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whole_processing.py
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whole_processing.py
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import cv2
import pye57
import pandas as pd
from sklearn.mixture import GaussianMixture
from PIL import Image
import numpy as np
import cv2 as cv
import uuid
from scipy.spatial import ConvexHull
import json_exporter
import copy
from typing import Tuple
import os
cv2 = cv
fname_img_original = f'img_original.png' # name of the image generated from the pointcloud
minx = None
miny = None
minz = None
maxx = None
maxy = None
maxz = None
ceil_height = 0
max_z = 0
def morph(input_img):
kernel = np.ones((21, 21), np.uint8)
morph_img = cv2.dilate(input_img, kernel, iterations=1)
morph_img = cv2.morphologyEx(morph_img, cv2.MORPH_OPEN, kernel)
morph_img = cv2.morphologyEx(morph_img, cv2.MORPH_CLOSE, kernel)
kernel = np.ones((20, 20), np.uint8)
morph_img = cv2.dilate(morph_img, kernel, iterations=1)
return morph_img
def create_image(e57_path: str):
resolution = 100
# TODO: THIS IS JUST FOR TESTING!!!!
load_file = os.path.exists(f'data/' + fname_img_original)
print(f'Loading the e57 file...')
e57 = pye57.E57(e57_path)
data_raw = e57.read_scan_raw(0)
print(f'... done.')
df = pd.DataFrame(data_raw)
# create a GaussianMixture to get the floor and ceiling cutoff
print(f'Calculating the cutoff height for the floor and ceiling... ')
dc = df["cartesianZ"]
dc_hist = np.histogram(dc.values, bins=80)
hist_weights = dc_hist[0] / dc_hist[0].sum()
hist_centers = dc_hist[1]
sampled_data = np.random.choice(hist_centers[1:], size=10000, p=hist_weights)
gmm = GaussianMixture(5)
gmm = gmm.fit(sampled_data.reshape(-1, 1))
floor_id = np.argmin(gmm.means_)
ceil_id = np.argmax(gmm.means_)
floor_cutoff = (gmm.means_[floor_id] + np.sqrt(gmm.covariances_[floor_id])*0.5).reshape(-1)[0]
ceil_cutoff = (gmm.means_[ceil_id] - np.sqrt(gmm.covariances_[ceil_id])*2.0).reshape(-1)[0]
global ceil_height
global max_z
ceil_height = ceil_cutoff
print(f'... done.')
print(f'Floor cutoff: {floor_cutoff}; ceiling cutoff: {ceil_cutoff} \n')
print(f'Generating the image from the pointcloud...')
df = df[df.cartesianZ > floor_cutoff]
df = df[df.cartesianZ < ceil_cutoff-2]
coords = df[["cartesianX", "cartesianY", "cartesianZ"]].copy()
minx = coords.cartesianX.min()
miny = coords.cartesianY.min()
minz = coords.cartesianZ.min()
maxx = coords.cartesianX.max()
maxy = coords.cartesianY.max()
maxz = coords.cartesianZ.max()
max_z = maxz
if not load_file:
coords.cartesianX -= minx
coords.cartesianY -= miny
coords.cartesianZ -= minz
coords.cartesianX *= resolution
coords.cartesianX = coords.cartesianX.round().astype(np.int32)
coords.cartesianY *= resolution
coords.cartesianY = coords.cartesianY.round().astype(np.int32)
coords.cartesianZ *= resolution
coords.cartesianZ = coords.cartesianZ.round().astype(np.int32)
maxZ = coords.cartesianZ.max()
minZ = 0
coords.groupby(["cartesianX", "cartesianY"]).max()
cartesianX = coords["cartesianX"].values
cartesianY = coords["cartesianY"].values
cartesianZ = coords["cartesianZ"].values
img = np.zeros((cartesianX.max() + 1, cartesianY.max() + 1), np.uint8)
# normalize between 0 and 255
for x, y, z in zip(cartesianX, cartesianY, cartesianZ):
img[x][y] = max(img[x][y], (255 * (z - minZ)) / (maxZ - minZ))
print(f'... done. /n')
print(f'Saving the image...')
img = Image.fromarray(img, "L")
img.save(f'data/' + fname_img_original)
print(f'... done.')
img_input = np.array(img)
# img_input = cv2.imread(f'data/' + fname_img_original)
else:
img_input = cv2.imread(f'data/img_original.png', 0)
return img_input, {'x': (minx, maxx), 'y': (miny, maxy), 'z': (minz, maxz)}
def separate_wall(img_:np.ndarray, threshold:np.float=3.0)-> Tuple[np.ndarray, np.ndarray]:
"""
Naive wall vs obstacles separations
Parameters
----------
img_ : the image input matrix
threshold : percentage threshold to define wall vs everytihng else
Returns : Tuple[obstacles:np.ndarray, walls: np.ndarray]
-------
"""
img_original = copy.deepcopy(img_)
img_wall = copy.deepcopy(img_) * 0
img_obstacles = copy.deepcopy(img_)
def point_to_line(point, p1, p2):
# line between p1, p2
# check calculate shapes
assert p1.shape[0] == 2
assert p2.shape[0] == 2
assert point.shape[1] == 2
zaeler = np.abs((p2[1] - p1[1]) * point[:, 0] - (p2[0] - p1[0]) * point[:, 1] + p2[0] * p1[1] - p2[1] * p1[0])
nenner = np.sqrt(np.square(p2[1] - p1[1]) + np.square(p2[0] - p1[0]))
return zaeler / nenner
point_coords_original_img = np.where(img_original > 3)
point_coords_original_img = np.hstack([point_coords_original_img[0].reshape([-1, 1]),
point_coords_original_img[1].reshape([-1, 1])])
hull = ConvexHull(point_coords_original_img)
hull_points = point_coords_original_img[hull.vertices]
for ix in range(hull_points.shape[0]):
start = hull_points[ix, :]
if (ix + 1) == hull_points.shape[0]:
end = hull_points[0, :]
else:
end = hull_points[ix + 1, :]
distances = point_to_line(point_coords_original_img, start, end)
thresh = np.percentile(distances, threshold) # 0.001 - one promile
zero_point_coords = point_coords_original_img[np.where(distances < thresh)]
img_obstacles[zero_point_coords[:, 0], zero_point_coords[:, 1]] = 0 # inverted?
