abc
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王庆刚
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@ -285,6 +285,38 @@ def boxing_img(det, img, line_width=3):
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return imgx
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def array2list(bboxes):
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track_fids = np.unique(bboxes[:, 7].astype(int))
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track_fids.sort()
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lboxes = []
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for f_id in track_fids:
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# print(f"The ID is: {t_id}")
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idx = np.where(bboxes[:, 7] == f_id)[0]
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box = bboxes[idx, :]
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lboxes.append(box)
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assert len(set(box[:, 4])) == len(box), "Please check!!!"
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return lboxes
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def get_subimgs(imgs, tracks, scale=2):
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bboxes = []
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if len(tracks):
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bboxes = array2list(tracks)
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subimgs = []
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for i, boxes in enumerate(bboxes):
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fid = int(boxes[0, 7])
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for *xyxy, tid, conf, cls, fid, bid in boxes:
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pt2 = [p/scale for p in xyxy]
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x1, y1, x2, y2 = (int(pt2[0]), int(pt2[1])), (int(pt2[2]), int(pt2[3]))
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subimgs.append((int(fid), int(bid), imgs[fid-1][y1:y2, x1:x2]))
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return subimgs
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def draw_tracking_boxes(imgs, tracks, scale=2):
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'''需要确保 imgs 覆盖tracks中的帧ID数
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tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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@ -295,27 +327,15 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
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'''
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def array2list(bboxes):
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track_fids = np.unique(bboxes[:, 7].astype(int))
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track_fids.sort()
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lboxes = []
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for f_id in track_fids:
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# print(f"The ID is: {t_id}")
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idx = np.where(bboxes[:, 7] == f_id)[0]
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box = bboxes[idx, :]
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lboxes.append(box)
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assert len(set(box[:, 4])) == len(box), "Please check!!!"
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return lboxes
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bboxes = array2list(tracks)
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bboxes = []
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if len(tracks):
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bboxes = array2list(tracks)
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# if len(bboxes)!=len(imgs):
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# return False, imgs
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subimgs = []
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annimgs = []
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for i, boxes in enumerate(bboxes):
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fid = int(boxes[0, 7])
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annotator = Annotator(imgs[fid-1].copy())
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@ -331,11 +351,11 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
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pt2 = [p/scale for p in xyxy]
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annotator.box_label(pt2, label, color=color)
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img = annotator.result()
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subimgs.append((fid, img))
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annimgs.append((int(fid), img))
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return subimgs
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return annimgs
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@ -37,6 +37,9 @@ def find_samebox_in_array(arr, target):
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def extract_data(datapath):
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'''
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0/1_track.data 数据读取
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'''
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bboxes, ffeats = [], []
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trackerboxes = np.empty((0, 9), dtype=np.float64)
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@ -147,8 +150,15 @@ def extract_data(datapath):
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return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict
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def read_tracking_output(filepath):
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'''
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0/1_tracking_output.data 数据读取
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'''
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boxes = []
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feats = []
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if not os.path.isfile(filepath):
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return np.array(boxes), np.array(feats)
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with open(filepath, 'r', encoding='utf-8') as file:
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for line in file:
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line = line.strip() # 去除行尾的换行符和可能的空白字符
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@ -176,7 +186,6 @@ def read_deletedBarcode_file(filePath):
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split_flag, all_list = False, []
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dict, barcode_list, similarity_list = {}, [], []
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clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
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for i, line in enumerate(clean_lines):
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@ -199,6 +208,7 @@ def read_deletedBarcode_file(filePath):
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if label == 'SeqDir':
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dict['SeqDir'] = value
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dict['filetype'] = "deletedBarcode"
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if label == 'Deleted':
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dict['Deleted'] = value
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if label == 'List':
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@ -259,15 +269,19 @@ def read_returnGoods_file(filePath):
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if label == 'SeqDir':
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dict['SeqDir'] = value
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dict['Deleted'] = value.split('_')[-1]
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dict['filetype'] = "returnGoods"
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if label == 'List':
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split_flag = True
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continue
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if split_flag:
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bcd = label.split('_')[-1]
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# event_list.append(label + '_' + bcd)
