18 changed files with 954 additions and 389 deletions
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""" |
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Copyright 2022 Huawei Technologies Co., Ltd |
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Licensed under the Apache License, Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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See the License for the specific language governing permissions and |
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limitations under the License. |
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""" |
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import time |
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import cv2 |
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import numpy as np |
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import torch |
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import torchvision |
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def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=False, scaleFill=False, scaleup=True): |
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# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 |
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shape = img.shape[:2] # current shape [height, width] |
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if isinstance(new_shape, int): |
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new_shape = (new_shape, new_shape) |
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# Scale ratio (new / old) |
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) |
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if not scaleup: # only scale down, do not scale up (for better test mAP) |
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r = min(r, 1.0) |
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# Compute padding |
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ratio = r, r # width, height ratios |
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) |
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding |
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if auto: # minimum rectangle |
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dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding |
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elif scaleFill: # stretch |
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dw, dh = 0.0, 0.0 |
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new_unpad = (new_shape[1], new_shape[0]) |
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ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios |
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dw /= 2 # divide padding into 2 sides |
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dh /= 2 |
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if shape[::-1] != new_unpad: # resize |
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img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) |
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) |
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) |
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img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border |
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return img, ratio, (dw, dh) |
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def non_max_suppression( |
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prediction, |
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conf_thres=0.25, |
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iou_thres=0.45, |
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classes=None, |
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agnostic=False, |
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multi_label=False, |
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labels=(), |
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max_det=300, |
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nm=0, # number of masks |
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): |
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"""Non-Maximum Suppression (NMS) on inference results to reject overlapping detections |
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Returns: |
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list of detections, on (n,6) tensor per image [xyxy, conf, cls] |
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""" |
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if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) |
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prediction = prediction[0] # select only inference output |
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device = prediction.device |
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mps = 'mps' in device.type # Apple MPS |
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if mps: # MPS not fully supported yet, convert tensors to CPU before NMS |
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prediction = prediction.cpu() |
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bs = prediction.shape[0] # batch size |
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nc = prediction.shape[2] - nm - 5 # number of classes |
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xc = prediction[..., 4] > conf_thres # candidates |
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# Checks |
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assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' |
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assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' |
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# Settings |
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# min_wh = 2 # (pixels) minimum box width and height |
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max_wh = 7680 # (pixels) maximum box width and height |
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max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() |
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time_limit = 0.5 + 0.05 * bs # seconds to quit after |
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multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) |
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t = time.time() |
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mi = 5 + nc # mask start index |
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output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs |
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for xi, x in enumerate(prediction): # image index, image inference |
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# Apply constraints |
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# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height |
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x = x[xc[xi]] # confidence |
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# Cat apriori labels if autolabelling |
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if labels and len(labels[xi]): |
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lb = labels[xi] |
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v = torch.zeros((len(lb), nc + nm + 5), device=x.device) |
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v[:, :4] = lb[:, 1:5] # box |
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v[:, 4] = 1.0 # conf |
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v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls |
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x = torch.cat((x, v), 0) |
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# If none remain process next image |
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if not x.shape[0]: |
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continue |
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# Compute conf |
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x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf |
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# Box/Mask |
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box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) |
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mask = x[:, mi:] # zero columns if no masks |
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# Detections matrix nx6 (xyxy, conf, cls) |
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if multi_label: |
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i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T |
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x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) |
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else: # best class only |
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conf, j = x[:, 5:mi].max(1, keepdim=True) |
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x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] |
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# Filter by class |
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if classes is not None: |
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x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] |
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# Check shape |
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n = x.shape[0] # number of boxes |
