def predict(self, img):
rgb_origin = img
img_numpy = img
img = torch.from_numpy(img.copy()).float()
img = img.cuda()
img_h, img_w = img.shape[0], img.shape[1]
img_trans = FastBaseTransform()(img.unsqueeze(0))
net_outs = self.net(img_trans)
nms_outs = NMS(net_outs, 0)
results = after_nms(nms_outs,
img_h,
img_w,
crop_masks=not self.args.no_crop,
visual_thre=self.args.visual_thre)
torch.cuda.synchronize()
temp = self.time_here
self.time_here = time.time()
self.frame_times.add(self.time_here - temp)
fps = 1 / self.frame_times.get_avg()
frame_numpy = draw_img(results,
img,
self.args,
class_color=True,
fps=fps)
return frame_numpy
def evaluate(net,
dataset,
max_num=-1,
during_training=False,
cocoapi=False,
traditional_nms=False):
frame_times = MovingAverage()
dataset_size = len(dataset) if max_num < 0 else min(max_num, len(dataset))
dataset_indices = list(range(len(dataset)))
dataset_indices = dataset_indices[:dataset_size]
progress_bar = ProgressBar(40, dataset_size)
# For each class and iou, stores tuples (score, isPositive)
# Index ap_data[type][iouIdx][classIdx]
ap_data = {
'box': [[APDataObject() for _ in cfg.dataset.class_names]
for _ in iou_thresholds],
'mask': [[APDataObject() for _ in cfg.dataset.class_names]
for _ in iou_thresholds]
}
make_json = Make_json()
for i, image_idx in enumerate(dataset_indices):
timer.reset()
with timer.env('Data loading'):
img, gt, gt_masks, h, w, num_crowd = dataset.pull_item(image_idx)
batch = img.unsqueeze(0)
if cuda:
batch = batch.cuda()
with timer.env('Network forward'):
#changed
net_outs = net(batch)
nms_outs = NMS(net_outs, traditional_nms)
prep_metrics(ap_data, nms_outs, gt, gt_masks, h, w, num_crowd,
dataset.ids[image_idx], make_json, cocoapi)
# First couple of images take longer because we're constructing the graph.
# Since that's technically initialization, don't include those in the FPS calculations.
fps = 0
if i > 1 and not during_training:
frame_times.add(timer.total_time())
fps = 1 / frame_times.get_avg()
progress = (i + 1) / dataset_size * 100
progress_bar.set_val(i + 1)
print('\rProcessing: %s %d / %d (%.2f%%) %.2f fps ' %
(repr(progress_bar), i + 1, dataset_size, progress, fps),
end='')
else:
table, box_row, mask_row = calc_map(ap_data)
print(table)
return table, box_row, mask_row
if cuda:
net = net.cuda()
# detect images
if args.image is not None:
images = glob.glob(args.image + '/*.jpg')
num = len(images)
for i, one_img in enumerate(images):
img_name = one_img.split('/')[-1]
img_origin = torch.from_numpy(cv2.imread(one_img)).cuda().float()
img_h, img_w = img_origin.shape[0], img_origin.shape[1]
img_trans = FastBaseTransform()(img_origin.unsqueeze(0))
net_outs = net(img_trans)
nms_outs = NMS(net_outs, args.traditional_nms)
show_lincomb = bool(args.show_lincomb and args.image_path)
with timer.env('after nms'):
results = after_nms(nms_outs,
img_h,
img_w,
show_lincomb=show_lincomb,
crop_masks=not args.no_crop,
visual_thre=args.visual_thre,
img_name=img_name)
torch.cuda.synchronize()
img_numpy = draw_img(results, img_origin, args)
def process():
try:
destFile = ""
if request.method == 'POST':
file = request.files['file']
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
destFile = os.path.join(app.config['UPLOAD_FOLDER'], filename)
file.save(destFile)
app.logger.warning('filename=(%s)', filename)
else:
app.logger.warning("Request dictionary data: {}".format(request.data))
app.logger.warning("Request dictionary form: {}".format(request.form))
url = request.form["url"]
print("url:", url)
# download file
destFile = download_file(url)
# app.logger.error('An error occurred')
app.logger.warning('destFile=(%s)', destFile)
img_name = destFile.split('/')[-1]
app.logger.warning('img_name=(%s)', img_name)
# img_origin = cv2.imread(one_img)
# img_tensor = torch.from_numpy(img_origin).float()
img_origin = cv2.imread(destFile)
# torch.from_numpy(img_origin).float()
img_tensor = torch.from_numpy(img_origin).float()
if cuda:
# img_origin = img_origin.cuda()
img_tensor = img_tensor.cuda()
img_h, img_w = img_tensor.shape[0], img_tensor.shape[1]
img_trans = FastBaseTransform()(img_tensor.unsqueeze(0))
net_outs = net(img_trans)
nms_outs = NMS(net_outs, args.traditional_nms)
