From c5c1e1b801c92eeb0019150eb2c25c9000d075d2 Mon Sep 17 00:00:00 2001 From: DTheLegend Date: Fri, 10 Feb 2023 03:37:05 +0000 Subject: [PATCH] Fix some minor display issues; Add prediction --- cli/fcos/__main__.py | 10 +- cli/fcos/cli/predict.py | 169 ++-------- cli/pyproject.toml | 2 +- main/{fcos/__init__.py => README.md} | 0 main/poetry.lock | 474 +++++++++++++++++++++++++++ main/pyproject.toml | 12 +- train/fcos/train/train.py | 7 +- 7 files changed, 530 insertions(+), 144 deletions(-) rename main/{fcos/__init__.py => README.md} (100%) create mode 100644 main/poetry.lock diff --git a/cli/fcos/__main__.py b/cli/fcos/__main__.py index e33169a..52648b4 100644 --- a/cli/fcos/__main__.py +++ b/cli/fcos/__main__.py @@ -24,6 +24,10 @@ train_parser.add_argument('--save_file', type=pathlib.Path, required=False) predict_parser = sub_parsers.add_parser("predict") +predict_parser.add_argument('model', type=pathlib.Path) +predict_parser.add_argument('weights', type=pathlib.Path) +predict_parser.add_argument('classfile', type=pathlib.Path) +predict_parser.add_argument('image', type=pathlib.Path) args = parser.parse_args() @@ -31,8 +35,12 @@ try: from fcos.train import train del args.command + train(**vars(args)) except ImportError: print("Train Module not included.") elif args.command == "predict": - pass + from fcos.cli import predict + del args.command + + predict.main(**vars(args)) diff --git a/cli/fcos/cli/predict.py b/cli/fcos/cli/predict.py index 931ba5a..9e21c38 100644 --- a/cli/fcos/cli/predict.py +++ b/cli/fcos/cli/predict.py @@ -1,145 +1,44 @@ -from importlib.resources import read_text, open_binary +from fcos.core.models import FCOS +from fcos.core.loaders import ClassLoader +from fcos.core.data_augmentation import preprocessing +from fcos.core.mAP.functions import fcos_to_boxes +import numpy as np import torch -import xml.dom.minidom import cv2 -import fcos.map_function as mf -from fcos.DataLoader import FolderData -import torch.utils.data as Data -import fcos.get_image as get_image -import fcos.module -import fcos.net -from fcos.net import FCOS -def prediction(confs, locs, centers, row, col): - # Find Classes. - try: - f = read_text(__package__, 'classes.txt') - classes = f.splitlines() - except FileNotFoundError: - print("classes.txt file was not found...") - exit(0) - - iou_lime = 0.5 # threshold for iou - cls_lime = 0.2 # threshold for confidence - - # obtain the size of all the feature maps - map_sizes = [] - for map_num in range(len(confs)): - # obtain the size of the feature map - H = confs[map_num].size(2) - W = confs[map_num].size(3) - map_sizes.append([H, W]) - # initialize a manager for feature maps - map_master = mf.Map_master(map_sizes) - - # initialize a list for storing predicted bounding boxes of different classes - GTmaster = [] - for i in classes: - GTmaster.append([]) - - # traverse all feature maps - for feature_num in range(len(confs)): - conf = confs[feature_num].detach().cpu() - loc = locs[feature_num].detach().cpu() - center = centers[feature_num].detach().cpu() - # suppress confidence - conf = conf * center - # obtain non-background area - indexes = torch.max(conf, 1)[1] - indexes = indexes.numpy().tolist()[0] - # search for pixels on the feature map whose confidence are over threshold - for i in range(len(indexes)): - for j in range(len(indexes[i])): - # the pixel is considered as positive sample if its confidence is larger than the threshold - if conf[0, indexes[i][j], i, j] >= cls_lime: - box = [feature_num, i, j, indexes[i][j], conf[0, indexes[i][j], i, j], loc[0, 