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MarkProcess.py
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MarkProcess.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 8 16:45:36 2019
Copyright © 2019 DataRock S.A.S. All rights reserved.
@author: DavidFelipe
Select the objects that would be analized for each layer
"""
try:
import numpy as np
from operator import itemgetter
import time
import progressbar
import cv2
except:
print(" PLEASE REVIEW THE MODULES THAT NEEDS THE SOFTWARE - AN ERROR WAS OCCURRED")
print(" %% THIRD MODULE %%")
print(" -- Select the objects from the minimum -- Check the current progress --")
class MarkProcess:
"""
Procedure to process the coordinates and the geometric properties of each object
segmented and found
"""
def __init__(self, imageRGB, diameter_range, flagMinimize):
print("MarkProcess Process")
self.image = imageRGB
self.diameter = diameter_range
self.flag = flagMinimize
self.diameterMark = 16
self.markGate = 58
def objectMatch(self, vectorL1, vectorL2, vectorL3):
"""
Each vector is a list of three vectors containing the information
of each sublayer of each layer
"""
print(" ")
print("MarkProcess object match process ")
widgets = [progressbar.Percentage(),
' ', progressbar.Bar(),
' ', progressbar.ETA(),
' ', progressbar.AdaptiveETA()]
bar = progressbar.ProgressBar(widgets=widgets, maxval=3)
bar.start()
inicio = time.time()
L1_organized = self.layer_organize(vectorL1)
L2_organized = self.layer_organize(vectorL2)
L3_organized = self.layer_organize(vectorL3)
bar.update(1)
L1_minimized =[]
L2_minimized =[]
L3_minimized =[]
bar.update(2)
if(self.flag == 1):
### Minimize the vector sparse
L10_minimize = self.minimize(L1_organized[0], self.diameter)
L11_minimize = self.minimize(L1_organized[1], self.diameter)
L12_minimize = self.minimize(L1_organized[2], self.diameter)
L20_minimize = self.minimize(L2_organized[0], self.diameter)
L21_minimize = self.minimize(L2_organized[1], self.diameter)
L22_minimize = self.minimize(L2_organized[2], self.diameter)
L3_minimized = self.minimize(L3_organized, self.diameter)
L1_minimized = [L10_minimize, L11_minimize, L12_minimize]
L2_minimized = [L20_minimize, L21_minimize, L22_minimize]
final = time.time() - inicio
print(final)
bar.update(3)
print(" ")
print("MarkProcess Ended with minimize function")
return L1_minimized, L2_minimized, L3_minimized
else:
final = time.time() - inicio
print(final)
bar.update(3)
print(" ")
print("MarkProcess Ended - organized")
return L1_organized, L2_organized, L3_organized
def layer_organize(self, vector_layer):
if(type(vector_layer) == list):
vector1_organized = self.organize_vector(vector_layer[0])
vector2_organized = self.organize_vector(vector_layer[1])
vector3_organized = self.organize_vector(vector_layer[2])
vector_organized = [vector1_organized, vector2_organized, vector3_organized]
else:
vector_organized = self.organize_vector(vector_layer)
return vector_organized
def organize_vector(self, vector):
vector_metrics = np.array([0,0,0,0])
x,y = vector.shape
for i in range(1,x):
line = vector[i]
w = line[2]
h = line[3]
x1 = int(w / 2)
y1 = int(h / 2)
cx = int(line[0] + x1)
cy = int(line[1] + y1)
distance = (cx**2) + (cy**2)
distance = int(np.sqrt(distance))
area = int(w * h)
line_block = np.array([distance, cx, cy, area])
vector_metrics = np.vstack((vector_metrics, line_block))
vector_sort = vector_metrics[vector_metrics[:,0].argsort()]
return vector_sort
def minimize(self, vector, diameter):
"""
Function to compare and delete some points
"""
x, y = vector.shape
widgets = [progressbar.Percentage(),
' ', progressbar.Bar(),
' ', progressbar.ETA(),
' ', progressbar.AdaptiveETA()]
bar2 = progressbar.ProgressBar(widgets=widgets, maxval=x)
bar2.start()
minimize_vector = np.array([0,0,0,0] )
c = 1
j = 2
while(c <= x-2):
line = vector[c]
val = line[0]
array = np.copy(line)
counter = 0
while(j <= x-1):
lineCompare = vector[j]
# print(lineCompare[0])
difference = lineCompare[0] - val
