diff --git a/.gitignore b/.gitignore index e34abed4fa..84a9dbbf2c 100644 --- a/.gitignore +++ b/.gitignore @@ -10,5 +10,6 @@ aiServer/runs/detect/exp/2021-08-05 13-11-50 .jpg /aiServer/TextReID/runs aiServer/crowdhuman_yolov5m.pt ai/.env -frontend/.env +frontend/.env server/src/main/java/com/capstone/server/dataInitializer/CCTVInitializer.java + diff --git a/ai/TextReID/datasets/cuhkpedes/clip_vocab_vit.npy b/ai/TextReID/datasets/cuhkpedes/clip_vocab_vit.npy deleted file mode 100644 index d45b29510d..0000000000 Binary files a/ai/TextReID/datasets/cuhkpedes/clip_vocab_vit.npy and /dev/null differ diff --git a/ai/TextReID/lib/data/datasets/__init__.py b/ai/TextReID/lib/data/datasets/__init__.py deleted file mode 100644 index e4ee7a6fc9..0000000000 --- a/ai/TextReID/lib/data/datasets/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .concat_dataset import ConcatDataset -from .cuhkpedes import CUHKPEDESDataset - -__all__ = ["ConcatDataset", "CUHKPEDESDataset"] diff --git a/ai/TextReID/lib/data/datasets/concat_dataset.py b/ai/TextReID/lib/data/datasets/concat_dataset.py deleted file mode 100644 index 87e054a6bc..0000000000 --- a/ai/TextReID/lib/data/datasets/concat_dataset.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -import bisect - -from torch.utils.data.dataset import ConcatDataset as _ConcatDataset - - -class ConcatDataset(_ConcatDataset): - """ - Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra - method for querying the sizes of the image - """ - - def get_idxs(self, idx): - dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) - if dataset_idx == 0: - sample_idx = idx - else: - sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] - return dataset_idx, sample_idx - - def get_id_info(self, idx): - dataset_idx, sample_idx = self.get_idxs(idx) - return self.datasets[dataset_idx].get_id_info(sample_idx) diff --git a/ai/TextReID/lib/data/datasets/cuhkpedes.py b/ai/TextReID/lib/data/datasets/cuhkpedes.py deleted file mode 100644 index 31707579d8..0000000000 --- a/ai/TextReID/lib/data/datasets/cuhkpedes.py +++ /dev/null @@ -1,70 +0,0 @@ -import json -import os - -import torch -from PIL import Image - -from lib.utils.caption import Caption - - -class CUHKPEDESDataset(torch.utils.data.Dataset): - def __init__( - self, - root, - ann_file, - use_onehot=True, - max_length=100, - transforms=None, - ): - self.root = root - self.use_onehot = use_onehot - self.max_length = max_length - self.transforms = transforms - - self.img_dir = os.path.join(self.root, "imgs") - # self.img_dir = self.root - - print("loading annotations into memory...") - dataset = json.load(open(ann_file, "r")) - self.dataset = dataset["annotations"] - - def __getitem__(self, index): - """ - Args: - index(int): Index - Returns: - tuple: (images, labels, captions) - """ - data = self.dataset[index] - - img_path = data["file_path"] - img = Image.open(os.path.join(self.img_dir, img_path)).convert("RGB") - - if self.use_onehot: - caption = data["onehot"] - caption = torch.tensor(caption) - caption = Caption([caption], max_length=self.max_length, padded=False) - else: - caption = data["sentence"] - caption = Caption(caption) - - caption.add_field("img_path", img_path) - - label = int(data["id"]) - label = torch.tensor(label) - caption.add_field("id", label) - - if self.transforms is not None: - img = self.transforms(img) - - query = data["sentence"] - - return img, caption, index, query - - def __len__(self): - return len(self.dataset) - - def get_id_info(self, index): - image_id = self.dataset[index]["image_id"] - pid = self.dataset[index]["id"] - return image_id, pid diff --git a/ai/TextReID/lib/data/encode/encoding.py b/ai/TextReID/lib/data/encode/encoding.py index 05133ccd92..ffe109ffcb 100644 --- a/ai/TextReID/lib/data/encode/encoding.py +++ b/ai/TextReID/lib/data/encode/encoding.py @@ -1,75 +1,75 @@ -import json -import re -import os - - -word_dict = {} # word (str) : encode (int) -max_onehot = -1 # 없는 단어를 새로 딕셔너리에 추가할 때 필요 - - -def load_word_dict(file_path): - global word_dict - global max_onehot - - if os.path.exists(file_path): - with open(file_path, "r") as file: - data = json.load(file) - word_dict = data["word_dict"] - max_onehot = data["max_onehot"] - else: - word_dict_path = os.path.dirname(os.path.abspath(__file__))+"/word_dict/test.json" - update_word_dict(word_dict_path) - - -def save_word_dict(file_path): - global word_dict - global max_onehot - - data = {"word_dict": word_dict, "max_onehot": max_onehot} - - with open(file_path, "w") as file: - json.dump(data, file) - - -def update_word_dict(file_path): - global word_dict - global max_onehot - - with open(file_path, "r") as file: - data = json.load(file) - - for i in range(len(data["annotations"])): - words = re.sub(r'[^a-zA-Z0-9\s]', '', data["annotations"][i]["sentence"]) - words = words.split() - for word, onehot in zip(words, data["annotations"][i]["onehot"]): - if onehot > max_onehot: - max_onehot = onehot - if word.lower() not in word_dict.keys(): - word_dict[word.lower()] = onehot - - -def encode(query): - global word_dict - global max_onehot - - output = [] - query = re.sub(r'[^a-zA-Z0-9\s]', ' ', query) - - for w in query.split(): - try: - output.append(word_dict[w.lower()]) - except KeyError: - print("Key %s not found in the dictionary." % w) - word_dict[w.lower()] = max_onehot + 1 - output.append(word_dict[w.lower()]) - max_onehot += 1 - - return output - - -def encoder(caption, file_path=os.path.dirname(os.path.abspath(__file__))+"/word_dict/annotations.json"): - load_word_dict(file_path) - caption = encode(caption) - save_word_dict(file_path) - +import json +import re +import os + + +word_dict = {} # word (str) : encode (int) +max_onehot = -1 # 없는 단어를 새로 딕셔너리에 추가할 때 필요 + + +def load_word_dict(file_path): + global word_dict + global max_onehot + + if os.path.exists(file_path): + with open(file_path, "r") as file: + data = json.load(file) + word_dict = data["word_dict"] + max_onehot = data["max_onehot"] + else: + word_dict_path = os.path.dirname(os.path.abspath(__file__))+"/word_dict/test.json" + update_word_dict(word_dict_path) + + +def save_word_dict(file_path): + global word_dict + global max_onehot + + data = {"word_dict": word_dict, "max_onehot": max_onehot} + + with open(file_path, "w") as file: + json.dump(data, file) + + +def update_word_dict(file_path): + global word_dict + global max_onehot + + with open(file_path, "r") as file: + data = json.load(file) + + for i in range(len(data["annotations"])): + words = re.sub(r'[^a-zA-Z0-9\s]', '', data["annotations"][i]["sentence"]) + words = words.split() + for