0, i, j], - loc[0, 1, i, j], loc[0, 2, i, j], loc[0, 3, i, j]] - box = map_master.decode_coordinate(box, row, col) - GTmaster[indexes[i][j]].append(box) - # initialize a empty list for returning the final detected bounding boxes after NMS - boxes = [] - # non maximum suppression (NMS) - for GT in GTmaster: - while len(GT) > 0: - max_obj = [] - for obj in GT[:]: - # obtain the bounding box with the highest confidence within the same category - if max_obj == []: - max_obj = obj - continue - if max_obj[1] < obj[1]: - max_obj = obj - GT.remove(max_obj) - # select the bounding box of the highest confidence as a final predicted box - boxes.append(max_obj) - if len(GT) > 0: - # remove other boxes of the same category whose iou between it and the selected box is larger than the threshold - for obj in GT[:]: - # calculate the iou between it and the selected bounding box - iou = mf.compute_iou([obj[2], obj[3], obj[4], obj[5]], - [max_obj[2], max_obj[3], max_obj[4], max_obj[5]]) - if iou > iou_lime: - # delete it when the iou breaks the threshold - GT.remove(obj) - return boxes - -def main(): +def main(model, weights, classfile, image): # load class list - print("balls") - - f = read_text('fcos', 'classes.txt') - classes = f.splitlines() - print("balls") + classes = ClassLoader(classfile) # load the model - with open_binary(fcos.module, 'net0.unpkl') as f: - net = FCOS() - net.load_state_dict(torch.load(f)) - net.eval() - # load test set - test_set = FolderData("./src/Drake/src/fcos/DataSet/labels/test/") - loader = Data.DataLoader( - dataset=test_set, # torch TensorDataset format - batch_size=1, # mini batch size - shuffle=True, # shuffle the daatset - num_workers=2, # read data by multi threads - ) - - # detect - for step, label_paths in enumerate(loader): - # read one image - xml_path = label_paths[0] - # read annotation file - dom = xml.dom.minidom.parse(xml_path) - # obtain root of the xml file - root = dom.documentElement - objects = root.getElementsByTagName("object") - path = root.getElementsByTagName('path')[0] - # obtain the path of the image - pathname = "./src/Drake/src/fcos/" + path.childNodes[0].data - print(pathname) - # read the image - frame = cv2.imread(pathname) + model = FCOS(torch.load(model)) + model.load_state_dict(torch.load(weights)) + train_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + model.to(train_device) + model.eval() - row = frame.shape[0] - col = frame.shape[1] - torch_images, labels = get_image.get_label(label_paths) - # predict - confs, locs, centers = net(torch_images) - boxes = prediction(confs, locs, centers, row, col) - for box in boxes: - xmin = box[2] - ymin = box[3] - xmax = box[4] - ymax = box[5] - # draw rectangle - frame = cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 40, 255), 2) - frame = cv2.putText(frame, classes[box[0]] + ":" + str(round(box[1].item(), 2)), (xmin, ymin - 5), cv2.FONT_HERSHEY_COMPLEX, 0.8, - (0, 40, 255), 1) - - cv2.imwrite(f'detections/detections_{step}.png', frame) + # obtain the path of the image + frame = cv2.imread(str(image.resolve())) + # torch digestive + mcvities = preprocessing(torch.from_numpy(np.transpose(frame, (2, 0, 1)))).unsqueeze(0) + mcvities = mcvities.to(train_device) -if __name__ == '__main__': - main() \ No newline at end of file + row = frame.shape[0] + col = frame.shape[1] + # predict + confs, locs, centers = model(mcvities) + boxes = fcos_to_boxes(classes, confs, locs, centers, row, col) + for box in boxes: + xmin = box[2] * col // 480 + ymin = box[3] * row // 360 + xmax = box[4] * col // 480 + ymax = box[5] * row // 360 + # draw rectangle + frame = cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 40, 255), 2) + frame = cv2.putText(frame, classes[box[0]] + ":" + str(round(box[1].item(), 2)), (xmin, ymin - 5), cv2.FONT_HERSHEY_COMPLEX, 0.8, + (0, 40, 255), 1) + + cv2.imshow(f'WOW!', frame) + cv2.waitKey(0) & 0xFF == ord('q') \ No newline at end of file diff --git a/cli/pyproject.toml b/cli/pyproject.toml index 885fa08..3d03266 100644 --- a/cli/pyproject.toml +++ b/cli/pyproject.toml @@ -16,4 +16,4 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.poetry.extras] -train = ["fcos-train"] \ No newline at end of file +train = ["fcos-train"] diff --git a/main/fcos/__init__.py b/main/README.md similarity index 100% rename from main/fcos/__init__.py rename to main/README.md diff --git a/main/poetry.lock b/main/poetry.lock new file mode 100644 index 0000000..4bc359e --- /dev/null +++ b/main/poetry.lock @@ -0,0 +1,474 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "certifi" +version = "2022.12.7" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"}, + {file = "certifi-2022.12.7.tar.gz", hash = "sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.0.1" +description = "The Real First Universal Charset Detector. 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+content-hash = "873ca51da785d98254b6dcc7dcd6a101741a8fd9372639b59022726702febc70" diff --git a/main/pyproject.toml b/main/pyproject.toml index 6410816..c28ba6a 100644 --- a/main/pyproject.toml +++ b/main/pyproject.toml @@ -4,15 +4,17 @@ version = "0.1.0" description = "" authors = ["DTheLegend "] readme = "README.md" -packages = [{include = "fcos"}] [tool.poetry.dependencies] -python = "3.7.3" -torch = "^1.13.1" - +python = "3.10.6" +fcos-core = {path = "../core"} +fcos-cli = {path = "../cli", optional=true} +fcos-train = {path = "../train", optional=true} [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" -[extras] \ No newline at end of file +[tool.poetry.extras] +cli = ["fcos-cli"] +train = ["fcos-train"] diff --git a/train/fcos/train/train.py b/train/fcos/train/train.py index 93f4ab2..c45c2a4 100644 --- a/train/fcos/train/train.py +++ b/train/fcos/train/train.py @@ -73,7 +73,7 @@ def train(weights, classfile, train_dataset, val_dataset, batch_size = 1, epoch with tqdm(total=100, position=2, desc="Accuracy") as tqdm_accuracy, tqdm(total=100, position=3, desc="Recall") as tqdm_recall, tqdm(total=100, position=4, desc="Mean Average Precision") as tqdm_mAP: for c_epoch in tqdm(range(start, epoch), position=0, desc="Epoch", leave=True): # release a mini-batch data - with tqdm(enumerate(loader), total=ceil(len(train_dataset) / batch_size), unit_scale=batch_size, position=1, desc="Step", leave=True) as tdqm_enumerated_loader: + with tqdm(enumerate(loader), total=ceil(len(train_dataset) / batch_size), unit_scale=batch_size, position=1, desc="Step") as tdqm_enumerated_loader: for step, (images, tags) in tdqm_enumerated_loader: # read images and labels device_image = images.to(train_device) @@ -89,10 +89,13 @@ def train(weights, classfile, train_dataset, val_dataset, batch_size = 1, epoch # evaluate the performance of current model mAP, mp, mr = return_mAP(model, val_dataset, classes) + tqdm_accuracy.reset() tqdm_accuracy.update(mp * 100) + tqdm_recall.reset() tqdm_recall.update(mr * 100) + tqdm_mAP.reset() tqdm_mAP.update(mAP * 100) - tqdm.write('Epoch: %d |mAP: %.4f' % (c_epoch, mAP)) + tqdm.write('Epoch: %d | mAP: %.4f' % (c_epoch, mAP)) # save if better if mAP >= max_mAP: if save_file: