def BoxDraw(box):
import matplotlib
from matplotlib.patches import Polygon
ax = matplotlib.pyplot.subplot(111, aspect='equal')
rect = Polygon([[box[0], box[2]], [box[1], box[2]], [box[1], box[3]],
[box[0], box[3]]])
rect.set_fill(False)
rect.set_linewidth(2)
ax.add_patch(rect)
def visualize_inference_sample(sample_id: str,
class_names: list,
image_dir: str,
json_dir: str,
render_dir: str) -> None:
"""Load inference result json file and render overlay on raw image."""
image_path = get_image_local_path(sample_id, image_dir)
json_path = get_inference_result_path(sample_id, json_dir)
render_path = os.path.join(render_dir, sample_id + '.jpg')
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
with open(json_path, 'r') as f:
data = json.load(f)
assert image.shape[0] == data["image"]["height"]
assert image.shape[1] == data["image"]["width"]
fig, ax = plt.subplots(1, figsize=(20,20))
colors = random_colors(len(data["annotations"]))
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(f"Sample_id: {sample_id}, shape {image.shape}")
masked_image = image.astype(np.uint32).copy()
for i, instance in enumerate(data["annotations"]):
x, y, w, h = instance["bbox"]
p = patches.Rectangle((x, y), w, h, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=colors[i], facecolor='none')
ax.add_patch(p)
polygon = instance["segmentation"][0]
polygon = np.array(polygon).reshape(-1, 2)
p = Polygon(polygon, facecolor="none", edgecolor=colors[i],
linewidth=None, fill=True)
p.set_fill(True)
ax.add_patch(p)
# Label
ax.text(x, y + 8, class_names[instance["category_id"]],
color='w', size=11, backgroundcolor="none")
ax.imshow(masked_image.astype(np.uint8))
plt.savefig(render_path)
plt.close(fig)
return
def drawPolygon(self):
"""
Draws the polygon
"""
plt.ion()
# plt.figure(self.n)
plt.axes()
if self.prevPolygon:
self.prevPolygon.remove()
plt.clf()
polygon = Polygon(xy=self.points, closed='True')
self.prevPolygon = polygon
polygon.set_fill(False)
plt.gca().add_patch(polygon)
plt.axis('scaled')
# plt.show(block = False)
self.n += 1
def visualize_sample(sample_id: str, directory_path: str) -> None:
"""Render the image and annotations for a given sample id. """
image = cv2.imread(get_image_local_path(sample_id, directory_path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
with open(get_json_local_path(sample_id, directory_path)) as f:
data = json.load(f)
_, ax = plt.subplots(1, figsize=(20, 20))
colors = random_colors(len(data["objects"]))
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(f"Sample_id: {sample_id}, shape {image.shape}")
masked_image = image.astype(np.uint32).copy()
for i, instance in enumerate(data["objects"]):
if instance["geometryType"] == "rectangle":
y1, x1, y2, x2 = instance["points"]["exterior"][0][1], \
instance["points"]["exterior"][0][0], \
instance["points"]["exterior"][1][1], \
instance["points"]["exterior"][1][0]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=colors[i], facecolor='none')
ax.add_patch(p)
if instance["geometryType"] == "polygon":
y1, x1 = instance["points"]["exterior"][0][1], \
instance["points"]["exterior"][0][0]
p = Polygon(instance["points"]["exterior"],
facecolor="none", edgecolor=colors[i],
linewidth=None, fill=True)
p.set_fill(True)
ax.add_patch(p)
# Label
ax.text(x1, y1 + 8, instance["classTitle"],
color='w', size=11, backgroundcolor="none")
ax.imshow(masked_image.astype(np.uint8))
plt.show()
return
def visualize_sample_analytic(sample_id: str, directory_path: str) -> None:
"""Render the image and annotations for a given sample id in analytic set."""
image = cv2.imread(get_image_local_path(sample_id, directory_path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
with open(get_people_local_path(sample_id, directory_path)) as f:
annotation_people = json.load(f)
with open(get_poly_local_path(sample_id, directory_path)) as f:
annotation_poly = json.load(f)
_, ax = plt.subplots(1, figsize=(20, 20))
print(f"{len(annotation_people['objects'])} peoples + {len(annotation_poly['objects'])} polygons")
colors = random_colors(len(annotation_people["objects"]) +
len(annotation_poly["objects"]))
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(f"Sample_id: {sample_id}, shape {image.shape}")
masked_image = image.astype(np.uint32).copy()
for i, instance in enumerate(annotation_people["objects"]):
if instance["geometryType"] == "rectangle":
y1, x1, y2, x2 = instance["points"]["exterior"][0][1], \
instance["points"]["exterior"][0][0], \
instance["points"]["exterior"][1][1], \
instance["points"]["exterior"][1][0]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=colors[i], facecolor='none')
ax.add_patch(p)
# Label
ax.text(x1, y1 + 8, instance["classTitle"],
color='w', size=11, backgroundcolor="none")
for j, instance in enumerate(annotation_poly["objects"]):
if instance["geometryType"] == "bitmap":
mask = base64_2_mask(instance["bitmap"]["data"])
x1, y1 = instance["bitmap"]["origin"][0], \
instance["bitmap"]["origin"][1]
contours, hierarchy = cv2.findContours(mask.astype(np.uint8),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnt = np.squeeze(contours[0], axis=1)
# Depends on whether the mini-mask was adpoted
# cnt += [x1, y1]
p = Polygon(cnt, facecolor="none",
edgecolor=colors[len(annotation_people["objects"])+j],
linewidth=None, fill=True)
p.set_fill(True)
ax.add_patch(p)
# Label
ax.text(x1, y1 + 8, instance["classTitle"],
color='w', size=11, backgroundcolor="none")
ax.imshow(masked_image.astype(np.uint8))
plt.show()
return
