image
if __name__ == '__main__':
model = Yolov4(None, n_classes=80, inference=True)
state = torch.load('16.pth')
model.load_state_dict(state['model'])
del state
model.eval()
rawimg = cv2.imread(
'/media/palm/data/coco/images/val2017/000000289343.jpg')
rawimg = cv2.cvtColor(rawimg, cv2.COLOR_BGR2RGB)
rawimg, bboxes = resize_image(rawimg, None, 608, 608)
img = rawimg.copy().transpose(2, 0, 1)
inputs = torch.from_numpy(np.expand_dims(img,
0).astype('float32')).div(255.0)
output = model(inputs)
boxes = post_processing(img, 0.4, 0.4, output)
img = plot_boxes_cv2(rawimg.astype('uint8'), boxes[0])
img = plot_lines(img, boxes[0])
cv2.imshow('a', img)
cv2.waitKey()
use_cuda = True
if use_cuda:
model.cuda()
for path, _, files in os.walk(img_root):
if len(_) > 0:
continue
for file in files:
if os.path.splitext(file)[1] not in ['.jpg', 'jpeg', 'png']:
continue
imgfile = os.path.join(path, file)
img = cv2.imread(imgfile)
# Inference input size is 416*416 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (width, height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda)
plot_boxes_cv2(img, boxes[0], os.path.join(img_out, file),
class_names)
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (args.width, args.height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
from tool.utils import load_class_names, plot_boxes_cv2
from tool.torch_utils import do_detect
for i in range(2): # This 'for' loop is for speed check
# Because the first iteration is usually longer
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda)
if namesfile == None:
if args.n_classes == 20:
namesfile = 'data/voc.names'
elif args.n_classes == 80:
namesfile = 'data/coco.names'
else:
print("please give namefile")
class_names = load_class_names(namesfile)
# print(class_names)
plot_boxes_cv2(
img, boxes[0],
'predicitons_imgs/predictions_' + tmp_img[-10:-4] + '.jpg',
class_names)
i += 1
if i == 100:
break
for i, abs_name in tqdm(enumerate(abs_names)):
img = cv2.imread(abs_name)
# Inference input size is 416*416 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (width, height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
from tool.utils import load_class_names, plot_boxes_cv2
from tool.torch_utils import do_detect
# for i in range(2): # This 'for' loop is for speed check
# # Because the first iteration is usually longer
boxes = do_detect(model, sized, 0.1, 0.4, use_cuda)
boxes = post_process_boxes(boxes)
if namesfile == None:
if n_classes == 20:
namesfile = 'data/voc.names'
elif n_classes == 80:
namesfile = '../data/coco.names'
else:
print("please give namefile")
class_names = load_class_names(namesfile)
save_path = f'../preds/golf_gray_coco_train/{i}.jpg'
plot_boxes_cv2(img, boxes, save_path, class_names)
import cv2
import numpy as np
import glob
from models import Yolov4
from tool import utils, torch_utils
import torch
f_names = glob.glob("test2/*.png")
model = Yolov4(yolov4conv137weight=None, n_classes=1, inference=True)
model.load_state_dict(torch.load("Yolov4.pth"))
model.cuda().eval()
for f in f_names:
im = cv2.imread(f)
im = cv2.resize(im, (512, 320))
with torch.no_grad():
boxes = torch_utils.do_detect(model,
cv2.cvtColor(im, cv2.COLOR_BGR2RGB), 0.4,
0.4, 1)
utils.plot_boxes_cv2(im, boxes[0], "b" + f, color=(0, 0, 255))
def main():
n_classes = 18
weightfile = r'D:\DeepBlueProjects\chem-lab\pytorch-YOLOv4-master\checkpoints\Yolov4_epoch6.pth'
imgfile = r'D:\Data\CMS01_single-end\val\JPEGImages\frontfront_0518.jpg'
base_dir = r'D:\Data\chem-yolov4\eval-dataset\top'
gt_path = os.path.join(base_dir, 'gt.json')
name_id_path = os.path.join(base_dir, 'name_id.json')
with open(gt_path, 'r') as f:
gt_dict = json.load(f)
with open(name_id_path, 'r') as f:
name_id_dict = json.load(f)
input_size = (960, 960)
model = Yolov4(yolov4conv137weight=None,
n_classes=n_classes,
inference=True)
pretrained_dict = torch.load(weightfile, map_location=torch.device('cuda'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in pretrained_dict.items():
name = k[7:] # remove `module.`，表面从第7个key值字符取到最后一个字符，正好去掉了module.
