def predict(cls, img=None, img_id=None):
detection_results = predict_image(img, cls.predictor)
print(detection_results)
predictions = {"img_id": str(img_id)}
annotations = []
for dr in detection_results:
a = {}
b = dr.box.tolist()
a["bbox"] = [
int(b[0]),
int(b[1]),
int(b[2] - b[0]),
int(b[3] - b[1])
]
category_id = dr.class_id
a["category_id"] = int(category_id)
a["category_name"] = cfg.DATA.CLASS_NAMES[int(category_id)]
rle = cls.binary_mask_to_rle(dr.mask)
a["segmentation"] = rle
annotations.append(a)
predictions["annotations"] = annotations
print(predictions)
return predictions
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func)
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite("output.png", viz)
logger.info("Inference output written to output.png")
tpviz.interactive_imshow(viz)
def do_predict(pred_func, input_file, output_path=None):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func)
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
out_filename = input_file.split("/")[-1]
output_path = out_path + out_filename
cv2.imwrite(output_path, viz)
logger.info("Inference output for {} written to output.png".format(input_file))
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func)
if cfg.MODE_MASK:
final = draw_final_outputs_blackwhite(img, results)
else:
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite("output.png", viz)
logger.info(
"Inference output for {} written to output.png".format(input_file))
def do_predict(predictor, input_file):
img = cv2.imread(os.path.join('test_images', input_file), cv2.IMREAD_COLOR)
results = predict_image(img, predictor)
if cfg.MODE_MASK:
final = draw_final_outputs_blackwhite(img, results)
else:
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1) #concatenate hata dena
opp = cv2.imwrite(os.path.join('test_inferences', input_file.split('.')[0]+".png"), viz)
if opp:
logger.info("Inference output for {} Successful".format(input_file))
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func)
final = draw_final_outputs(img, results)
# add green rectangle arround original picture that with failure
height, width, channels = img.shape
cv2.rectangle(img, (0, 0), (width, height),
color=(100, 220, 80), thickness=5)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite(result_folder_path+"/0.png", viz)
logger.info("Inference output written to 0.png")
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
start = time.time()
results = predict_image(img, pred_func)
end = time.time()
print('cost time is :/s', end - start)
#if cfg.MODE_MASK:
# final = draw_final_outputs_blackwhite(img, results)
#else:
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite("{}.png".format(input_file[-15:-4]), viz)
logger.info("Inference output for {} written to {}.png".format(
input_file, input_file[-15:-4]))
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func) # get error from this
img_name = ntpath.basename(input_file)
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
if not os.path.exists(result_folder):
os.makedirs(result_folder)
cv2.imwrite(result_folder + img_name, viz)
logger.info("Inference output for {} written to {}".format(
input_file, result_folder))
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
# start_time = time.time()
results = predict_image(img, pred_func)
# end_time = time.time()
# print(f"--------- Inference time : {end_time - start_time}seconds -----------------")
if cfg.MODE_MASK:
final = draw_final_outputs_blackwhite(img, results)
else:
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite("output.png", viz)
logger.info(
"Inference output for {} written to output.png".format(input_file))
tpviz.interactive_imshow(viz)
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func) # get error from this
img_name = ntpath.basename(input_file)
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
save_path = "/home/jetson/Documents/result/" + model_num + "/"
if not os.path.exists(save_path):
os.makedirs(save_path)
cv2.imwrite(save_path + img_name, viz)
logger.info("Inference output for {} written to {}".format(
input_file, save_path))
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
# while True:
print('Starting predtiction')
start_time = time.time()
results = predict_image(img, pred_func)
print(time.time() - start_time)
if cfg.MODE_MASK:
final = draw_final_outputs_blackwhite(img, results)
else:
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite("output.png", viz)
logger.info(
"Inference output for {} written to output.png".format(input_file))
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func)
final = draw_final_outputs(img, results)
# add green rectangle arround original picture that with failure
height, width, channels = img.shape
cv2.rectangle(img, (0, 0), (width, height),
color=(100, 220, 80),
thickness=5)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite(
"/home/jetson/tensorpack/examples/FasterRCNN/static/images/output.png",