word, onehot in zip(words, data["annotations"][i]["onehot"]): + if onehot > max_onehot: + max_onehot = onehot + if word.lower() not in word_dict.keys(): + word_dict[word.lower()] = onehot + + +def encode(query): + global word_dict + global max_onehot + + output = [] + query = re.sub(r'[^a-zA-Z0-9\s]', ' ', query) + + for w in query.split(): + try: + output.append(word_dict[w.lower()]) + except KeyError: + print("Key %s not found in the dictionary." % w) + word_dict[w.lower()] = max_onehot + 1 + output.append(word_dict[w.lower()]) + max_onehot += 1 + + return output + + +def encoder(caption, file_path=os.path.dirname(os.path.abspath(__file__))+"/word_dict/annotations.json"): + load_word_dict(file_path) + caption = encode(caption) + save_word_dict(file_path) + return caption \ No newline at end of file diff --git a/ai/TextReID/lib/data/metrics/evaluation.py b/ai/TextReID/lib/data/metrics/evaluation.py index bde5d46db2..60c63f4378 100644 --- a/ai/TextReID/lib/data/metrics/evaluation.py +++ b/ai/TextReID/lib/data/metrics/evaluation.py @@ -1,151 +1,151 @@ -import logging -import os - -import json -import numpy as np -import torch -import torch.nn.functional as F -from lib.config import cfg - -from lib.utils.logger import table_log - - -def rank(similarity, q_pids, g_pids, topk=[1, 5, 10], get_mAP=True): - max_rank = max(topk) - if get_mAP: - indices = torch.argsort(similarity, dim=1, descending=True) - else: - # acclerate sort with topk - _, indices = torch.topk( - similarity, k=max_rank, dim=1, largest=True, sorted=True - ) # q * topk - indices = indices.to(g_pids.device) - pred_labels = g_pids[indices] # q * k - matches = pred_labels.eq(q_pids.view(-1, 1)) # q * k - - all_cmc = matches[:, :max_rank].cumsum(1) - all_cmc[all_cmc > 1] = 1 - all_cmc = all_cmc.float().mean(0) * 100 - all_cmc = all_cmc[topk - 1] - - if not get_mAP: - return all_cmc, indices - - num_rel = matches.sum(1) # q - tmp_cmc = matches.cumsum(1) # q * k - tmp_cmc = [tmp_cmc[:, i] / (i + 1.0) for i in range(tmp_cmc.shape[1])] - tmp_cmc = torch.stack(tmp_cmc, 1) * matches - AP = tmp_cmc.sum(1) / num_rel # q - mAP = AP.mean() * 100 - return all_cmc, mAP, indices - - -def jaccard(a_list, b_list): - return float(len(set(a_list) & set(b_list))) / float(len(set(a_list) | set(b_list))) - - -def jaccard_mat(row_nn, col_nn): - jaccard_sim = np.zeros((row_nn.shape[0], col_nn.shape[0])) - # FIXME: need optimization - for i in range(row_nn.shape[0]): - for j in range(col_nn.shape[0]): - jaccard_sim[i, j] = jaccard(row_nn[i], col_nn[j]) - return torch.from_numpy(jaccard_sim) - - -def k_reciprocal(q_feats, g_feats, neighbor_num=5, alpha=0.05): - qg_sim = torch.matmul(q_feats, g_feats.t()) # q * g - gg_sim = torch.matmul(g_feats, g_feats.t()) # g * g - - qg_indices = torch.argsort(qg_sim, dim=1, descending=True) - gg_indices = torch.argsort(gg_sim, dim=1, descending=True) - - qg_nn = qg_indices[:, :neighbor_num] # q * n - gg_nn = gg_indices[:, :neighbor_num] # g * n - - jaccard_sim = jaccard_mat(qg_nn.cpu().numpy(), gg_nn.cpu().numpy()) # q * g - jaccard_sim = jaccard_sim.to(qg_sim.device) - return alpha * jaccard_sim # q * g - - -def get_unique(image_ids): - keep_idx = {} - for idx, image_id in enumerate(image_ids): - if image_id not in keep_idx.keys(): - keep_idx[image_id] = idx - return torch.tensor(list(keep_idx.values())) - - -def evaluation( - dataset, - predictions, - output_folder, - topk, - cap, - save_data=True, - rerank=True, - search_num=0, - save_folder = "./output/output.json" -): - logger = logging.getLogger("PersonSearch.inference") - data_dir = os.path.join(output_folder, "inference_data.npz") - - if predictions is None: - inference_data = np.load(data_dir) - logger.info("Load inference data from {}".format(data_dir)) - image_pid = torch.tensor(inference_data["image_pid"]) - text_pid = torch.tensor(inference_data["text_pid"]) - similarity = torch.tensor(inference_data["similarity"]) - if rerank: - rvn_mat = torch.tensor(inference_data["rvn_mat"]) - rtn_mat = torch.tensor(inference_data["rtn_mat"]) - else: - image_ids, pids = [], [] - image_global, text_global = [], [] - - # FIXME: need optimization - for idx, prediction in predictions.items(): - image_id, pid = dataset.get_id_info(idx) - image_ids.append(image_id) - pids.append(pid) - image_global.append(prediction[0]) - if len(prediction) == 2: - # text query를 하나만 넣었으므로, text emgedding은 배치의 제일 처음 이미지에만 들어감 - # 왜냐하면 유사도 검사 시 배치 별로 검사를 했으니까 - text_global.append(prediction[1]) - - pids = list(map(int, pids)) - image_pid = torch.tensor(pids) - text_pid = torch.tensor(pids) - image_global = torch.stack(image_global, dim=0) - text_global = torch.stack(text_global, dim=0) - - keep_idx = get_unique(image_ids) - image_global = image_global[keep_idx] - image_pid = image_pid[keep_idx] - - image_global = F.normalize(image_global, p=2, dim=1) - text_global = F.normalize(text_global, p=2, dim=1) - - similarity = torch.matmul(text_global, image_global.t()) - - # top 10 results 반환 - sorted_indices = torch.argsort(similarity[0], descending=True) - sorted_values = similarity[0][sorted_indices] - top_k = cfg.TEST.TOP_K - write = [cap[0]] # 저장할 output - for index, value in zip(sorted_indices[:top_k], sorted_values[:top_k]): - # image_id, pid = dataset.get_id_info(idx) - img, caption, idx, query = dataset.__getitem__(index) - img_path = caption.get_field("img_path") - # print(f"Index: {index}, Similarity: {value}, pid: {pid}") - dict = {"img_path": img_path, "Similarity": value.item()} - write.append(dict) - - - #저장 폴더 생성 - if not os.path.exists(os.path.dirname(save_folder)): - os.makedirs(os.path.dirname(save_folder), exist_ok=True) - - with open(save_folder, "w", encoding='utf-8') as f: +import logging +import os + +import json +import numpy as np +import torch +import torch.nn.functional as F +from lib.config import cfg + +from lib.utils.logger import table_log + + +def rank(similarity, q_pids, g_pids, topk=[1, 5, 10], get_mAP=True): + max_rank = max(topk) + if get_mAP: + indices = torch.argsort(similarity, dim=1, descending=True) + else: + # acclerate sort with topk + _, indices = torch.topk( + similarity, k=max_rank, dim=1, largest=True, sorted=True + ) # q * topk + indices = indices.to(g_pids.device) + pred_labels = g_pids[indices] # q * k + matches = pred_labels.eq(q_pids.view(-1, 1)) # q * k + + all_cmc = matches[:, :max_rank].cumsum(1) + all_cmc[all_cmc > 1] = 1 + all_cmc = all_cmc.float().mean(0) * 100 + all_cmc = all_cmc[topk - 1] + + if not get_mAP: + return all_cmc, indices + + num_rel = matches.sum(1) # q + tmp_cmc = matches.cumsum(1) # q * k + tmp_cmc = [tmp_cmc[:, i] / (i + 1.0) for i in range(tmp_cmc.shape[1])] + tmp_cmc = torch.stack(tmp_cmc, 1) * matches + AP = tmp_cmc.sum(1) / num_rel # q + mAP = AP.mean() * 100 + return all_cmc, mAP, indices + + +def jaccard(a_list, b_list): + return float(len(set(a_list) & set(b_list))) / float(len(set(a_list) | set(b_list))) + + +def jaccard_mat(row_nn, col_nn): + jaccard_sim = np.zeros((row_nn.shape[0], col_nn.shape[0])) + # FIXME: need optimization + for i in range(row_nn.shape[0]): + for j in range(col_nn.shape[0]): + jaccard_sim[i, j] = jaccard(row_nn[i], col_nn[j]) + return torch.from_numpy(jaccard_sim) + + +def k_reciprocal(q_feats, g_feats, neighbor_num=5, alpha=0.05): + qg_sim = torch.matmul(q_feats, g_feats.t()) # q * g + gg_sim = torch.matmul(g_feats, g_feats.t()) # g * g + + qg_indices = torch.argsort(qg_sim, dim=1, descending=True) + gg_indices = torch.argsort(gg_sim, dim=1, descending=True) + + qg_nn = qg_indices[:, :neighbor_num] # q * n + gg_nn = gg_indices[:, :neighbor_num] # g * n + + jaccard_sim = jaccard_mat(qg_nn.cpu().numpy(), gg_nn.cpu().numpy()) # q * g + jaccard_sim = jaccard_sim.to(qg_sim.device) + return alpha * jaccard_sim # q * g + + +def get_unique(image_ids): + keep_idx = {} + for idx, image_id in enumerate(image_ids): + if image_id not in keep_idx.keys(): + keep_idx[image_id] = idx + return torch.tensor(list(keep_idx.values())) + + +def evaluation( + dataset, + predictions, + output_folder, + topk, + cap, + save_data=True, + rerank=True, + search_num=0, + save_folder = "./output/output.json" +): + logger = logging.getLogger("PersonSearch.inference") + data_dir = os.path.join(output_folder, "inference_data.npz") + + if predictions is None: + inference_data = np.load(data_dir) + logger.info("Load inference data from {}".format(data_dir)) + image_pid = torch.tensor(inference_data["image_pid"]) + text_pid = torch.tensor(inference_data["text_pid"]) + similarity = torch.tensor(inference_data["similarity"]) + if rerank: + rvn_mat = torch.tensor(inference_data["rvn_mat"]) + rtn_mat = torch.tensor(inference_data["rtn_mat"]) + else: + image_ids, pids = [], [] + image_global, text_global = [], [] + + # FIXME: need optimization + for idx, prediction in predictions.items(): + image_id, pid = dataset.get_id_info(idx) + image_ids.append(image_id) + pids.append(pid) + image_global.append(prediction[0]) + if len(prediction) == 2: + # text query를 하나만 넣었으므로, text emgedding은 배치의 제일 처음 이미지에만 들어감 + # 왜냐하면 유사도 검사 시 배치 별로 검사를 했으니까 + text_global.append(prediction[1]) + + pids = list(map(int, pids)) + image_pid = torch.tensor(pids) + text_pid = torch.tensor(pids) + image_global = torch.stack(image_global, dim=0) + text_global = torch.stack(text_global, dim=0) + + keep_idx = get_unique(image_ids) + image_global = image_global[keep_idx] + image_pid = image_pid[keep_idx] + + image_global = F.normalize(image_global, p=2, dim=1) + text_global = F.normalize(text_global, p=2, dim=1) + + similarity = torch.matmul(text_global, image_global.t()) + + # top 10 results 반환 + sorted_indices = torch.argsort(similarity[0], descending=True) + sorted_values = similarity[0][sorted_indices] + top_k = cfg.TEST.TOP_K + write = [cap[0]] # 저장할 output + for index, value in zip(sorted_indices[:top_k], sorted_values[:top_k]): + # image_id, pid = dataset.get_id_info(idx) + img, caption, idx, query = dataset.__getitem__(index) + img_path = caption.get_field("img_path") + # print(f"Index: {index}, Similarity: {value}, pid: {pid}") + dict = {"img_path": img_path, "Similarity": value.item()} + write.append(dict) + + + #저장 폴더 생성 + if not os.path.exists(os.path.dirname(save_folder)): + os.makedirs(os.path.dirname(save_folder), exist_ok=True) + + with open(save_folder, "w", encoding='utf-8') as f: json.dump(write, f, ensure_ascii=False, indent=4) \ No newline at end of file diff --git a/ai/TextReID/lib/engine/inference.py b/ai/TextReID/lib/engine/inference.py index cb775af6dd..d719e24b47 100644 --- a/ai/TextReID/lib/engine/inference.py +++ b/ai/TextReID/lib/engine/inference.py @@ -1,111 +1,111 @@ -import datetime -import logging -import os -import time -from collections import defaultdict - -import torch -from tqdm import tqdm - -from lib.data.metrics import evaluation -from lib.utils.comm import all_gather, is_main_process, synchronize - -from lib.utils.caption import Caption - -from lib.data.encode.encoding import encoder - - -def compute_on_dataset(model, data_loader, cap, device, query): - model.eval() - results_dict = defaultdict(list) - print("query: ", query) - caption = query - cap.append(caption) - caption = encoder(caption) - caption = Caption([torch.tensor(caption)]) - for batch in tqdm(data_loader): - images, captions, image_ids = batch - images = images.to(device) - captions = [caption.to(device)] - with torch.no_grad(): - output = model(images, captions) - for result in output: - for img_id, pred in zip(image_ids, result): - results_dict[img_id].append(pred) - return results_dict, cap - - -def _accumulate_predictions_from_multiple_gpus(predictions_per_gpu): - all_predictions = all_gather(predictions_per_gpu) - if not is_main_process(): - return - # merge the list of dicts - predictions = {} - for p in all_predictions: - predictions.update(p) - # convert a dict where the key is the index in a list - image_ids = list(sorted(predictions.keys())) - if len(image_ids) != image_ids[-1] + 1: - logger = logging.getLogger("PersonSearch.inference") - logger.warning( - "Number of images that were gathered from multiple processes is not " - "a contiguous set. Some images might be missing from the evaluation" - ) - return predictions - - -def inference( - model, - data_loader, - dataset_name="cuhkpedes-test", - device="cuda", - output_folder="", - save_data=True, - rerank=True, - search_num=0, - query ="", - save_folder = "./output/output.json" -): - logger = logging.getLogger("PersonSearch.inference") - dataset = data_loader.dataset - logger.info( - "Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)) - ) - - predictions = None - if not os.path.exists(os.path.join(output_folder, "inference_data.npz")): - # convert to a torch.device for efficiency - device = torch.device(device) - num_devices = ( - torch.distributed.get_world_size() - if torch.distributed.is_initialized() - else 1 - ) - start_time = time.time() - - predictions, cap = compute_on_dataset(model, data_loader, [], device,query) - # wait for all processes to complete before measuring the time - synchronize() - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=total_time)) - logger.info( - "Total inference time: {} ({} s / img per device, on {} devices)".format( - total_time_str, total_time * num_devices / len(dataset), num_devices - ) - ) - predictions = _accumulate_predictions_from_multiple_gpus(predictions) - - if not is_main_process(): - return - - return evaluation( - dataset=dataset, - predictions=predictions, - output_folder=output_folder, - save_data=save_data, - rerank=rerank, - topk=[1, 5, 10], - cap=cap, - search_num=search_num, - save_folder = save_folder - ) +import datetime +import logging +import os +import time +from collections import defaultdict + +import torch +from tqdm import tqdm + +from lib.data.metrics import evaluation +from lib.utils.comm import all_gather, is_main_process, synchronize + +from lib.utils.caption import Caption + +from lib.data.encode.encoding import encoder + + +def compute_on_dataset(model, data_loader, cap, device, query): + model.eval() + results_dict = defaultdict(list) + print("query: ", query) + caption = query + cap.append(caption) + caption = encoder(caption) + caption = Caption([torch.tensor(caption)]) + for batch in tqdm(data_loader): + images, captions, image_ids = batch + images = images.to(device) + captions = [caption.to(device)] + with torch.no_grad(): + output = model(images, captions) + for result in output: + for img_id, pred in zip(image_ids, result): + results_dict[img_id].append(pred) + return results_dict, cap + + +def _accumulate_predictions_from_multiple_gpus(predictions_per_gpu): + all_predictions = all_gather(predictions_per_gpu) + if not is_main_process(): + return + # merge the list of dicts + predictions = {} + for p in all_predictions: + predictions.update(p) + # convert a dict where the key is the index in a list + image_ids = list(sorted(predictions.keys())) + if len(image_ids) != image_ids[-1] + 1: + logger = logging.getLogger("PersonSearch.inference") + logger.warning( + "Number of images that were gathered from multiple processes is not " + "a contiguous set. Some images might be missing from the evaluation" + ) + return predictions + + +def inference( + model, + data_loader, + dataset_name="cuhkpedes-test", + device="cuda", + output_folder="", + save_data=True, + rerank=True, + search_num=0, + query ="", + save_folder = "./output/output.json" +): + logger = logging.getLogger("PersonSearch.inference") + dataset = data_loader.dataset + logger.info( + "Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)) + ) + + predictions = None + if not os.path.exists(os.path.join(output_folder, "inference_data.npz")): + # convert to a torch.device for efficiency + device = torch.device(device) + num_devices = ( + torch.distributed.get_world_size() + if torch.distributed.is_initialized() + else 1 + ) + start_time = time.time() + + predictions, cap = compute_on_dataset(model, data_loader, [], device,query) + # wait for all processes to complete before measuring the time + synchronize() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + logger.info( + "Total inference time: {} ({} s / img per device, on {} devices)".format( + total_time_str, total_time * num_devices / len(dataset), num_devices + ) + ) + predictions = _accumulate_predictions_from_multiple_gpus(predictions) + + if not is_main_process(): + return + + return evaluation( + dataset=dataset, + predictions=predictions, + output_folder=output_folder, + save_data=save_data, + rerank=rerank, + topk=[1, 5, 10], + cap=cap, + search_num=search_num, + save_folder = save_folder + ) diff --git a/ai/requirements.txt b/ai/requirements.txt old mode 100755 new mode 100644 diff --git a/ai/yolov5-crowdhuman/data/scripts/get_coco.sh b/ai/yolov5-crowdhuman/data/scripts/get_coco.sh old mode 100755 new mode 100644 diff --git a/ai/yolov5-crowdhuman/test/20210905-162400.mp4 b/ai/yolov5-crowdhuman/test/20210905-162400.mp4 old mode 100755 new mode 100644 diff --git a/ai/yolov5_crowdhuman/.gitignore 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b/frontend/public/index.html index 0c54fd77d1..23659f23c8 100644 --- a/frontend/public/index.html +++ b/frontend/public/index.html @@ -19,7 +19,7 @@ --> + src="//dapi.kakao.com/v2/maps/sdk.js?appkey=%REACT_APP_KAKAOMAP_KEY%&libraries=services,clusterer"> React App diff --git a/frontend/src/assets/icons/Marker_d.svg b/frontend/src/assets/icons/Marker_d.svg new file mode 100644 index 0000000000..1d6f3637dc --- /dev/null +++ b/frontend/src/assets/icons/Marker_d.svg @@ -0,0 +1,10 @@ + + + + + + + + + + diff --git a/frontend/src/assets/icons/cctvMarker.svg b/frontend/src/assets/icons/cctvMarker.svg new file mode 100644 index 0000000000..ddacc32a1a --- /dev/null +++ b/frontend/src/assets/icons/cctvMarker.svg @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/frontend/src/assets/icons/cctvMarker_b.svg b/frontend/src/assets/icons/cctvMarker_b.svg new file mode 100644 index 0000000000..bfc5cc0b45 --- /dev/null +++ b/frontend/src/assets/icons/cctvMarker_b.svg @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/frontend/src/assets/icons/cctvMarker_d.svg b/frontend/src/assets/icons/cctvMarker_d.svg new file mode 100644 index 0000000000..698b831c4d --- /dev/null +++ b/frontend/src/assets/icons/cctvMarker_d.svg @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/frontend/src/assets/icons/cctvMarker_g.svg b/frontend/src/assets/icons/cctvMarker_g.svg new file mode 100644 index 0000000000..87bc193203 --- /dev/null +++ b/frontend/src/assets/icons/cctvMarker_g.svg @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/frontend/src/assets/icons/cctvMarker_y.svg b/frontend/src/assets/icons/cctvMarker_y.svg new file mode 100644 index 0000000000..99464849e6 --- /dev/null +++ b/frontend/src/assets/icons/cctvMarker_y.svg @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/frontend/src/assets/icons/cctvMarker_yy.svg b/frontend/src/assets/icons/cctvMarker_yy.svg new file mode 100644 index 0000000000..7333f7788e --- /dev/null +++ b/frontend/src/assets/icons/cctvMarker_yy.svg @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/frontend/src/assets/icons/centerMarker.svg b/frontend/src/assets/icons/centerMarker.svg new file mode 100644 index 0000000000..780d8f29cf --- /dev/null +++ b/frontend/src/assets/icons/centerMarker.svg @@ -0,0 +1,4 @@ + + + + diff --git a/frontend/src/assets/icons/locationMarker.svg b/frontend/src/assets/icons/locationMarker.svg new file mode 100644 index 0000000000..817bce8275 --- /dev/null +++ b/frontend/src/assets/icons/locationMarker.svg @@ -0,0 +1,3 @@ + + + diff --git a/frontend/src/assets/icons/rangeMarker_activate.svg b/frontend/src/assets/icons/rangeMarker_activate.svg new file mode 100644 index 0000000000..dfe91f7a74 --- /dev/null +++ b/frontend/src/assets/icons/rangeMarker_activate.svg @@ -0,0 +1,3 @@ + + + diff --git a/frontend/src/assets/icons/rangeMarker_disabled.svg b/frontend/src/assets/icons/rangeMarker_disabled.svg new file mode 100644 index 0000000000..7726f782f3 --- /dev/null +++ b/frontend/src/assets/icons/rangeMarker_disabled.svg @@ -0,0 +1,3 @@ + + + diff --git a/frontend/src/components/addMisingPerson/IntelligentSearchInfo.js b/frontend/src/components/addMisingPerson/IntelligentSearchInfo.js index d50169da8b..c83bd42eec 100644 --- a/frontend/src/components/addMisingPerson/IntelligentSearchInfo.js +++ b/frontend/src/components/addMisingPerson/IntelligentSearchInfo.js @@ -22,7 +22,7 @@ const rangeConfig = { }, ], }; -export const IntelligentSearchInfo = ({ form }) => { +export const IntelligentSearchInfo = ({ form, getLocation }) => { return ( @@ -35,7 +35,7 @@ export const IntelligentSearchInfo = ({ form }) => { /> - + ); diff --git a/frontend/src/components/common/SearchBox.js b/frontend/src/components/common/SearchBox.js index b017b043dd..81fc2264ee 100644 --- a/frontend/src/components/common/SearchBox.js +++ b/frontend/src/components/common/SearchBox.js @@ -1,14 +1,28 @@ +/*global kakao*/ import styled from "styled-components"; import { Input, Modal } from "antd"; import { SearchOutlined } from "@ant-design/icons"; import DaumPostcode from "react-daum-postcode"; -import { useState } from "react"; +import { useEffect, useRef, useState } from "react"; +import { debounce } from "lodash"; + +import { Circle, Map, MapMarker } from "react-kakao-maps-sdk"; //도로명 주소 검색 -export const SeacrchBox = ({ title, form, name }) => { +export const SeacrchBox = ({ title, form, name, getLocation }) => { + const mapRef = useRef(); const [openPostcode, setOpenPostcode] = useState(false); const [location, setLocation] = useState(""); + const [markerPosition, setMarkerPosition] = useState({}); + + useEffect(() => { + console.log("selectAddress", location); + if (location) { + handleGeocoder(); + } + }, [location]); + const handle = { // 버튼 클릭 이벤트 clickButton: () => { @@ -19,10 +33,36 @@ export const SeacrchBox = ({ title, form, name }) => { selectAddress: (data) => { setLocation(data.address); form.setFieldsValue({ [name]: data.address }); // 주소 정보를 Form.Item에 직접 설정 + console.log("selectAddress", data.address); + }, + + // 선택 완료 이벤트 + clickOK: () => { + getLocation(markerPosition); setOpenPostcode(false); }, }; + const handleGeocoder = () => { + const geocoder = new kakao.maps.services.Geocoder(); + var callback = function (result, status) { + if (status === kakao.maps.services.Status.OK) { + console.log(result); + setMarkerPosition({ + lat: result[0].y, + lng: result[0].x, + }); + const map = mapRef.current; + if (map) { + console.log("markerPosition", markerPosition); + map.setCenter(new kakao.maps.LatLng(result[0].y, result[0].x)); + map.setLevel(6); + } + } + }; + geocoder.addressSearch(location, callback); + }; + return ( @@ -31,19 +71,46 @@ export const SeacrchBox = ({ title, form, name }) => { - setOpenPostcode(false)} + onOk={handle.clickOK} onCancel={() => setOpenPostcode(false)}> - - + + + + + + + + {markerPosition.lat && markerPosition.lng && ( + <> + + + + )} + + + + ); }; @@ -74,3 +141,21 @@ const SearchIconWrapper = styled.div` padding: 0.9rem; border-left: 0.1rem solid #d9d9d9; `; + +const ModalWrapper = styled(Modal)``; +const ModalContent = styled.div` + display: flex; + flex-direction: row; + width: 100%; + height: 100%; +`; + +const DaumPostcodeWrapper = styled.div` + width: 50%; + height: 100%; +`; + +const MapWrapper = styled.div` + width: 50%; + height: 100%; +`; diff --git a/frontend/src/components/missingPersonReport/ReportMain.js b/frontend/src/components/missingPersonReport/ReportMain.js index 01ee21df4d..ec3368fe7b 100644 --- a/frontend/src/components/missingPersonReport/ReportMain.js +++ b/frontend/src/components/missingPersonReport/ReportMain.js @@ -7,12 +7,23 @@ import { IntelligentReportList } from "./IntelligentReportList"; import { ReportTabs } from "./ReportTabs"; import { ReportResultImages } from "./ReportResultImages"; -export const ReportMain = ({ data, step, history, step1data, onClick }) => { +export const ReportMain = ({ + data, + step, + history, + firstdata, + betweenData, + secondData, + onClick, + firstCCTVData, + betweenCCTVData, + secondCCTVData, +}) => { //console.log("ReportMain_id: " + id.id); console.log("ReportMain_data: ", data); console.log("ReportMain_step: ", step); console.log("ReportMain_history: ", history); - console.log("ReportMain_step1data: ", step1data); + console.log("ReportMain_step1data: ", firstdata); return ( @@ -36,11 +47,19 @@ export const ReportMain = ({ data, step, history, step1data, onClick }) => { - + - + diff --git a/frontend/src/components/missingPersonReport/ReportMap.js b/frontend/src/components/missingPersonReport/ReportMap.js index 5625c3cffd..f509af37db 100644 --- a/frontend/src/components/missingPersonReport/ReportMap.js +++ b/frontend/src/components/missingPersonReport/ReportMap.js @@ -1,9 +1,343 @@ +/*global kakao*/ + import styled from "styled-components"; -import { Map, MapMarker } from "react-kakao-maps-sdk"; -export const ReportMap = () => { +import { useEffect, useRef, useState } from "react"; +import { Circle, CustomOverlayMap, Map, MapMarker, useMap } from "react-kakao-maps-sdk"; +import { Button, Divider, FloatButton, List, Segmented, Switch, Tooltip } from "antd"; +import Icon, { CloseOutlined, PlusOutlined, MinusOutlined } from "@ant-design/icons"; +import CenterMarker from "../../assets/icons/centerMarker.svg"; +import ActivateRangeMarker from "../../assets/icons/rangeMarker_activate.svg"; +import DisabledRangeMarker from "../../assets/icons/rangeMarker_disabled.svg"; +import LocationMarker from "../../assets/icons/locationMarker.svg"; +import CCTVMarker from "../../assets/icons/cctvMarker_yy.svg"; +import CCTVMarkerBlue from "../../assets/icons/cctvMarker_b.svg"; +import CCTVMarkerDisabled from "../../assets/icons/Marker_d.svg"; + +/*실종자 리포트 맵 컴포넌트 */ +export const ReportMap = ({ start, end, searchRange, step, firstData, betweenData, secondData }) => { + const mapRef = useRef(); + const [location, setLocation] = useState(""); + + /*Overlay filter*/ + const [showStep, setShowStep] = useState("first"); + const [showRange, setShowRange] = useState(true); + + /*Marker State*/ + const [firstState, setFirstState] = useState(true); + const [betweenState, setBetweenState] = useState(false); + const [secondState, setSecondState] = useState(false); + + /*position */ + const [rangePosition, setRangePosition] = useState({ + lat: 37.410826, + lng: 126.894317, + }); + const [firstPosition, setFirstPosition] = useState([]); + const [betweenPosition, setBetweenPosition] = useState([]); + const [secondPosition, setSecondPosition] = useState([]); + const [filterPosition, setFilterPosition] = useState([]); + + useEffect(() => { + console.log("ReportMap", firstData); + if (searchRange) { + setRangePosition({ + lat: searchRange.latitude, + lng: searchRange.longitude, + }); + handleCenter(); + } + if (firstData) { + setFirstPosition(firstData); + setFirstState(true); + } + if (betweenData) { + console.log("betweenData", betweenData); + setBetweenPosition(betweenData); + const excludedIds = new Set(betweenData.map((item) => item.id)); + const filteredMarkers = firstData.filter((marker) => !excludedIds.has(marker.id)); + setFilterPosition(filteredMarkers); + setBetweenState(true); + } + if (secondData) { + console.log("secondData", secondData); + setSecondPosition(secondData); + setSecondState(true); + } + }, [searchRange, firstData, betweenData, secondData]); + + useEffect(() => { + console.log("step1Position", firstPosition); + handleLocation(); + }, [rangePosition]); + + const handleCenter = () => { + const map = mapRef.current; + if (map) { + console.log("markerPosition", rangePosition); + map.setCenter(new kakao.maps.LatLng(rangePosition.lat, rangePosition.lng)); + map.setLevel(4); + } + console.log("handleCenter", rangePosition); + }; + + const handleLevel = (type) => { + const map = mapRef.current; + if (!map) return; + + if (type === "increase") { + map.setLevel(map.getLevel() + 1); + } else { + type === "decrease"; + map.setLevel(map.getLevel() - 1); + } + }; + + const handleLocation = () => { + const geocoder = new kakao.maps.services.Geocoder(); + var coord = new kakao.maps.LatLng(rangePosition.lat, rangePosition.lng); + + console.log("handleLocation", rangePosition); + var callback = function (result, status) { + if (status === kakao.maps.services.Status.OK) { + console.log("handleLocation", result); + if (result[0].road_address) { + setLocation(result[0].road_address.address_name); + } + console.log("location", location); + } + }; + geocoder.coord2Address(coord.getLng(), coord.getLat(), callback); + }; + + const Item = ({ item }) => { + return ( + + + + ); + }; + + const EventMarkerContainer = ({ position, images, markerStyle }) => { + const map = useMap(); + const [isVisible, setIsVisible] = useState(false); + + return ( + <> + { + map.panTo(marker.getPosition()); + setIsVisible(true); + }} + image={{ + src: markerStyle, // 마커이미지의 주소입니다 + size: { + width: 30, + height: 40, + }, // 마커이미지의 크기입니다 + }} + /> + {isVisible && ( + + + +

이미지 목록

+ + + 탐색 결과는 최신순으로 정렬됩니다. + + { + console.log(page); + }, + pageSize: 3, + size: "small", + position: "bottom", + }} + dataSource={images} + renderItem={(item) => } + /> + + + )} + + ); + }; + + const FilterMarkerContainer = ({ marker }) => { + const [isVisible, setIsVisible] = useState(false); + return ( + <> + setIsVisible(true)} + // onMouseOut={() => setIsVisible(false)} + image={{ + src: CCTVMarkerDisabled, + size: { + width: 18, + height: 24, + }, + }} + /> + {/* {isVisible && ( + + +

1차 탐색 결과 중 선별된 이미지가 없는 cctv입니다.

+ + + )} */} + + ); + }; + return ( - + + {showRange && rangePosition.lat && rangePosition.lng && ( + <> + + + + )} + {showStep == "first" && + firstState && + firstPosition && + firstPosition.map( + (position) => ( + console.log("position", position), + ( + + ) + ), + )} + + {showStep == "between" && betweenState && betweenPosition && ( + <> + {betweenPosition.map( + (position) => ( + console.log("position", position), + ( + + ) + ), + )} + {filterPosition && + filterPosition.map((position) => )} + + )} + {showStep == "second" && + secondState && + secondPosition && + secondPosition.map( + (position) => ( + console.log("position", position), + ( + + ) + ), + )} + + + { + handleCenter(); + }}> + +

{location}

+ + { + setShowStep(value); + handleCenter(); + }} + /> + + + + { + setShowRange(!showRange); + }}> + {showRange ? : } + + + + handleLevel("decrease")}> + + {/* */} + + + handleLevel("increase")}> + {/* */} + + + + ); }; @@ -13,7 +347,196 @@ const StReportMap = styled.div` flex-direction: column; flex: 1; flex-grow: 1; + position: relative; + width: 100%; + height: 100%; + /* max-width: 90rem; */ +`; + +const ContentsContainer = styled.div` + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + width: 37.3rem; + height: 30rem; + padding: 1rem; + background-color: white; + border-radius: 1rem; + + overflow-y: hidden; + cursor: pointer; +`; + +const TopContainer = styled.div` + display: flex; + flex-direction: row; + justify-content: space-between; + align-items: center; + width: 100%; + p { + font-size: 1.5rem; + } +`; +const ExplainText = styled.div` + display: flex; + justify-content: start; + width: 100%; + font-size: 1.2rem; + color: #8b8b8b; +`; + +const ImageList = styled(List)` width: 100%; height: 100%; - max-width: 90rem; +`; + +const ItemContainer = styled.div` + display: flex; + flex-direction: column; + align-items: center; + margin-top: 0.9rem; + + p { + @media all and (max-width: 1537px) { + font-size: 1rem; + } + font-size: 1.3rem; + } +`; + +const ItemImage = styled.img` + /* width: 14.4rem; + height: 23.8rem; */ + width: 11.52rem; + height: 19.04rem; + margin-right: 0.5rem; + @media all and (max-width: 1537px) { + width: 7.2rem; + height: 11.9rem; + } +`; + +const OverlayTopContainer = styled.div` + display: flex; + flex-direction: row; + + position: absolute; + z-index: 1; + top: 1rem; + left: 1rem; +`; + +const OverlaySideContainer = styled.div` + display: flex; + flex-direction: column; + + position: absolute; + z-index: 1; + top: 10rem; + right: 1rem; +`; + +const OverlayTooltip = styled(Tooltip)``; + +const OverlayButtonStyle = ` + display: flex; + margin-bottom: 1rem; + border-radius: 0.4rem; + background-color: white; + box-shadow: 0px 0px 5px 0px rgba(0, 0, 0, 0.1);`; + +const SingleButton = styled.div` + ${OverlayButtonStyle} + + width: 3.5rem; + height: 3.5rem; + justify-content: center; +`; + +const GroupButton = styled.div` + ${OverlayButtonStyle} + + width: 3.5rem; + height: 7rem; + flex-direction: column; + justify-content: space-between; +`; + +const ButtonWrapper = styled.div` + display: flex; + flex-direction: row; + justify-content: center; + width: 100%; + height: 3.5rem; +`; + +const OverlayLocationWrapper = styled.div` + ${OverlayButtonStyle} + flex-direction:row; + justify-content: start; + align-items: center; + /* width: 25rem; */ + height: 4rem; + padding: 0 1.2rem 0 1rem; + margin-right: 1rem; + opacity: 0.9; + + cursor: pointer; + &:hover { + color: rgba(0, 0, 0, 0.88); + opacity: 1; + } + + img { + width: 2rem; + margin-right: 0.2rem; + } + p { + color: #555555; + } +`; + +const OverlaySegmented = styled(Segmented)` + height: 4rem; + &.ant-segmented { + display: flex; + justify-content: center; + align-items: center; + height: 4rem; + background-color: white; + } + + &.ant-segmented .ant-segmented-item-label { + display: flex; + justify-content: center; + align-items: center; + height: 3.4rem; + } + + &.ant-segmented .ant-segmented-item-selected { + background-color: #1890ff; + color: white; + } +`; + +const IconWrapper = styled(Icon)` + color: #555555; + &:focus { + color: rgba(0, 0, 0, 0.5); + } + &:active { + color: rgba(0, 0, 0, 0.5); + } +`; + +const CustomInfoWindow = styled.div` + display: flex; + flex-wrap: wrap; + width: 20rem; + height: 4rem; + border-radius: 1rem; + + background-color: white; + opacity: 0.9; `; diff --git a/frontend/src/components/missingPersonReport/ReportTabs.js b/frontend/src/components/missingPersonReport/ReportTabs.js index 07ae266002..3f01dc55cc 100644 --- a/frontend/src/components/missingPersonReport/ReportTabs.js +++ b/frontend/src/components/missingPersonReport/ReportTabs.js @@ -27,14 +27,14 @@ const operations = ( /> ); -export const ReportTabs = ({ id, step1data }) => { +export const ReportTabs = ({ id, firstdata, betweenData, secondData }) => { console.log("ReportTabs_id: " + id); const [data1, setData1] = useState([]); const [dataBetween, setdataBetween] = useState([]); /* todo 이미지 고른거 가져오기*/ const [data2, setData2] = useState([]); useEffect(() => { - setData1(step1data); - }, [step1data]); + setData1(firstdata); + }, [firstdata]); console.log("data1", data1); console.log("data2", data2); const items = [ diff --git a/frontend/src/core/api/index.js b/frontend/src/core/api/index.js index 16c048665f..b0b235ab9a 100644 --- a/frontend/src/core/api/index.js +++ b/frontend/src/core/api/index.js @@ -151,6 +151,24 @@ export const getBetweenResultImg = async (page, id) => { return data; }; +/* 탐색 결과에 대한 cctv 위치 (Get)*/ +export const getCCTVResult = async (id, step) => { + const data = axios + .get(`${process.env.REACT_APP_API_ROOT}/api/missing-people/${id}/mapposition?step=${step}`, { + headers: { "Content-Type": "application/json" }, + }) + .then(function (response) { + return response.data; + }) + .catch(function (e) { + // 실패 시 처리 + console.error(e); + console.log(e.response.data); + alert("cctv 위치 가져오기 실패. 재시도해주세요."); + }); + return data; +}; + /*의뢰인용 메인 - 실종자 정보 (Get) */ export const getGuardianMissingPerson = async () => { const data = axios diff --git a/frontend/src/pages/AddMissingPersonPage.js b/frontend/src/pages/AddMissingPersonPage.js index 0b10e795ff..b77b8d80e5 100644 --- a/frontend/src/pages/AddMissingPersonPage.js +++ b/frontend/src/pages/AddMissingPersonPage.js @@ -7,6 +7,7 @@ import { WearingInfo } from "../components/addMisingPerson/WearingInfo"; import { postMissingPerson } from "../core/api"; import { useNavigate } from "react-router-dom"; import { AnnotationInfo } from "../components/addMisingPerson/AnnotationInfo"; +import { useState } from "react"; const validateMessages = { required: "필수 항목입니다!", @@ -21,7 +22,15 @@ const validateMessages = { function AddMissingPersonPage() { const [form] = Form.useForm(); + const [latlng, setLatlng] = useState({}); + const navigate = useNavigate(); + + const getLocation = (latlng) => { + setLatlng(latlng); + console.log("latlng", latlng); + }; + const onFinish = (fieldsValue) => { console.log("gender", fieldsValue["user"]["gender"]); const values = { @@ -49,8 +58,8 @@ function AddMissingPersonPage() { fieldsValue["searchPeriod"][0].format("YYYY-MM-DD") + "T" + fieldsValue["searchPeriod"][0].format("HH:mm"), endTime: fieldsValue["searchPeriod"][1].format("YYYY-MM-DD") + "T" + fieldsValue["searchPeriod"][1].format("HH:mm"), - latitude: 37.610767, - longitude: 126.996967, + latitude: latlng["lat"], + longitude: latlng["lng"], locationAddress: fieldsValue["searchLocation"] || "서울 성북구 정릉로 77", shoesColor: "빨강", missingPeopleType: "아동", @@ -92,7 +101,7 @@ function AddMissingPersonPage() { 지능형 탐색 초기 정보 등록 - + diff --git a/frontend/src/pages/MissingPersonReportPage.js b/frontend/src/pages/MissingPersonReportPage.js index 15bf4e8ef1..bf02c0981c 100644 --- a/frontend/src/pages/MissingPersonReportPage.js +++ b/frontend/src/pages/MissingPersonReportPage.js @@ -11,7 +11,14 @@ import { IntelligentSearchOption } from "../components/reportIntelligent/Intelli import { IntelligentBasicInfo } from "../components/reportIntelligent/IntelligentBasicInfo"; import { IntelligentMap } from "../components/reportIntelligent/IntelligentMap"; import { IntelligentSearchResult } from "../components/reportIntelligent/IntelligentSearchResult"; -import { getMissingPerson, getMissingPeopleStep, getSearchHistoryList, getSearchResultImg } from "../core/api"; +import { + getMissingPerson, + getMissingPeopleStep, + getSearchHistoryList, + getSearchResultImg, + getBetweenResultImg, + getCCTVResult, +} from "../core/api"; import { useLocation } from "react-router-dom"; import { ReportMain } from "../components/missingPersonReport/ReportMain"; import { ReportIntelligent } from "../components/reportIntelligent/ReportIntelligent"; @@ -19,7 +26,12 @@ function MissingPersonReportPage() { const [missingPerson, setMissingPerson] = useState([]); const [step, setStep] = useState([]); const [searchHistoryList, setSearchHistoryList] = useState([]); - const [step1data, setStep1data] = useState([]); + const [firstdata, setFirstdata] = useState([]); + const [betweenData, setBetweenData] = useState([]); + const [secondData, setSecondData] = useState([]); + const [firstCCTVData, setFirstCCTVData] = useState([]); + const [betweenCCTVData, setBetweenCCTVData] = useState([]); + const [secondCCTVData, setSecondCCTVData] = useState([]); const [loading, setLoading] = useState(false); const location = useLocation(); console.log("sssdfsadfsad location", location); @@ -39,6 +51,8 @@ function MissingPersonReportPage() { useEffect(() => { console.log("useEffect", userId); fetchData(); + fetchResultData(); + fetchCCTVData(); }, []); const fetchData = () => { @@ -75,9 +89,35 @@ function MissingPersonReportPage() { getSearchHistoryList(userId).then((res) => { setSearchHistoryList(res.data); }); + }; + + const fetchResultData = () => { getSearchResultImg(1, userId, "first").then((res) => { - console.log("step1data", res.data); - setStep1data(res.data); + console.log("firstData", res.data); + setFirstdata(res.data); + }); + getBetweenResultImg(1, userId).then((res) => { + console.log("betweenData", res.data); + setBetweenData(res.data); + }); + getSearchResultImg(1, userId, "second").then((res) => { + console.log("secondData", res.data); + setSecondData(res.data); + }); + }; + + const fetchCCTVData = () => { + getCCTVResult(userId, "first").then((res) => { + console.log("firstCCTVData", res.data); + setFirstCCTVData(res.data); + }); + getCCTVResult(userId, "between").then((res) => { + console.log("betweenCCTVData", res.data); + setBetweenCCTVData(res.data); + }); + getCCTVResult(userId, "second").then((res) => { + console.log("secondCCTVData", res.data); + setSecondCCTVData(res.data); }); }; @@ -93,77 +133,21 @@ function MissingPersonReportPage() { ); }; - /*실종자 리포트 - 메인*/ - // const ReportMain = () => { - // return ( - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // ); - // }; - /*실종자 리포트 - 지능형 탐색*/ - // const ReportIntelligent = () => { - // return ( - // - // - // - // - // 지능형 탐색 - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // - // ); - // }; return ( - {/* - */} diff --git a/server/gradlew b/server/gradlew old mode 100755 new mode 100644 diff --git a/server/gradlew.bat b/server/gradlew.bat index 6689b85bee..93e3f59f13 100644 --- a/server/gradlew.bat +++ b/server/gradlew.bat @@ -1,92 +1,92 @@ -@rem -@rem Copyright 2015 the original author or authors. -@rem -@rem Licensed under the Apache License, Version 2.0 (the "License"); -@rem you may not use this file except in compliance with the License. -@rem You may obtain a copy of the License at -@rem -@rem https://www.apache.org/licenses/LICENSE-2.0 -@rem -@rem Unless required by applicable law or agreed to in writing, software -@rem distributed under the License is distributed on an "AS IS" BASIS, -@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -@rem See the License for the specific language governing permissions and -@rem limitations under the License. -@rem - -@if "%DEBUG%"=="" @echo off -@rem ########################################################################## -@rem -@rem Gradle startup script for Windows -@rem -@rem ########################################################################## - -@rem Set local scope for the variables with windows NT shell -if "%OS%"=="Windows_NT" setlocal - -set DIRNAME=%~dp0 -if "%DIRNAME%"=="" set DIRNAME=. -@rem This is normally unused -set APP_BASE_NAME=%~n0 -set APP_HOME=%DIRNAME% - -@rem Resolve any "." and ".." in APP_HOME to make it shorter. -for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi - -@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. -set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m" - -@rem Find java.exe -if defined JAVA_HOME goto findJavaFromJavaHome - -set JAVA_EXE=java.exe -%JAVA_EXE% -version >NUL 2>&1 -if %ERRORLEVEL% equ 0 goto execute - -echo. -echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. -echo. -echo Please set the JAVA_HOME variable in your environment to match the -echo location of your Java installation. - -goto fail - -:findJavaFromJavaHome -set JAVA_HOME=%JAVA_HOME:"=% -set JAVA_EXE=%JAVA_HOME%/bin/java.exe - -if exist "%JAVA_EXE%" goto execute - -echo. -echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% -echo. -echo Please set the JAVA_HOME variable in your environment to match the -echo location of your Java installation. - -goto fail - -:execute -@rem Setup the command line - -set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar - - -@rem Execute Gradle -"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %* - -:end -@rem End local scope for the variables with windows NT shell -if %ERRORLEVEL% equ 0 goto mainEnd - -:fail -rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of -rem the _cmd.exe /c_ return code! -set EXIT_CODE=%ERRORLEVEL% -if %EXIT_CODE% equ 0 set EXIT_CODE=1 -if not ""=="%GRADLE_EXIT_CONSOLE%" exit %EXIT_CODE% -exit /b %EXIT_CODE% - -:mainEnd -if "%OS%"=="Windows_NT" endlocal - -:omega +@rem +@rem Copyright 2015 the original author or authors. +@rem +@rem Licensed under the Apache License, Version 2.0 (the "License"); +@rem you may not use this file except in compliance with the License. +@rem You may obtain a copy of the License at +@rem +@rem https://www.apache.org/licenses/LICENSE-2.0 +@rem +@rem Unless required by applicable law or agreed to in writing, software +@rem distributed under the License is distributed on an "AS IS" BASIS, +@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +@rem See the License for the specific language governing permissions and +@rem limitations under the License. +@rem + +@if "%DEBUG%"=="" @echo off +@rem ########################################################################## +@rem +@rem Gradle startup script for Windows +@rem +@rem ########################################################################## + +@rem Set local scope for the variables with windows NT shell +if "%OS%"=="Windows_NT" setlocal + +set DIRNAME=%~dp0 +if "%DIRNAME%"=="" set DIRNAME=. +@rem This is normally unused +set APP_BASE_NAME=%~n0 +set APP_HOME=%DIRNAME% + +@rem Resolve any "." and ".." in APP_HOME to make it shorter. +for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi + +@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m" + +@rem Find java.exe +if defined JAVA_HOME goto findJavaFromJavaHome + +set JAVA_EXE=java.exe +%JAVA_EXE% -version >NUL 2>&1 +if %ERRORLEVEL% equ 0 goto execute + +echo. +echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. +echo. +echo Please set the JAVA_HOME variable in your environment to match the +echo location of your Java installation. + +goto fail + +:findJavaFromJavaHome +set JAVA_HOME=%JAVA_HOME:"=% +set JAVA_EXE=%JAVA_HOME%/bin/java.exe + +if exist "%JAVA_EXE%" goto execute + +echo. +echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% +echo. +echo Please set the JAVA_HOME variable in your environment to match the +echo location of your Java installation. + +goto fail + +:execute +@rem Setup the command line + +set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar + + +@rem Execute Gradle +"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %* + +:end +@rem End local scope for the variables with windows NT shell +if %ERRORLEVEL% equ 0 goto mainEnd + +:fail +rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of +rem the _cmd.exe /c_ return code! +set EXIT_CODE=%ERRORLEVEL% +if %EXIT_CODE% equ 0 set EXIT_CODE=1 +if not ""=="%GRADLE_EXIT_CONSOLE%" exit %EXIT_CODE% +exit /b %EXIT_CODE% + +:mainEnd +if "%OS%"=="Windows_NT" endlocal + +:omega