def calc_final_match(modelFoto, inputFoto):
primary = readOpenPoseJson.readJsonFile('json_data/' + modelFoto + '.json')
secondary = readOpenPoseJson.readJsonFile('json_data/' + inputFoto + '.json')
#parameters van functie zijn slecht gekozen => secondary = input && primary = model
result = calcEucldError.norm_cte_decide_match_or_not(secondary, primary)
print("Match or not: ", result)
return result
def calc_match(modelFoto, inputFoto):
# 2D array. Elke entry is een coordinatenkoppel(joint-point) in 3D , z-coordinaat wordt nul gekozen want we werken in 2D
# Bevat 18 coordinatenkoppels want openpose returnt 18 joint-points
primary = readOpenPoseJson.readJsonFile('json_data/' + modelFoto + '.json')
secondary = readOpenPoseJson.readJsonFile('json_data/' + inputFoto + '.json')
#parameters van functie zijn slecht gekozen => secondary = input && primary = model
result = calcEucldError.norm_cte(secondary, primary)
return result
resolutieX = 1440 resolutieY = 1920 modelFoto = "foto2" inputFoto = "foto1_rendered" #2D array. Elke entry is een coordinatenkoppel(joint-point) in 3D , z-coordinaat wordt nul gekozen want we werken in 2D #Voorbeeld; # primary = np.array([[11,4,0], # [18,7,0], # [3,11,0], # [11, 11, 0], # [12, 19, 0], # [17, 26, 0], # [5, 26, 0]]) #Bevat 18 coordinatenkoppels want openpose returnt 18 joint-points primary = readOpenPoseJson.readJsonFile(modelFoto + '.json') secondary = readOpenPoseJson.readJsonFile(inputFoto + '.json') # secondary = np.array([[35,3,0], # [46,20,0], # [22,13,0], # [35, 13, 0], # [35, 24, 0], # [43,34, 0], # [26, 35, 0]]) #Zoek 2D affine transformatie matrix om scaling, rotatatie en translatie te beschrijven tussen model en input #2x2 matrix werkt niet voor translaties # Pad the data with ones, so that our transformation can do translations too n = primary.shape[0] pad = lambda x: np.hstack([x, np.ones((x.shape[0], 1))]) #horizontaal stacken
def doCalc(modelFoto, inputFoto):
# plot vars
markersize = 3
im_model = plt.imread('Pictures/' + modelFoto + '.jpg')
# print("res: ", im_model.shape[0])
# portrait foto, gsm jochen
resolutieX_model = im_model.shape[1] # 1440
resolutieY_model = im_model.shape[0] # 1920
im_input = plt.imread('Pictures/' + inputFoto + '.jpg')
# rendered foto
resolutieX = im_input.shape[1] # 1440 #1280
resolutieY = im_input.shape[0] # 1920 #720
# 2D array. Elke entry is een coordinatenkoppel(joint-point) in 3D , z-coordinaat wordt nul gekozen want we werken in 2D
# Bevat 18 coordinatenkoppels want openpose returnt 18 joint-points
primary = readOpenPoseJson.readJsonFile('json_data/' + modelFoto + '.json')
secondary = readOpenPoseJson.readJsonFile('json_data/' + inputFoto +
'.json')
primary_fixed_points = np.vstack(
[primary[0:3], primary[5], primary[8], primary[11]])
secondary_fixed_points = np.vstack(
[secondary[0:3], secondary[5], secondary[8], secondary[11]])
(modelTransform_fixed_points, A_fixed_points
) = calcTransformationMatrix.calcTransformationMatrix_fixed_points(
secondary_fixed_points, primary_fixed_points, secondary)
plot = False
if plot:
f, (ax1, ax2, ax4) = plt.subplots(1, 3, sharey=True, figsize=(14, 6))
implot = ax1.imshow(im_model)
ax1.set_title(modelFoto + '(model)')
ax1.plot(*zip(*primary),
marker='o',
color='r',
ls='',
label='model',
ms=markersize) # ms = markersize
