def run():
fileList = os.listdir(cfg.resultsFolder)
resultsFileList = filter(lambda element: '.result' in element, fileList)
for resultsFile in resultsFileList:
resultsFilePath = cfg.resultsFolder + '/' +resultsFile
file = open(resultsFilePath, 'r')
imageResults = pickle.load(file)
boxes = imageResults['bboxes']
scores = imageResults['scores']
imagepath = imageResults['imagepath']
filename = os.path.basename(imagepath)
if boxes is None:
print 'No pedestrians found for image '+imagepath
continue
print 'Saving results for image '+filename
idx = np.where(scores > cfg.decision_threshold)
boxes = boxes[idx]
scores = scores[idx]
boxes, scores = nms.non_max_suppression_fast(boxes, scores, overlapthresh= cfg.nmsOverlapThresh)
img = Image.open(imagepath)
#Show the results on a colored image
img = drawing.drawResultsOnImage(img, boxes, scores)
io.imsave('Results/'+filename, img)
file.close()
print 'Finished!'
def run():
fileList = os.listdir(cfg.resultsFolder)
resultsFileList = filter(lambda element: '.result' in element, fileList)
for resultsFile in resultsFileList:
resultsFilePath = cfg.resultsFolder + '/' + resultsFile
file = open(resultsFilePath, 'rb')
imageResults = pickle.load(file)
boxes = imageResults['bboxes']
scores = imageResults['scores']
imagepath = imageResults['imagepath']
filename = os.path.basename(imagepath)
if boxes is None:
print('No pedestrians found for image ' + imagepath)
continue
print('Saving results for image ' + filename)
idx = np.where(scores > cfg.decision_threshold)
boxes = boxes[idx]
scores = scores[idx]
boxes, scores = nms.non_max_suppression_fast(
boxes, scores, overlapthresh=cfg.nmsOverlapThresh)
img = Image.open(imagepath)
#Show the results on a colored image
img = drawing.drawResultsOnImage(img, boxes, scores)
# print(type(img))
io.imsave('Results/' + filename, np.array(img))
file.close()
print('Finished!')
def f(resultsFile):
totalTP = np.zeros(len(detection_thresholds))
totalFN = np.zeros(len(detection_thresholds))
totalFP = np.zeros(len(detection_thresholds))
resultsFilePath = cfg.resultsFolder + '/' + resultsFile
file = open(resultsFilePath, 'r')
imageResults = pickle.load(file)
file.close()
#Retrieve the data for this result
detectedBoxes = imageResults['bboxes']
detectedScores = imageResults['scores']
imagePath = imageResults['imagepath']
modelIndexes = imageResults['model']
curThreshIDX = 0
imageFilename = os.path.basename(imagePath) # Get the filename
imageBasename = os.path.splitext(imageFilename)[0] #Take out the extension
#Find the annotations for this image.
annotationsFilePath = cfg.annotationsFolderPath + 'gt.' + imageBasename + '.txt'
annotatedBoxes = utils.readINRIAAnnotations(annotationsFilePath)
for thresh in detection_thresholds:
#Select only the bounding boxes that passed the current detection threshold
# for i in range(5):
# idx, = np.where(modelIndexes == i)
# if idx == []:
# continue
# else:
# detectedScores[idx] = detectedScores[idx] + cfg.compensate[i]
idx, = np.where(detectedScores > thresh)
if len(idx) > 0:
detectedBoxes = detectedBoxes[idx]
detectedScores = detectedScores[idx]
#Apply NMS on the selected bounding boxes
detectedBoxes, detectedScores = nms.non_max_suppression_fast(
detectedBoxes,
detectedScores,
overlapthresh=cfg.nmsOverlapThresh)
else:
detectedBoxes = []
detectedScores = []
#Compute the statistics for the current detected boxes
TP, FP, FN = eval.evaluateImage(annotatedBoxes, detectedBoxes,
detectedScores)
totalTP[curThreshIDX] += TP
totalFP[curThreshIDX] += FP
totalFN[curThreshIDX] += FN
curThreshIDX += 1
return [totalTP, totalFP, totalFN]
def testImage(imagePath, decisionThreshold, applyNMS):
fileList = os.listdir(cfg.modelRootPath)
# Filter all model files
modelsList = filter(lambda element: '.model' in element, fileList)
# Filter our specific feature method
currentModel = cfg.model + '_' + cfg.modelFeatures
currentModelsList = filter(lambda element: currentModel in element,
modelsList)
models = []
subImages = [] #To save backgorund crops
for modelname in currentModelsList:
file = open(cfg.modelRootPath + modelname, 'r')
svc = pickle.load(file)
models.append(svc)
file.close()
image = io.imread(imagePath, as_grey=True)
image = util.img_as_ubyte(
image) #Read the image as bytes (pixels with values 0-255)
rows, cols = image.shape
pyramid = tuple(pyramid_gaussian(image, downscale=cfg.downScaleFactor))
scale = 0
boxes = None
scores = None
indices = None
for p in pyramid[0:]:
#We now have the subsampled image in p
window_shape = (32, 32)
#Add padding to the image, using reflection to avoid border effects
if cfg.padding > 0:
p = pad(p, cfg.padding, 'reflect')
try:
views = view_as_windows(p, window_shape, step=cfg.window_step)
except ValueError:
#block shape is bigger than image
break
num_rows, num_cols, width, height = views.shape
for row in range(0, num_rows):
for col in range(0, num_cols):
#Get current window
subImage = views[row, col]
# subImages.append(subImage) #To save backgorund crops: Accumulate them in an array
#Extract features
feats = feature_extractor.extractFeatures(subImage)
#Obtain prediction score for each model
for model in models:
decision_func = model.decision_function(feats)
idx = models.index(model)
decision_func += cfg.compensate[idx]
if decision_func > decisionThreshold:
# if decision_func > -0.2: #For bootstrapping
# Signal found!
h, w = window_shape
scaleMult = math.pow(cfg.downScaleFactor, scale)
x1 = int(scaleMult * (col * cfg.window_step -
cfg.padding + cfg.window_margin))
y1 = int(scaleMult * (row * cfg.window_step -
cfg.padding + cfg.window_margin))
x2 = int(x1 + scaleMult * (w - 2 * cfg.window_margin))
y2 = int(y1 + scaleMult * (h - 2 * cfg.window_margin))
if (y1 > 0) and (y2 > 0):
if y2 - y1 > 330:
continue
#bootstrapping: Save image (if positive)
# print(decision_func)
# subImages.append(subImage)
bbox = (x1, y1, x2, y2)
score = decision_func[0]
if boxes is not None:
boxes = np.vstack((bbox, boxes))
scores = np.hstack((score, scores))
indices = np.hstack((idx, indices))
else:
boxes = np.array([bbox])
scores = np.array([score])
indices = np.array([idx])
break
scale += 1
# To save backgorund crops
# numSubImages = len(subImages)
# for x in range(0,10): #Save 10 crops for each background image
# randomIndex = random.randint(1,numSubImages-1) #Get a random window index
# imageName = imagePath.split('/') #Working on the crop name...
# imageName = imageName[len(imageName)-1]
# filename = (imageName[:-4]+'-'+str(x)+'.jpg')
# io.imsave('Results/'+filename, subImages[randomIndex]) #Save the crop
#end To save backgorund crops
# To save bootstrapping windows
# numSubImages = len(subImages)
# length = min(10, len(subImages))
# if length > 0:
# for x in range(0,length) : #Save windows with detections (max 10)
# if numSubImages == 1:
# randomIndex = 0
# else:
# randomIndex = random.randint(1, numSubImages-1) #Get a random window index
# imageName = imagePath.split('/') #Working on the crop name...
# imageName = imageName[len(imageName)-1]
# filename = (imageName[:-4]+'-'+str(x)+'.jpg')
# io.imsave('Bootstrapping/'+filename, subImages[randomIndex]) #Save the crop
#end To save bootstrapping windows
if applyNMS:
#From all the bounding boxes that are overlapping, take those with maximum score.
boxes, scores = nms.non_max_suppression_fast(boxes, scores,
cfg.nmsOverlapThresh)
#Color boostraping
# save_windows(boxes, imagePath)
return boxes, scores, indices
def run():
print ('Start evaluating results')
fileList = os.listdir(cfg.resultsFolder)
resultsFileList = list(filter(lambda element: '.result' in element, fileList))
detection_thresholds = np.arange(cfg.decision_threshold_min,
cfg.decision_threshold_max,
cfg.decision_threshold_step)
totalTP = np.zeros(len(detection_thresholds))
totalFN = np.zeros(len(detection_thresholds))
totalFP = np.zeros(len(detection_thresholds))
for resultsFile in resultsFileList:
resultsFilePath = cfg.resultsFolder+'/'+resultsFile
file = open(resultsFilePath, 'rb')
imageResults = pickle.load(file)
file.close()
#Retrieve the data for this result
detectedBoxes = imageResults['bboxes']
detectedScores = imageResults['scores']
imagePath = imageResults['imagepath']
curThreshIDX = 0
imageFilename = os.path.basename(imagePath) # Get the filename
imageBasename = os.path.splitext(imageFilename)[0] #Take out the extension
#Find the annotations for this image.