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event_list.append(label)
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barcode_list.append(label.split('_')[-1])
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barcode_list.append(bcd)
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similarity_list.append(value.split(',')[0])
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type_list.append(value.split('=')[-1])
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if len(barcode_list): dict['barcode'] = barcode_list
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if len(similarity_list): dict['similarity'] = similarity_list
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if len(event_list): dict['event'] = event_list
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@ -279,33 +293,51 @@ def read_returnGoods_file(filePath):
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# =============================================================================
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# def read_seneor(filepath):
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# WeightDict = OrderedDict()
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# with open(filepath, 'r', encoding='utf-8') as f:
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# lines = f.readlines()
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# for i, line in enumerate(lines):
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# line = line.strip()
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#
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# keyword = line.split(':')[0]
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# value = line.split(':')[1]
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#
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# vdata = [float(s) for s in value.split(',') if len(s)]
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#
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# WeightDict[keyword] = vdata[-1]
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#
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# return WeightDict
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# =============================================================================
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def read_one2one_simi(filePath):
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def read_seneor(filepath):
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WeightDict = OrderedDict()
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with open(filepath, 'r', encoding='utf-8') as f:
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SimiDict = {}
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with open(filePath, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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flag = False
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for i, line in enumerate(lines):
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line = line.strip()
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if line.find('barcode:')<0 and not flag:
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continue
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if line.find('barcode:')==0 :
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flag = True
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continue
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keyword = line.split(':')[0]
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value = line.split(':')[1]
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vdata = [float(s) for s in value.split(',') if len(s)]
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WeightDict[keyword] = vdata[-1]
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return WeightDict
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# if line.endswith(','):
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# line = line[:-1]
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if flag:
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barcode = line.split(',')[0].strip()
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value = line.split(',')[1].split(':')[1].strip()
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SimiDict[barcode] = float(value)
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if flag and not line:
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flag = False
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return SimiDict
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def read_weight_timeConsuming(filePth):
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@ -362,15 +394,14 @@ def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
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nw = len(wdata)
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assert(nw) >= 8, "The num of weight data is less than 8!"
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i1, i2 = 0, 7
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while i2 < nw:
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data = wdata[i1:(i2+1)]
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max(data) - min(data)
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if i2<7:
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i1 = 0
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else:
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i1 = i2-windth
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# i1, i2 = 0, 7
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# while i2 < nw:
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# data = wdata[i1:(i2+1)]
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# max(data) - min(data)
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# if i2<7:
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# i1 = 0
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# else:
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# i1 = i2-windth
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min_t = min(wtime + stime)
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wtime = [t-min_t for t in wtime]
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@ -405,15 +436,12 @@ def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
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def main(file_path):
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def test_process(file_path):
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WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(file_path)
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plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict)
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if __name__ == "__main__":
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def main():
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files_path = r'\\192.168.1.28\share\测试_202406\0814\0814\20240814-102227-62264578-a720-4eb9-b95e-cb8be009aa98_null'
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k = 0
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for filename in os.listdir(files_path):
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@ -424,42 +452,21 @@ if __name__ == "__main__":
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extract_data(file_path)
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if os.path.isfile(file_path) and filename.find("process.data")>=0:
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main(file_path)
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test_process(file_path)
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k += 1
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if k == 1:
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break
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# print("Done")
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def main1():
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fpath = r'\\192.168.1.28\share\测试_202406\1101\images\20241101-140456-44dc75b5-c406-4cb2-8317-c4660bb727a3_6922130101355_6922130101355\process.data'
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simidct = read_one2one_simi(fpath)
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print(simidct)
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if __name__ == "__main__":
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# main()
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main1()
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