ax1.plot(*zip(*primary_fixed_points),
marker='o',
color='g',
ls='',
label='model',
ms=markersize) # ms = markersize
red_patch = mpatches.Patch(color='red', label='model')
ax1.legend(handles=[red_patch])
ax2.set_title(inputFoto)
ax2.imshow(im_input)
ax2.plot(*zip(*secondary), marker='o', color='b', ls='', ms=markersize)
ax2.legend(handles=[mpatches.Patch(color='blue', label='input')])
ax4.set_title('Transformatie + model')
ax4.imshow(im_model)
ax4.plot(*zip(*primary), marker='o', color='b', ls='', ms=markersize)
ax4.plot(*zip(*modelTransform_fixed_points),
marker='o',
color='cyan',
ls='',
ms=markersize)
plt.draw()
plt.show()
# Gewoon MAX[ xi-x'i en yi-y'i ]
maxError = np.abs(primary - modelTransform_fixed_points)
# np.sqrt(np.sum((maxError[:,0] - maxError[:,1]) ** 2))
euclDis = ((maxError[:, 0])**2 + maxError[:, 1]**2)**0.5
euclDisNorm = ((maxError[:, 0] / resolutieX_model)**2 +
(maxError[:, 1] / resolutieY_model)**2)**0.5
max_euclDis = max(euclDis)
sum_max_euclDis = np.sum(euclDis)
return [max_euclDis, sum_max_euclDis]
markersize = 3
im_model = plt.imread('Pictures/' + modelFoto + '.jpg')
# print("res: ", im_model.shape[0])
# portrait foto, gsm jochen
resolutieX_model = im_model.shape[1] # 1440
resolutieY_model = im_model.shape[0] # 1920
im_input = plt.imread('Pictures/' + inputFoto + '.jpg')
# rendered foto
resolutieX = im_input.shape[1] # 1440 #1280
resolutieY = im_input.shape[0] # 1920 #720
# 2D array. Elke entry is een coordinatenkoppel(joint-point) in 3D , z-coordinaat wordt nul gekozen want we werken in 2D
# Bevat 18 coordinatenkoppels want openpose returnt 18 joint-points
primary = readOpenPoseJson.readJsonFile('json_data/' + modelFoto + '.json')
secondary = readOpenPoseJson.readJsonFile('json_data/' + inputFoto + '.json')
# enkel subarrays
# NEK WORDT MOMENTEEL NIET GEBRUIKT, geeft minder nauwkeurige resultaten ??
primary_torso = primary[2:8]
secondary_torso = secondary[2:8]
primary_legs = primary[8:14]
secondary_legs = secondary[8:14]
primary_face = np.vstack([primary[0], primary[14:18]])
secondary_face = np.vstack([secondary[0], secondary[14:18]])
(modelTransform_torso, A_torso) = calcTransformationMatrix.calcTransformationMatrix(primary_torso, secondary_torso)
(modelTransform_legs, A_legs) = calcTransformationMatrix.calcTransformationMatrix(primary_legs, secondary_legs)
def matchPose(modelP, inputP):
modelFoto = modelP #"foto3"
inputFoto = inputP #"foto1"
# plot vars
markersize = 3
im_model = plt.imread('Pictures/' + modelFoto + '.jpg')
print("res: " , im_model.shape[0])
# portrait foto, gsm jochen
resolutieX_model = im_model.shape[1] # 1440
resolutieY_model = im_model.shape[0] # 1920
im_input = plt.imread('Pictures/' + inputFoto + '.jpg')
# rendered foto
resolutieX = im_input.shape[1] # 1440 #1280
resolutieY = im_input.shape[0] # 1920 #720
# 2D array. Elke entry is een coordinatenkoppel(joint-point) in 3D , z-coordinaat wordt nul gekozen want we werken in 2D
# Voorbeeld;
# primary = np.array([[11,4,0],
# [18,7,0],
# [3,11,0],
# [11, 11, 0],
# [12, 19, 0],
# [17, 26, 0],
# [5, 26, 0]])
# Bevat 18 coordinatenkoppels want openpose returnt 18 joint-points
primary = readOpenPoseJson.readJsonFile('json_data/' + modelFoto + '.json')
secondary = readOpenPoseJson.readJsonFile('json_data/' + inputFoto + '.json')
