fixed new sample cannot detect boxes due to filter
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unknown
+53
-21
@@ -45,6 +45,8 @@ class Sensor:
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FNORMAL = auto()
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FSHEARX = auto()
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FSHEARY = auto()
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CENTROID = auto()
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CONTOUR = auto()
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DEPTH = auto()
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# =========================================================================
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@@ -91,15 +93,22 @@ class Sensor:
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self.vupdate = False
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self.stitch_calibration = []
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# Data variables
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# Data Variables
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self.img = None
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self.img_size_raw = None
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self.img_filtered = None
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self.flow = None
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self.vnormal = None
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self.shear = None
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self.depth = None
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self.vshear = None
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# self.depth = None
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self.n_boxes = None
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self.fnormal = self.fshearx = self.fsheary = self.ftor = 0.0
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# Contact Data Variables
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self.centroid = None
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self.centroid_found = False
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self.contour = None
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self.depth_map = None
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# Timing
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self.last_log_time = time.perf_counter()
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@@ -116,7 +125,11 @@ class Sensor:
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self._stop_event = None
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# Load configuration
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self.config_cam = None
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self.config_stitcher = None
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self.config_contact = None
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self._load_config()
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# Initialize components
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self._stitcher = Stitcher(isstitch=isstitch, index=self.config_index)
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@@ -167,7 +180,8 @@ class Sensor:
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if log_timing: t0 = time.perf_counter()
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img = np.ascontiguousarray(self._stitcher.stitch_rt_optimized())
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if log_timing: timings['stitch'] = (time.perf_counter() - t0) * 1000
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self.img_size_raw = img.shape
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# Resize
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img = cv2.resize(img, tuple(self.config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
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@@ -222,7 +236,7 @@ class Sensor:
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# Get components
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self.vnormal = self.hhd.get_normal()
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self.shear = self.hhd.get_shear()
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self.vshear = self.hhd.get_shear()
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# # Depth computation
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# if log_timing: t0 = time.perf_counter()
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@@ -244,21 +258,31 @@ class Sensor:
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return self.flow
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def get_contact(self, multiple_foe=False, min_contour_area=3000, min_magnitude=1.0, expansion_threshold=0.01, downsample_factor=1, kernel_size=(5,5), denoise_sigma=15):
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def compute_contact(self, multiple_foe=False, min_contour_area=3000, min_magnitude=1.0, expansion_threshold=0.01, downsample_factor=1, kernel_size=(5,5), denoise_sigma=15):
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if multiple_foe:
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return compute_multiple_foe(self.vnormal, min_contour_area=min_contour_area, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
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else:
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if self.cuda:
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return compute_foe_gpu(self.vnormal, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
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else:
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return compute_foe(self.vnormal, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
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result = compute_multiple_foe(self.vnormal, min_contour_area=min_contour_area, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
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else:
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if self.cuda:
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result = compute_foe_gpu(self.vnormal, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
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else:
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result = compute_foe(self.vnormal, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
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self.centroid = result['centroid']
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self.centroid_found = result['centroid_found']
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if 'contour' in result:
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self.contour = result['contour']
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self.depth_map = result['divergence_map']
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return result
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# def get_depth(self, denoise_sigma=15):
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# return getFilteredDensity(self.vnormal, use_cuda=self.cuda)
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# self.depth = getFilteredDensity(self.vnormal, use_cuda=self.cuda)
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def get_depth(self, denoise_sigma=15):
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return getFilteredDensity(self.vnormal, use_cuda=self.cuda)
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self.depth = getFilteredDensity(self.vnormal, use_cuda=self.cuda)
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def update(self):
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"""
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Convenience method: get_img + compute_deformation in one call.
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@@ -270,7 +294,7 @@ class Sensor:
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self.compute_deformation()
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return {
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'img': self.img, 'flow': self.flow,
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'normal': self.vnormal, 'shear': self.shear, 'depth': self.depth,
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'normal': self.vnormal, 'shear': self.vshear, 'depth': self.depth,
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'fps': self.fps, 'fnormal': self.fnormal,
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'fshearx': self.fshearx, 'fsheary': self.fsheary, 'ftor': self.ftor,
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}
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@@ -291,13 +315,15 @@ class Sensor:
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mapping = {
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self.info.VRAW: lambda: self.flow if self.flow is not None else np.array([]),
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self.info.VNORMAL: lambda: self.vnormal if self.vnormal is not None else np.array([]),
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self.info.VSHEAR: lambda: self.shear if self.shear is not None else np.array([]),
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self.info.VSHEAR: lambda: self.vshear if self.vshear is not None else np.array([]),
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self.info.FNORMAL: lambda: self.fnormal,
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self.info.FSHEARX: lambda: self.fshearx,
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self.info.FSHEARY: lambda: self.fsheary,
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self.info.FPS: lambda: self.fps,
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self.info.TIMESTAMP: lambda: time.strftime('%H:%M:%S'),
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self.info.DEPTH: lambda: self.depth if self.depth is not None else np.array([]),
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self.info.DEPTH: lambda: self.depth_map if self.depth_map is not None else np.array([]),
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self.info.CENTROID: lambda: self.centroid if self.centroid is not None else np.array([]),
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self.info.CONTOUR: lambda: self.contour if self.contour is not None else np.array([]),
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}
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return tuple(mapping.get(arg, lambda: None)() for arg in args if isinstance(arg, self.info))
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@@ -431,6 +457,12 @@ class Sensor:
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self.config_index = cam["index"]
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self.config_pos_default = cam["pos_default"]
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config_stitcher = self.config["stitcher"]
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config_contact = self.config["contact"]
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if self.verbose:
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print(f"Config: {self.config_name}")
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print(f" Resolution: {self.config_resolution_raw} → {self.config_resolution_stitch}")
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@@ -579,7 +611,7 @@ class Sensor:
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while not self._stop_event.is_set():
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try:
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img, flow = self._flow_queue.get(timeout=0.1)
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contact_result = self.get_contact(flow)
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contact_result = self.compute_contact(flow)
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self._queue_put(self._result_queue, contact_result)
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except Empty:
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continue
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