viz)
logger.info("Inference output written to output.png")
def do_predict(pred_func, input_file):
'''
输入input_file,读取与图片,然后调用pred_func,得到了box,score,lables,
结果输出到output.jpg,并交互显示
boxes,labels,scores
:param pred_func:
:param input_file:
:return:
'''
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func)
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite("output.png", viz)
logger.info("Inference output for {} written to output.png".format(input_file))
tpviz.interactive_imshow(viz)
def evaluate_rcnn(model_name, paper_arxiv_id, cfg_list, model_file):
evaluator = COCOEvaluator(
root=COCO_ROOT, model_name=model_name, paper_arxiv_id=paper_arxiv_id
)
category_id_to_coco_id = {
v: k for k, v in COCODetection.COCO_id_to_category_id.items()
}
cfg.update_config_from_args(cfg_list) # TODO backup/restore config
finalize_configs(False)
MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()
predcfg = PredictConfig(
model=MODEL,
session_init=SmartInit(model_file),
input_names=MODEL.get_inference_tensor_names()[0],
output_names=MODEL.get_inference_tensor_names()[1],
)
predictor = OfflinePredictor(predcfg)
def xyxy_to_xywh(box):
box[2] -= box[0]
box[3] -= box[1]
return box
df = get_eval_dataflow("coco_val2017")
df.reset_state()
for img, img_id in tqdm.tqdm(df, total=len(df)):
results = predict_image(img, predictor)
res = [
{
"image_id": img_id,
"category_id": category_id_to_coco_id.get(
int(r.class_id), int(r.class_id)
),
"bbox": xyxy_to_xywh([round(float(x), 4) for x in r.box]),
"score": round(float(r.score), 3),
}
for r in results
]
evaluator.add(res)
if evaluator.cache_exists:
break
evaluator.save()
def do_predict(pred_func, input_file):
img = imread(input_file)
if (len(img.shape) == 2):
img = np.expand_dims(img, axis=2)
img = np.repeat(img, 3, axis=2)
img = scipy.misc.imresize(img, (800, 800))
results = predict_image(img, pred_func)
print(len(results))
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
#for result in results:
#if(result.class_id != 3):
#print(result)
imsave('./output2/outputimg.tif', final)
logger.info(
"Inference output for {} written to outputimg.tif".format(input_file))
return results
def do_predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = predict_image(img, pred_func)
if cfg.MODE_MASK:
final = draw_final_outputs_blackwhite(img, results)
else:
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
'''fxl
# modify file name
cv2.imwrite("output.png", viz)
logger.info("Inference output for {} written to output.png".format(input_file))
'''
base_name = os.path.basename(input_file)
img_name = os.path.splitext(base_name)[0]
result_file = "/content/drive/My Drive/Colab/tp/myoutput/predict_output/" + img_name + ".png"
cv2.imwrite(result_file, viz)
print(img_number, end='')
print("." +
"Inference for {} written to {}".format(base_name, result_file))
def _predict_with_gt(pred_func,
input_file,
ground_truths,
output_dir=None,
font_rs=10,
thickness_rs=10):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
# resized_img, orig_shape, scale = run_resize_image(img)
# TODO: predict_image already contains resize
results = predict_image(img, pred_func)
results = list(filter(lambda x: x.score > 0.7, results))
font_scale = np.sqrt(min(img.shape[:2])) / font_rs
thickness = thickness_rs
print('font_scale:', font_scale)
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
if cfg.MODE_MASK:
final = draw_final_outputs_blackwhite(img,
results,
font_scale=font_scale,
thickness=thickness)
else:
final = draw_final_outputs(img,
results,
font_scale=font_scale,
thickness=thickness)
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
image_with_gt = draw_final_outputs(img,
ground_truths,
font_scale=font_scale,
thickness=thickness)
viz = np.concatenate((image_with_gt, final), axis=1)
out_path = os.path.join(output_dir,
re.sub('/', '-', input_file) + '.out.png')
cv2.imwrite(out_path, viz)
logger.info("Inference output for {} written to\n {}".format(
input_file, out_path))
if args.visualize:
do_visualize(MODEL, args.load)
else:
predcfg = PredictConfig(
model=MODEL,
session_init=get_model_loader(args.load),
input_names=MODEL.get_inference_tensor_names()[0],
output_names=MODEL.get_inference_tensor_names()[1])
if args.compact:
ModelExporter(predcfg).export_compact(args.compact, optimize=False)
elif args.serving:
ModelExporter(predcfg).export_serving(args.serving, optimize=False)
if args.predict:
predictor = OfflinePredictor(predcfg)
for image_file in args.predict:
do_predict(predictor, image_file)
elif args.evaluate:
assert args.evaluate.endswith('.json'), args.evaluate
do_evaluate(predcfg, args.evaluate)
elif args.benchmark:
df = get_eval_dataflow(cfg.DATA.VAL[0])
df.reset_state()
predictor = OfflinePredictor(predcfg)
for img in tqdm.tqdm(df, total=len(df)):
# This include post-processing time, which is done on CPU and not optimized
# To exclude it, modify `predict_image`.
predict_image(img[0], predictor)