# secondary = np.array([[35,3,0],
# [46,20,0],
# [22,13,0],
# [35, 13, 0],
# [35, 24, 0],
# [43,34, 0],
# [26, 35, 0]])
#FILTERING & STRIPPING OF UNDETECTED JOINTS
#Check if all joints are detected. Undetected joints are undefinded (0,0)
#Remove undetected joints from matrix
for i in range(0,17):
if i < secondary.shape[0]:
if(secondary[i][0] == 0 and secondary[i][1] ==0):
print("no_joint! ")
secondary = np.delete(secondary, (i), axis=0)
primary = np.delete(primary, (i), axis=0)
i = i-1
#enkel subarrays
#primary = primary[8:14]
#secondary = secondary[8:14]
(modelTransform, A) = calcTransformationMatrix.calcTransformationMatrix(primary, secondary)
print("A- transformatie matrix:")
print(A)
print("Input:")
print(secondary)
print("Result- transformatie van model:")
print(modelTransform)
#print("eerste kolom:")
#print(modelTransform[:,0])
#bereken correlatie tussen 2 matrixen
print("###correlation: ")
print(np.corrcoef(modelTransform[:,0]/resolutieX, secondary[:,0]/resolutieY))
print(np.corrcoef(modelTransform[:, 1]/resolutieX, secondary[:, 1]/resolutieY))
print("matric correlation: " )
print(util.corr2_coeff(modelTransform, secondary).min())
# Gewoon MAX[ xi-x'i en yi-y'i ]
maxError = np.abs(secondary - modelTransform)
print("Max error:", maxError.max())
# print(maxError)
# np.sqrt(np.sum((maxError[:,0] - maxError[:,1]) ** 2))
euclDis = ((maxError[:, 0]) ** 2 + maxError[:, 1] ** 2) ** 0.5
euclDisNorm = ((maxError[:, 0] / resolutieX) ** 2 + (maxError[:, 1] / resolutieY) ** 2) ** 0.5
# Euclidean distance boven gwn coordinaten verschil want eucl dis is ne cirkel
# print("euclidean dis: ", euclDis)
max_euclDis = max(euclDis)
print("\n-------- Euclidean dis -------")
print("Max euclidean dis: ", max_euclDis)
print("Body part: ", util.getBodyPartByIndex(np.argmax(euclDis, axis=None)))
max_euclDis_norm = max(euclDisNorm)
sum_max_euclDis_norm = np.sum(euclDisNorm)
print("\n----- Euclidean dis genormaliseeerd -----")
print("euclid dis NORM; ", euclDisNorm)
print("Max euclidean dis norm resolutie: ", max_euclDis_norm)
print("Sum euclidean dis: ", sum_max_euclDis_norm)
print("Body part: ", util.getBodyPartByIndex(np.argmax(euclDisNorm, axis=None)))
# plot img
f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, sharey=True, figsize=(14, 6))
implot = ax1.imshow(im_model)
ax1.set_title(modelFoto + '(model)')
ax1.plot(*zip(*primary), marker='o', color='r', ls='', label='model', ms=markersize) # ms = markersize
red_patch = mpatches.Patch(color='red', label='model')
ax1.legend(handles=[red_patch])
ax2.set_title(inputFoto)
ax2.imshow(im_input)
ax2.plot(*zip(*secondary), marker='o', color='b', ls='', ms=markersize)
ax2.legend(handles=[mpatches.Patch(color='blue', label='input')])
ax3.set_title('Transformation of model')
ax3.imshow(im_input)
ax3.plot(*zip(*modelTransform), marker='o', color='y', ls='', ms=markersize)
ax3.legend(handles=[mpatches.Patch(color='yellow', label='Transformation of model')])
ax4.set_title('Transformatie + input')
ax4.imshow(im_input)
ax4.plot(*zip(*secondary), marker='o', color='b', ls='', ms=markersize)
ax4.plot(*zip(*modelTransform), marker='o', color='y', ls='', ms=markersize)
print("Evaluation poses: ", evaluateError(max_euclDis_norm, sum_max_euclDis_norm))
plt.show()
return (max_euclDis, max_euclDis_norm, sum_max_euclDis_norm);