show_lincomb = bool(args.show_lincomb)
results = after_nms(nms_outs, img_h, img_w, show_lincomb=show_lincomb, crop_masks=not args.no_crop,
visual_thre=args.visual_thre, img_name=img_name)
# img_h, img_w = img_origin.shape[0], img_origin.shape[1]
# img_trans = FastBaseTransform()(img_origin.unsqueeze(0))
# net_outs = net(img_trans)
# nms_outs = NMS(net_outs, args.traditional_nms)
app.logger.warning('img_h=(%s)', img_h)
app.logger.warning('img_w=(%s)', img_w)
app.logger.warning('cuda=(%s)', cuda)
app.logger.warning('args.show_lincomb=(%s)', args.show_lincomb)
app.logger.warning('args.no_crop=(%s)', args.no_crop)
app.logger.warning('args.visual_thre=(%s)', args.visual_thre)
app.logger.warning('args=(%s)', args)
# show_lincomb = bool(args.show_lincomb)
with timer.env('after nms'):
results = after_nms(nms_outs, img_h, img_w, show_lincomb=show_lincomb, crop_masks=not args.no_crop,
visual_thre=args.visual_thre, img_name=img_name)
if cuda:
torch.cuda.synchronize()
# app.logger.warning('results=(%s)', results)
# img_numpy = draw_img(results, img_origin, args)
img_numpy = draw_img(results, img_origin, img_name, args)
cv2.imwrite(f'results/images/{img_name}', img_numpy)
# print(f'\r{i + 1}/{num}', end='')
try:
im = Image.open(f'results/images/{img_name}')
# im = Image.open(destFile)
io = BytesIO()
im.save(io, format='JPEG')
return Response(io.getvalue(), mimetype='image/jpeg')
except IOError:
abort(404)
# return send_from_directory('.', filename), 200
callback = json.dumps({"results": results})
return callback, 200
except:
traceback.print_exc()
return {'message': 'input error'}, 400
def evaluate(net,
dataset,
max_num=-1,
during_training=False,
benchmark=False,
cocoapi=False,
traditional_nms=False):
frame_times = MovingAverage()
dataset_size = len(dataset) if max_num < 0 else min(max_num, len(dataset))
dataset_indices = list(range(len(dataset)))
dataset_indices = dataset_indices[:dataset_size]
progress_bar = ProgressBar(40, dataset_size)
if benchmark:
timer.disable('Data loading')
else:
# For each class and iou, stores tuples (score, isPositive)
# Index ap_data[type][iouIdx][classIdx]
ap_data = {
'box': [[APDataObject() for _ in cfg.dataset.class_names]
for _ in iou_thresholds],
'mask': [[APDataObject() for _ in cfg.dataset.class_names]
for _ in iou_thresholds]
}
make_json = Make_json()
for i, image_idx in enumerate(dataset_indices):
timer.reset()
with timer.env('Data loading'):
img, gt, gt_masks, h, w, num_crowd = dataset.pull_item(image_idx)
batch = Variable(img.unsqueeze(0))
if cuda:
batch = batch.cuda()
with timer.env('Network forward'):
net_outs = net(batch)
nms_outs = NMS(net_outs, traditional_nms)
if benchmark:
prep_benchmark(nms_outs, h, w)
else:
prep_metrics(ap_data, nms_outs, gt, gt_masks, h, w, num_crowd,
dataset.ids[image_idx], make_json, cocoapi)
# First couple of images take longer because we're constructing the graph.
# Since that's technically initialization, don't include those in the FPS calculations.
fps = 0
if i > 1 and not during_training:
frame_times.add(timer.total_time())
fps = 1 / frame_times.get_avg()
progress = (i + 1) / dataset_size * 100
progress_bar.set_val(i + 1)
print('\rProcessing: %s %d / %d (%.2f%%) %.2f fps ' %
(repr(progress_bar), i + 1, dataset_size, progress, fps),
end='')
if benchmark:
print('\n\nStats for the last frame:')
timer.print_stats()
avg_seconds = frame_times.get_avg()
print('Average: %5.2f fps, %5.2f ms' %
(1 / frame_times.get_avg(), 1000 * avg_seconds))
else:
if cocoapi:
make_json.dump()
print(
f'\nJson files dumped, saved in: {json_path}, start evaluting.'
)
gt_annotations = COCO(cfg.dataset.valid_info)
bbox_dets = gt_annotations.loadRes(
f'{json_path}/bbox_detections.json')
mask_dets = gt_annotations.loadRes(
f'{json_path}/mask_detections.json')
print('\nEvaluating BBoxes:')
bbox_eval = COCOeval(gt_annotations, bbox_dets, 'bbox')
bbox_eval.evaluate()
bbox_eval.accumulate()
bbox_eval.summarize()
print('\nEvaluating Masks:')
bbox_eval = COCOeval(gt_annotations, mask_dets, 'segm')
bbox_eval.evaluate()
bbox_eval.accumulate()
bbox_eval.summarize()
return
table, mask_row = calc_map(ap_data)
print(table)
return table, mask_row
