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https://github.com/blakeblackshear/frigate.git
synced 2026-02-03 09:45:22 +03:00
WIP
This commit is contained in:
Andreas Franzén
parent
9f51347f9c
commit
dfb178d233
@ -28,11 +28,14 @@ logger = logging.getLogger("EdgeTPUModel")
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class YOLOv5Tfl(DetectionApi):
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def __init__(self, det_device=None, model_config=None):
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self.labels = load_labels(
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model_config.labelmap_path
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) # Just to be able to print human readable labels
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"""
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Creates an object for running a Yolov5 model on an EdgeTPU or a Desktop
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Inputs:
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- model_file: path to edgetpu-compiled tflite file
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- names_file: yaml names file (yolov5 format)
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- conf_thresh: detection threshold
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- iou_thresh: NMS threshold
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- desktop: option to run model on a desktop
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@ -42,11 +45,11 @@ class YOLOv5Tfl(DetectionApi):
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"""
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self.model_file = model_config.path
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self.labels = load_labels(model_config.labelmap_path)
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self.desktop = True # Should be cpu?
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self.conf_thresh = 0.25
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self.iou_thresh = 0.45
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self.filter_classes = None
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# self.filter_classes = [15, 16] # cat, dog
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self.agnostic_nms = False
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self.max_det = 1000
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@ -121,10 +124,9 @@ class YOLOv5Tfl(DetectionApi):
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tensor_input = np.squeeze(tensor_input, axis=0)
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results = self.forward(tensor_input)
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det = results[0]
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# logger.info(f"detections {len(det)}")
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detections = np.zeros((20, 6), np.float32)
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i = 0
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for *xyxy, conf, cls in reversed(det):
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for *xyxy, conf, cls in det:
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detections[i] = [
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int(cls),
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float(conf),
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@ -135,8 +137,6 @@ class YOLOv5Tfl(DetectionApi):
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]
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i += 1
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logger.info(f"{self.labels[int(cls)], int(cls), float(conf)}")
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return detections
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def forward(self, x: np.ndarray, with_nms=True) -> np.ndarray:
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@ -237,79 +237,6 @@ class Colors:
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return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4))
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def plot_one_box(
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box, im, color=(128, 128, 128), txt_color=(255, 255, 255), label=None, line_width=3
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):
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# Plots one xyxy box on image im with label
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assert (
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im.data.contiguous
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), "Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image."
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lw = line_width or max(int(min(im.size) / 200), 2) # line width
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c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
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cv2.rectangle(im, c1, c2, color, thickness=lw, lineType=cv2.LINE_AA)
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if label:
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tf = max(lw - 1, 1) # font thickness
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txt_width, txt_height = cv2.getTextSize(
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label, 0, fontScale=lw / 3, thickness=tf
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)[0]
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c2 = c1[0] + txt_width, c1[1] - txt_height - 3
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cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(
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im,
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label,
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(c1[0], c1[1] - 2),
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0,
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lw / 3,
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txt_color,
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thickness=tf,
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lineType=cv2.LINE_AA,
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)
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return im
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def resize_and_pad(image, desired_size):
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old_size = image.shape[:2]
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ratio = float(desired_size / max(old_size))
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new_size = tuple([int(x * ratio) for x in old_size])
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# new_size should be in (width, height) format
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image = cv2.resize(image, (new_size[1], new_size[0]))
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delta_w = desired_size - new_size[1]
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delta_h = desired_size - new_size[0]
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pad = (delta_w, delta_h)
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color = [100, 100, 100]
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new_im = cv2.copyMakeBorder(
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image, 0, delta_h, 0, delta_w, cv2.BORDER_CONSTANT, value=color
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)
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return new_im, pad
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def get_image_tensor(img, max_size, debug=False):
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"""
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Reshapes an input image into a square with sides max_size
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"""
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if type(img) is str:
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img = cv2.imread(img)
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resized, pad = resize_and_pad(img, max_size)
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resized = resized.astype(np.float32)
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if debug:
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cv2.imwrite("intermediate.png", resized)
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# Normalise!
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resized /= 255.0
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return img, resized, pad
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def xyxy2xywh(x):
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# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
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y = np.copy(x)
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@ -638,15 +638,15 @@ def mjpeg_feed(camera_name):
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@bp.route("/<camera_name>/latest.jpg")
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def latest_frame(camera_name):
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draw_options = {
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"bounding_boxes": request.args.get("bbox", type=int),
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"bounding_boxes": request.args.get("bbox", default=1, type=int),
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"timestamp": request.args.get("timestamp", type=int),
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"zones": request.args.get("zones", type=int),
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"mask": request.args.get("mask", type=int),
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"motion_boxes": request.args.get("motion", type=int),
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"motion_boxes": request.args.get("motion", default=1, type=int),
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"regions": request.args.get("regions", type=int),
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}
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resize_quality = request.args.get("quality", default=70, type=int)
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save_output = request.args.get("save_output", default=0, type=int)
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save_output = request.args.get("save_output", default=1, type=int)
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if camera_name in current_app.frigate_config.cameras:
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frame = current_app.detected_frames_processor.get_current_frame(
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@ -660,7 +660,10 @@ def latest_frame(camera_name):
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frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
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if save_output == 1:
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cv2.imwrite(f'/tmp/{camera_name}.{time.time_ns() // 1000000}.jpeg', frame)
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cv2.imwrite(
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f"/tmp/debug/{camera_name}.yolov5.cat.t25.{time.time_ns() // 1000000}.jpeg",
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frame,
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)
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ret, jpg = cv2.imencode(
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".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), resize_quality]
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@ -45,11 +45,8 @@ class LocalObjectDetector(ObjectDetector):
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self.fps = EventsPerSecond()
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if labels is None:
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self.labels = {}
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# else:
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# self.labels = load_labels(labels)
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if model_config.labelmap_path:
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self.labels = load_labels(model_config.labelmap_path)
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else:
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self.labels = load_labels(labels)
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if model_config:
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self.input_transform = tensor_transform(model_config.input_tensor)
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@ -232,18 +229,23 @@ class RemoteObjectDetector:
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self.np_shm[:] = tensor_input[:]
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self.event.clear()
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self.detection_queue.put(self.name)
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result = self.event.wait(timeout=10.0)
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result = self.event.wait(timeout=100.0)
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# if it timed out
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if result is None:
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return detections
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# file_object = open("/tmp/detections.yolov5.txt", "a")
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for d in self.out_np_shm:
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if d[1] < threshold:
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# if d[1] < threshold:
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if d[1] < 0.25:
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break
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detections.append(
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(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
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)
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# file_object.write(f"{self.labels[int(d[0])]},{float(d[1])}\n")
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# file_object.close()
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logger.info(f"end detections")
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self.fps.update()
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return detections
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