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python: examplesL gesture_rec: Code cleanup
Added more commentaries and made code look somewhat nicer. Signed-off-by: Daniel Matyas <[email protected]>
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bindings/python/examples/gesture_rec/process.py

Lines changed: 15 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -3,6 +3,7 @@
33
from scipy.spatial import ConvexHull
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from scipy.ndimage import distance_transform_edt
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6+
# These are empirical values, which were found by trial and error
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pixel_no_threshold = 200
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analyzed_region_distance = 38.25
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hand_radius_error = 1.9
@@ -25,17 +26,19 @@ def _display_result(self):
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# =============================================================================
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def _depth_img_hist(self):
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# hist[0] = number of elements
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# hist[1] = distance normalized to 255
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# hist[1] = distance normalized to 255 (this was done in notebook.py)
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counts, bins = np.histogram(self.depth_img.ravel(), 512)
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hist = [counts, bins]
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start = 1
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while start < (len(hist[0]) - 2) and hist[0][start] < pixel_no_threshold:
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start += 1
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bin_step = hist[1][start + 1] - hist[1][start]
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stop = int((hist[1][start] + analyzed_region_distance - hist[1][0]) / bin_step)
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38-
self.stop_distance = hist[1][stop]
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self.stop_distance = hist[1][stop]
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# Analyze the pixel only if it is closer than a threshold
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self.binary_img = (self.depth_img < self.stop_distance) * 1
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# =============================================================================
@@ -45,6 +48,7 @@ def _detect_hand(self):
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props = measure.regionprops(labels)
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if len(props) == 0:
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raise Exception("No object found.")
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props.sort(key=lambda x: x.area, reverse=True)
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self.max_area = props[0]
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points = props[0].filled_image
@@ -55,8 +59,8 @@ def _detect_hand(self):
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# Compute the distance of non-zero points (hand) to the nearest zero point (background)
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self.dist_map = distance_transform_edt(points)
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# Indices of hand center, i.e. the point farthest from the background
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self.hand_center = tuple(arr[0] for arr in np.where(self.dist_map == np.max(self.dist_map)))
59-
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self.radius = hand_radius_error * np.max(self.dist_map)
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# =============================================================================
@@ -71,21 +75,22 @@ def _count_fingers(self):
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[vertices[-1] - vertices[0]], axis=0)
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# distance bw 2 consecutive vertices
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# In cdist variables the distance units are pixels
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cdist = np.sqrt(dist[:, 0] ** 2 + dist[:, 1] ** 2)
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# This is just some formula invented by me. It is not optimal.
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# It is used to make cdist_threshold inversely proportional to distance_threshold,
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# while keeping it lower than 25
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# It is used to make cdist_threshold inversely proportional to stop_distance,
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# while keeping it between 0 and 25
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cdist_threshold = np.sqrt(1 - self.stop_distance / 255) * 25
80-
cdist = (cdist <= cdist_threshold) * 1
85+
cdist_bin = (cdist <= cdist_threshold) * 1
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# Used to check whether a cdist smaller than the threshold
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# is following a cdist bigger than the threshold
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cdist_diff = np.append(cdist[0:len(cdist) - 1] - cdist[1:len(cdist)],
85-
[cdist[-1] - cdist[0]], axis=0)
89+
cdist_diff = np.append(cdist_bin[0:len(cdist_bin) - 1] - cdist_bin[1:len(cdist_bin)],
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[cdist_bin[-1] - cdist_bin[0]], axis=0)
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# Indices of vertices which correspond to fingertips
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dist_idx = np.where((cdist_diff == -1) | ((cdist_diff == 0) & (cdist == 0)))
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dist_idx = np.where((cdist_diff == -1) | ((cdist_diff == 0) & (cdist_bin == 0)))
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dist_idx = np.array(dist_idx) + 1
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# dist_idx is a double list
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dist_idx = dist_idx[0]
@@ -123,3 +128,4 @@ def detect_gesture(self):
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self.resultVar = "Found " + str(len(self.fingers[0])) + " extended fingers. Rock"
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else:
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self.resultVar = "Found " + str(len(self.fingers[0])) + " extended fingers. Unknown gesture"
131+

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