if(difference <= diameter):
array = np.vstack((array, lineCompare))
counter += 1
elif(difference > diameter):
try:
distance_mean = np.mean(array[:,0])
cx_mean = np.average(array[:,1])
cy_mean = np.average(array[:,2])
area_mean = np.average(array[:,3])
result = np.array([distance_mean, cx_mean, cy_mean, area_mean])
c = c + counter
except:
result = array
break
j += 1
minimize_vector = np.vstack((minimize_vector, result))
c += 1
j = c + 1
# print(c)
bar2.update(c)
bar2.update(x)
return minimize_vector
def imageMatch(self, vector):
"""
Process to mark the coordinates in the current image process
"""
print(" ")
x,y = vector.shape
widgets = [progressbar.Percentage(),
' ', progressbar.Bar(),
' ', progressbar.ETA(),
' ', progressbar.AdaptiveETA()]
bar = progressbar.ProgressBar(widgets=widgets, maxval=x)
bar.start()
mark_image = np.copy(self.image)
for i in range(0, x):
bar.update(i)
line = vector[i]
cx = int(line[1])
cy = int(line[2])
cv2.circle(mark_image, (cx, cy), 10, (255, 255, 255), 2)
cv2.circle(mark_image, (cx, cy), 4, (0, 0, 255), -1)
bar.update(x)
return mark_image
def meanGeometry(self, vector):
if(type(vector) == list):
vector1 = vector[0]
vector2 = vector[1]
vector3 = vector[2]
vector1_mean = np.mean(vector1[:,3])
vector2_mean = np.mean(vector2[:,3])
vector3_mean = np.mean(vector3[:,3])
vector_mean = np.array([vector1_mean, vector2_mean, vector3_mean])
else:
vector_mean = np.mean(vector[:,3])
return vector_mean
def imageGrouped(self, vector):
"""
Process to mark the coordinates in the current image process
"""
mask_image = np.zeros_like(self.image)
mark_image = np.copy(self.image)
x,y = vector.shape
for i in range(0, x):
line = vector[i]
cx = int(line[1])
cy = int(line[2])
cv2.circle(mask_image, (cx, cy), self.diameterMark, (255, 255, 255), -1) #### Aqui hay un valor que puede cambiar dependiendo de la escala
cv2.circle(mark_image, (cx, cy), 10, (255, 255, 255), 2)
cv2.circle(mark_image, (cx, cy), 4, (0, 0, 255), -1)
return mark_image, mask_image
def Vagroup(self, vector1, vector2, vector3, vector4):
grouped = np.vstack([vector1, vector2, vector3, vector4])
grouped_organized = self.layer_organize(grouped)
grouped_minimized = self.minimize(grouped_organized, 8)
## With the minimized function
image_minimized = self.imageGrouped(grouped_minimized)
## Raw vector integrated
image_grouped, mask_image = self.imageGrouped(grouped)
grouped_vector = [grouped, grouped_organized, grouped_minimized]
return image_grouped, mask_image, grouped_vector
def countIntegration(self, image, mask_image):
x, y, _ = image.shape
image_mark = np.copy(self.image)
gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY)
contours,hierachy = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
counter = 0
vector_object = np.array([0,0,0,0])
for (i, contour) in enumerate(contours):
(x, y, w, h) = cv2.boundingRect(contour)
contour_valid = (w >= 5) and (
h >= 5) and (w <= 500) and (h <= 500)
if not contour_valid:
continue
## Definimos bounding box para el tracker
boundingBox = np.array([x,y,w,h])
## Definimos tipo de Tracker
# getting center of the bounding box
x1 = int(w / 2)
y1 = int(h / 2)
cx = x + x1
cy = y + y1
if(w > self.markGate or h > self.markGate):
cv2.circle(image_mark, (cx, cy), 14, (255, 255, 255), 2)
cv2.circle(image_mark, (cx, cy), 7, (255, 0, 0), -1)
cv2.circle(mask_image, (cx, cy), 6, (0, 0, 255), -1)
counter += 2
elif(w > self.markGate and h > self.markGate):
counter += 3
cv2.circle(image_mark, (cx, cy), 14, (255, 255, 255), 2)
cv2.circle(image_mark, (cx, cy), 7, (0, 255, 0), -1)
cv2.circle(mask_image, (cx, cy), 6, (0, 255, 0), -1)
else :
cv2.circle(image_mark, (cx, cy), 10, (255, 255, 255), 2)
cv2.circle(image_mark, (cx, cy), 4, (0, 0, 255), -1)
cv2.circle(mask_image, (cx, cy), 4, (255, 0, 0), -1)
counter += 1
## Vector of the current object
vector_object = np.vstack((vector_object, boundingBox))
return image_mark, mask_image, vector_object, counter