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if not n: # no boxes |
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continue |
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elif n > max_nms: # excess boxes |
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x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence |
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else: |
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x = x[x[:, 4].argsort(descending=True)] # sort by confidence |
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# Batched NMS |
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c = x[:, 5:6] * (0 if agnostic else max_wh) # classes |
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boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores |
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i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS |
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if i.shape[0] > max_det: # limit detections |
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i = i[:max_det] |
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output[xi] = x[i] |
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if mps: |
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output[xi] = output[xi].to(device) |
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if (time.time() - t) > time_limit: |
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print(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded') |
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break # time limit exceeded |
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return output |
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def xywh2xyxy(x): |
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# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right |
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) |
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y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x |
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y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y |
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y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x |
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y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y |
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return y |
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def get_labels_from_txt(path): |
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labels_dict = dict() |
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with open(path) as f: |
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for cat_id, label in enumerate(f.readlines()): |
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labels_dict[cat_id] = label.strip() |
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return labels_dict |
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def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): |
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# Rescale coords (xyxy) from img1_shape to img0_shape |
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if ratio_pad is None: # calculate from img0_shape |
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gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new |
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pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding |
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else: |
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gain = ratio_pad[0][0] |
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pad = ratio_pad[1] |
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coords[:, [0, 2]] -= pad[0] # x padding |
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coords[:, [1, 3]] -= pad[1] # y padding |
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coords[:, :4] /= gain |
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clip_coords(coords, img0_shape) |
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return coords |
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def clip_coords(boxes, shape): |
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# Clip bounding xyxy bounding boxes to image shape (height, width) |
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if isinstance(boxes, torch.Tensor): # faster individually |
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boxes[:, 0].clamp_(0, shape[1]) # x1 |
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boxes[:, 1].clamp_(0, shape[0]) # y1 |
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boxes[:, 2].clamp_(0, shape[1]) # x2 |
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boxes[:, 3].clamp_(0, shape[0]) # y2 |
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else: # np.array (faster grouped) |
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boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2 |
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boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2 |
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def nms(box_out, conf_thres=0.4, iou_thres=0.5): |
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try: |
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boxout = non_max_suppression(box_out, conf_thres=conf_thres, iou_thres=iou_thres, multi_label=True) |
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except: |
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boxout = non_max_suppression(box_out, conf_thres=conf_thres, iou_thres=iou_thres) |
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return boxout |
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def draw_bbox(bbox, img0, color, wt, names): |
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det_result_str = '' |
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for idx, class_id in enumerate(bbox[:, 5]): |
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if float(bbox[idx][4] < float(0.05)): |
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continue |
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img0 = cv2.rectangle(img0, (int(bbox[idx][0]), int(bbox[idx][1])), (int(bbox[idx][2]), int(bbox[idx][3])), color, wt) |
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img0 = cv2.putText(img0, str(idx) + ' ' + names[int(class_id)], (int(bbox[idx][0]), int(bbox[idx][1] + 16)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) |
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img0 = cv2.putText(img0, '{:.4f}'.format(bbox[idx][4]), (int(bbox[idx][0]), int(bbox[idx][1] + 32)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) |
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det_result_str += '{} {} {} {} {} {}\n'.format(names[bbox[idx][5]], str(bbox[idx][4]), bbox[idx][0], bbox[idx][1], bbox[idx][2], bbox[idx][3]) |
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return img0 |
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import os.path |
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from model.plugins.ModelBase import ModelBase |
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from myutils.ConfigManager import myCongif |
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import torch |
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import cv2 |
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class Model(ModelBase): |
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def __init__(self,path): |
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super().__init__() |
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self.name = "人员入侵(ACL)" |
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self.version = "V1.0" |
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self.model_type = 2 |
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#找pt模型路径 -- 一个约束py文件和模型文件的路径关系需要固定， -- 上传模型时，要解压好路径 |
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dirpath,filename = os.path.split(path) |
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model_file = os.path.join(dirpath,"yolov5s.pt") #目前约束模型文件和py文件在同一目录 |
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yolov5_path = myCongif.get_data("yolov5_path") |
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print(f"************{model_file},{yolov5_path}") |
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#实例化模型 |
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self.model = torch.hub.load(yolov5_path, 'custom', path=model_file, source='local') |
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def verify(self,image,data,isdraw=1): |
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results = self.model(image) # 进行模型检测 --- 需要统一接口 |
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detections = results.pandas().xyxy[0].to_dict(orient="records") |
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bwarn = False |
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warn_text = "" |
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#是否有检测区域，有先绘制检测区域 由于在该函数生成了polygon对象，所有需要在检测区域前调用。 |
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if data[1] == 1: |
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self.draw_polygon(image,data[2],(0, 255, 0)) |
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# 绘制检测结果 --- 也需要封装在类里， |
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for det in detections: |