img_wall[zero_point_coords[:, 0], zero_point_coords[:, 1]] = img_original[zero_point_coords[:, 0], zero_point_coords[:, 1]]
return img_obstacles, img_wall
def valentins_part(img_all_colors, img_original, shape_type):
img_original = morph(img_original)
all_contours = []
color_interval_len = 32
remove_last_interval = 1
for color_interval_start in range(color_interval_len, 255 - remove_last_interval * color_interval_len,
color_interval_len):
img = cv.inRange(img_original, color_interval_start, color_interval_start + color_interval_len)
contours, hierarchy = cv.findContours(img, cv2.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
img_cnt = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img_outer_cnt = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img_approx = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img_hulls = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img_area_filtered = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img_cnt_filled = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
def create_graph(vertex, color, image):
for g in range(0, len(vertex) - 1):
for y in range(0, len(vertex[0][0]) - 1):
cv.circle(image, (vertex[g][0][y], vertex[g][0][y + 1]), 3, (255, 255, 255), -1)
cv.line(image, (vertex[g][0][y], vertex[g][0][y + 1]),
(vertex[g + 1][0][y], vertex[g + 1][0][y + 1]),
color, 2)
cv.line(image, (vertex[len(vertex) - 1][0][0], vertex[len(vertex) - 1][0][1]),
(vertex[0][0][0], vertex[0][0][1]),
color, 2)
#
# for b,cnt in enumerate(contours):
# if hierarchy[0,b,3] == -1: #<-the mistake might be here
# approx = cv.approxPolyDP(cnt,0.015*cv.arcLength(cnt,True), True)
# clr = (255, 0, 0)
# create_graph(approx, clr) #function for drawing the found contours in the new img
# cv.imwrite('starg.jpg', newimg)
hierarchy = hierarchy[0] # get the actual inner list of hierarchy descriptions
if not isinstance(hierarchy, np.ndarray) or len(hierarchy.shape) == 1:
continue
# For each contour, find the bounding rectangle and draw it
for currentContour, currentHierarchy in zip(contours, hierarchy):
# x, y, w, h = cv2.boundingRect(currentContour)
# if currentHierarchy[2] < 0:
# # these are the innermost child components
# # cv2.rectangle(newimg, (x, y), (x + w, y + h), (0, 0, 255), 20)
# pass
# elif currentHierarchy[3] < 0:
# # these are the outermost parent components
# cv2.rectangle(newimg, (x, y), (x + w, y + h), (0, 255, 0), 20)
create_graph(currentContour, (0, 0, 255), img_cnt)
cv.fillPoly(img_cnt_filled, pts=[currentContour], color=(0, 255, 0))
if currentHierarchy[3] < 0:
create_graph(currentContour, (0, 0, 255), img_outer_cnt)
approx = cv.approxPolyDP(currentContour, 0.001 * cv.arcLength(currentContour, True), True)
create_graph(approx, (0, 0, 255), img_approx)
hull = approx
create_graph(hull, (0, 0, 255), img_hulls)
area = cv.contourArea(approx)
if area >= 10000:
create_graph(hull, (0, 0, 255), img_area_filtered)
create_graph(hull, (0, 0, 255), img_all_colors)
col_range_end = color_interval_start + color_interval_len
all_contours.append((currentContour, col_range_end, shape_type))
return all_contours
def export_json(img_original, all_contours, original_interval_m, ceil_height, max_z):
def map_to_interval(val, old_interval, new_interval):
A, B = old_interval
a, b = new_interval
return (val - A) * (b - a) / (B - A) + a
interval_x = (0, img_original.shape[1])
interval_y = (0, img_original.shape[0])
layers_json_format = []
for contour, height_intensity, shape_type in all_contours:
contour_points = []
for i, point in enumerate(contour):
contour_points.append({'x': round(map_to_interval(point[0][0], interval_x, original_interval_m['x']), 2),
'y': round(map_to_interval(point[0][1], interval_y, original_interval_m['y']), 2),
'id': i + 1})
layers_json_format.append(
{'points': contour_points,
'height': round(map_to_interval(height_intensity, (0, 255), original_interval_m['z']), 2),
'shape_type': shape_type,
'shapeId': str(uuid.uuid1())
})
json_exporter.export('data/result.json', layers_json_format, f'data/{fname_img_original}', ceil_height, max_z)
def image_processing(e57_path):
image_path = f'data/img_final.png'
json_path = f'data/result.json'
img_input, original_interval_m = create_image(e57_path)
img_obstacles, img_wall = separate_wall(img_input, 6)
img_all_colors = cv2.cvtColor(img_input, cv2.COLOR_GRAY2RGB)
contours_obstacles = valentins_part(img_all_colors, img_obstacles, f'obstacle')
contours_walls = valentins_part(img_all_colors, img_wall, f'wall')
cv2.imwrite(image_path, img_all_colors)
export_json(img_input, contours_obstacles + contours_walls, original_interval_m, ceil_height, max_z)
return image_path, json_path
if __name__ == "__main__":
image_processing(f'data/CustomerCenter1.e57')