new_state_dict[name] = v # 新字典的key值对应的value为一一对应的值。
model.load_state_dict(pretrained_dict)
use_cuda = True
if use_cuda:
model.cuda()
data_txt = os.path.join(base_dir, 'data.txt')
save_dir = os.path.join(base_dir, 'JPEGImages_pred')
result_dir = os.path.join(base_dir, 'result_txt')
with open(data_txt, 'r') as f:
imgfiles = f.readlines()
box_list = []
for imgfile in imgfiles:
img = cv2.imread(imgfile.strip('\n'))
img_h, img_w, _ = img.shape
img_name = imgfile.split('\\')[-1].strip('\n')
img_id = name_id_dict[img_name]
result_txt = os.path.join(result_dir, img_name[:-4] + '.txt')
result_f = open(result_txt, 'w')
# Inference input size is 416*416 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (input_size[1], input_size[0]))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
# for i in range(2): # This 'for' loop is for speed check
# Because the first iteration is usually longer
boxes = do_detect(model, sized, 0.01, 0.3, use_cuda)
for box in boxes[0]:
x1 = int((box[0] - box[2] / 2.0) * img_w)
y1 = int((box[1] - box[3] / 2.0) * img_h)
x2 = int((box[0] + box[2] / 2.0) * img_w)
y2 = int((box[1] + box[3] / 2.0) * img_h)
w = x2 - x1
h = y2 - y1
if len(box) >= 7:
cls_conf = box[5]
cls_id = box[6]
box_list.append({
"image_id": img_id,
"category_id": int(cls_id),
"bbox": [x1, y1, w, h],
"score": float(cls_conf)
})
string = ','.join([
str(cls_id),
str(x1),
str(y1),
str(x2),
str(y2),
str(cls_conf)
]) + '\n'
result_f.write(string)
if cls_conf > 0.3:
cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)),
(255, 0, 255), 1)
cv2.putText(img, str(cls_id), (int(x1 + 10), int(y1 + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 255),
1)
cv2.putText(img, str(round(cls_conf, 3)),
(int(x1 + 30), int(y1 + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (20, 240, 0), 1)
else:
print('????')
result_f.close()
namesfile = 'data/chem.names'
class_names = load_class_names(namesfile)
save_name = os.path.join(save_dir, img_name)
plot_boxes_cv2(img, boxes[0], save_name, class_names)
# cv2.imshow('result', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
info, map_iou0_5 = get_coco_mAP(gt_dict, box_list)
# print("---base_eval---epoch%d"%real_epoch)
print(info)
for file in files_list:
img_path = os.path.join(parent, file)
rel_img_path = os.path.join(parent_path, file)
img = cv2.imread(img_path)
sized = cv2.resize(img, (args.width, args.height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda, False)
class_names = load_class_names(args.namesfile)
output_path = os.path.join(args.output, rel_img_path)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
plot_boxes_cv2(img, boxes[0], output_path, class_names)
else:
img = cv2.imread(args.input)
# Inference input size is 608*608 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (args.width, args.height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda)
class_names = load_class_names(args.namesfile)
boxes, period = do_detect(model, sized, 0.4, 0.6, use_cuda)
# print(len(boxes))
# print(boxes)
# print(len(box))
# print(box)
box = boxes[0]
pred_fname = 'predictions/mAp512_30/' + imgfile[imgfile.rfind('\\') +
1:]
output_fname = 'predictions/mAp512_30/' + imgfile[
imgfile.rfind('\\') + 1:imgfile.rfind('.png')] + '.txt'
with open(output_fname, 'w') as outfile:
if len(box) >= 1:
class_names = load_class_names(namesfile)
plot_boxes_cv2(img, box, pred_fname, class_names)
cls_conf = box[0][5]
cls_id = box[0][6]
print('%s: %f' % (class_names[cls_id], cls_conf))
blist = map(str, box[0][0:4])
outline = str(cls_id) + ' ' + ' '.join(blist) + ' ' + str(
cls_conf) + '\n'
# print('')
outfile.write(outline)
# print('===\n %f \n===' % period)
time.append(period)
else:
print('no result')
outline = '-1\n'
outfile.write(outline)
print(np.mean(time))
def infer(self,data): sized = cv2.resize(data, (self.width, self.height)) sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB) boxes = do_detect(self.model, sized, 0.4, 0.6, self.use_cuda,self.is_half) plot_boxes_cv2(data, boxes[0], savename='./data/predictions.jpg', class_names=self.class_names)
if use_cuda:
model.cuda()
img = cv2.imread(imgfile)
# Inference input size is 416*416 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (width, height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
from tool.utils import load_class_names, plot_boxes_cv2
from tool.torch_utils import do_detect
for i in range(2): # This 'for' loop is for speed check
# Because the first iteration is usually longer
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda)
if namesfile == None:
if n_classes == 20:
namesfile = 'data/voc.names'
elif n_classes == 80:
namesfile = 'data/coco.names'
else:
print("please give namefile")
class_names = load_class_names(namesfile)
plot_boxes_cv2(img, boxes[0], 'predictions.jpg', class_names)
def inference(self, img_path, plot=False, j=0):
def convert_rgb(dmap):
x = np.asarray([dmap] * 3)
return np.transpose(x, (1, 2, 0))
t0 = time.time()
# Depth estimation
data, original_size = load_data_bts(img_path)
disp = predict_bts(self.depth_model, data, original_size)
# print(disp)
# Bounding box
img, boxes = self.bbox_inference(img_path)
img = plot_boxes_cv2(img,
boxes[0],
class_names=self.class_names,
disp=disp)
# Construct message
# df = construct_object_table(img, boxes, self.class_names, disp)
# msg = get_instructions(df)
# Plot
if plot:
cv2.imwrite('output/_{}'.format(os.path.basename(img_path)), disp)
# plt.imshow(disp, cmap='plasma')
# plt.savefig('frame/depth_map{}.png'.format(j))
plot_boxes_cv2(img,
boxes[0],
savename='output/bbox{}.png'.format(j),
class_names=self.class_names,
disp=disp)
def create_grid(gimg, n=6, cl=(91, 235, 52)):
h, w = gimg.shape[0:2]
ys = [int(h * j / n) for j in range(1, n)]
xs = [int(w * j / n) for j in range(1, n)]
for x in xs:
cv2.line(gimg, (x, 0), (x, h), cl, 1, 1)
for y in ys:
cv2.line(gimg, (0, y), (w, y), cl, 1, 1)
sensor_gimg = gimg.copy()
start_xs = [int(h * j / n) for j in range(0, n)]
start_ys = [int(w * j / n) for j in range(0, n)]
end_xs = [int(h * j / n) for j in range(1, n + 1)]
end_ys = [int(w * j / n) for j in range(1, n + 1)]
for x1, x2 in zip(start_xs, end_xs):
for y1, y2 in zip(start_ys, end_ys):
sensor_gimg[x1:x2,
y1:y2, :] = np.mean(sensor_gimg[x1:x2,
y1:y2, :])
return gimg, sensor_gimg
dmap = convert_rgb(disp)
grid_dmap = dmap.copy()
grid_dmap, sensor_gimg = create_grid(grid_dmap)
t1 = time.time()
total_time = round(t1 - t0, 2)
self.log.info(
"1 - Done inference_image {}. -- {} minutes {} seconds".format(
img_path, total_time // 60, total_time % 60))
# return msg, df
return dmap, img, grid_dmap, sensor_gimg