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if det['name'] == 'person': #标签是人 |
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x1, y1, x2, y2 = int(det['xmin']), int(det['ymin']), int(det['xmax']), int(det['ymax']) |
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# 绘制目标识别的锚框 |
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cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) |
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if data[1] == 1: # 指定了检测区域 |
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x_center = (x1 + x2) / 2 |
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y_center = (y1 + y2) / 2 |
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#绘制中心点？ |
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cv2.circle(image, (int(x_center), int(y_center)), 5, (0, 0, 255), -1) |
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#判断是否区域点 |
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if not self.is_point_in_region((x_center, y_center)): |
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continue #没产生报警-继续 |
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#产生报警 |
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bwarn = True |
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warn_text = "Intruder detected!" |
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return detections,bwarn,warn_text |
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def testRun(self): |
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print("1111") |
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@ -0,0 +1,80 @@ |
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person |
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bicycle |
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car |
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motorcycle |
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airplane |
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bus |
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train |
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truck |
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boat |
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traffic_light |
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fire_hydrant |
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stop_sign |
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parking_meter |
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bench |
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bird |
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cat |
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dog |
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horse |
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sheep |
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cow |
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elephant |
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bear |
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zebra |
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giraffe |
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backpack |
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umbrella |
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handbag |
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tie |
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suitcase |
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frisbee |
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skis |
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snowboard |
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sports_ball |
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kite |
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baseball_bat |
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baseball_glove |
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skateboard |
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surfboard |
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tennis_racket |
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bottle |
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wine_glass |
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cup |
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fork |
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knife |
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spoon |
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bowl |
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banana |
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apple |
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sandwich |
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orange |
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broccoli |
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carrot |
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hot_dog |
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pizza |
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donut |
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cake |
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chair |
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couch |
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potted_plant |
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bed |
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dining_table |
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toilet |
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tv |
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laptop |
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mouse |
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remote |
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keyboard |
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cell_phone |
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microwave |
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oven |
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toaster |
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sink |
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refrigerator |
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book |
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clock |
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vase |
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scissors |
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teddy_bear |
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hair_drier |
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toothbrush |
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After Width: | Height: | Size: 64 KiB |
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@ -1,17 +1,31 @@ |
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import platform |
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from myutils.ConfigManager import myCongif |
from myutils.ConfigManager import myCongif |
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from myutils.MyLogger_logger import LogHandler |
from myutils.MyLogger_logger import LogHandler |
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from core.ViewManager import mVManager |
from core.ViewManager import mVManager |
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from web import create_app |
from web import create_app |
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from core.ModelManager import mMM |
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import os |
import os |
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import platform |
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import shutil |
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#print(f"Current working directory (run.py): {os.getcwd()}") |
print(f"Current working directory (run.py): {os.getcwd()}") |
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web = create_app() |
web = create_app() |
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if __name__ == '__main__': |
if __name__ == '__main__': |
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# logger = LogHandler().get_logger("main") |
system = platform.system() |
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# logger.debug("debug") |
if system == "Windows": |
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# logger.info("info") |
total, used, free = shutil.disk_usage("/") |
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mVManager.start_check_rtsp() |
elif system == "Linux": |
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"""获取Linux系统的剩余存储空间""" |
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statvfs = os.statvfs(os.getcwd()) |
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free = statvfs.f_bavail * statvfs.f_frsize |
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else: |
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raise NotImplementedError(f"Unsupported operating system: {system}") |
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print(free/(1024*1024)) |
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mMM.start_work() # 启动所有通道的处理 |
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mVManager.start_check_rtsp() #线程更新视频在线情况 |
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web.run(debug=True,port=5001) |
web.run(debug=True,port=5001) |
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|
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