annotationsFilePath = cfg.annotationsFolderPath+'/'+imageBasename+'.txt'
annotatedBoxes = utils.readINRIAAnnotations(annotationsFilePath)
for thresh in detection_thresholds:
#Select only the bounding boxes that passed the current detection threshold
idx, = np.where(detectedScores > thresh)
if len(idx) > 0:
detectedBoxes = detectedBoxes[idx]
detectedScores = detectedScores[idx]
#Apply NMS on the selected bounding boxes
detectedBoxes, detectedScores = nms.non_max_suppression_fast(detectedBoxes, detectedScores, overlapthresh= cfg.nmsOverlapThresh)
else:
detectedBoxes = []
detectedScores = []
#Compute the statistics for the current detected boxes
TP, FP, FN = eval.evaluateImage(annotatedBoxes, detectedBoxes, detectedScores )
totalTP[curThreshIDX] += TP
totalFP[curThreshIDX] += FP
totalFN[curThreshIDX] += FN
curThreshIDX += 1
#Compute metrics
print (totalTP + totalFP)
detection_rate = totalTP / (totalTP + totalFN) #Tasa de deteccion
miss_rate = 1 - detection_rate #Tasa de error
fppi = totalFP / len(resultsFileList) #FPPI (Falsos positivos por imagen)
#Plot the results
plt.figure()
plt.plot(fppi, miss_rate, 'r', label='Miss-Rate vs FPPI')
plt.xlabel('FPPI ')
plt.ylabel('Error rate')
plt.title(cfg.model + ' ' + cfg.modelFeatures)
plt.legend()
plt.show()
def run():
print 'Start evaluating results'
fileList = os.listdir(cfg.resultsFolder)
resultsFileList = filter(lambda element: '.result' in element, fileList)
detection_thresholds = np.arange(cfg.decision_threshold_min,
cfg.decision_threshold_max,
cfg.decision_threshold_step)
totalTP = np.zeros(len(detection_thresholds))
totalFN = np.zeros(len(detection_thresholds))
totalFP = np.zeros(len(detection_thresholds))
for resultsFile in resultsFileList:
resultsFilePath = cfg.resultsFolder+'/'+resultsFile
file = open(resultsFilePath, 'r')
imageResults = pickle.load(file)
file.close()
#Retrieve the data for this result
detectedBoxes = imageResults['bboxes']
detectedScores = imageResults['scores']
imagePath = imageResults['imagepath']
curThreshIDX = 0
imageFilename = os.path.basename(imagePath) # Get the filename
imageBasename = os.path.splitext(imageFilename)[0] #Take out the extension
#Find the annotations for this image.
annotationsFilePath = cfg.annotationsFolderPath+'/'+imageBasename+'.txt'
annotatedBoxes = utils.readINRIAAnnotations(annotationsFilePath)
for thresh in detection_thresholds:
#Select only the bounding boxes that passed the current detection threshold
idx, = np.where(detectedScores > thresh)
if len(idx) > 0:
detectedBoxes = detectedBoxes[idx]
detectedScores = detectedScores[idx]
#Apply NMS on the selected bounding boxes
detectedBoxes, detectedScores = nms.non_max_suppression_fast(detectedBoxes, detectedScores, overlapthresh= cfg.nmsOverlapThresh)
else:
detectedBoxes = []
detectedScores = []
#Compute the statistics for the current detected boxes
TP, FP, FN = eval.evaluateImage(annotatedBoxes, detectedBoxes, detectedScores )
totalTP[curThreshIDX] += TP
totalFP[curThreshIDX] += FP
totalFN[curThreshIDX] += FN
curThreshIDX += 1
#Compute metrics
print totalTP + totalFP
detection_rate = totalTP / (totalTP + totalFN) #Tasa de deteccion
miss_rate = 1 - detection_rate #Tasa de error
fppi = totalFP / len(resultsFileList) #FPPI (Falsos positivos por imagen)
#Plot the results
plt.figure()
plt.plot(fppi, miss_rate, 'r', label='Miss-Rate vs FPPI')
plt.xlabel('FPPI ')
plt.ylabel('Error rate')
plt.title(cfg.model + ' ' + cfg.modelFeatures)
plt.legend()
plt.show()
def testImage(imagePath, decisionThreshold, applyNMS):
fileList = os.listdir(cfg.modelRootPath)
# Filter all model files
modelsList = filter(lambda element: '.model' in element, fileList)
# Filter our specific feature method
currentModel = cfg.model+'_'+cfg.modelFeatures
currentModelsList = filter(lambda element: currentModel in element, modelsList)
models = []
subImages = [] #To save backgorund crops
for modelname in currentModelsList:
file = open(cfg.modelRootPath + modelname, 'r')
svc = pickle.load(file)
models.append(svc)
file.close()
image = io.imread(imagePath, as_grey=True)
image = util.img_as_ubyte(image) #Read the image as bytes (pixels with values 0-255)
rows, cols = image.shape
pyramid = tuple(pyramid_gaussian(image, downscale=cfg.downScaleFactor))
scale = 0
boxes = None
scores = None
indices = None
for p in pyramid[0:]:
#We now have the subsampled image in p
window_shape = (32,32)
#Add padding to the image, using reflection to avoid border effects
if cfg.padding > 0:
p = pad(p,cfg.padding,'reflect')
try:
views = view_as_windows(p, window_shape, step=cfg.window_step)
except ValueError:
#block shape is bigger than image
break
num_rows, num_cols, width, height = views.shape
for row in range(0, num_rows):
for col in range(0, num_cols):
#Get current window
subImage = views[row, col]
# subImages.append(subImage) #To save backgorund crops: Accumulate them in an array
#Extract features
feats = feature_extractor.extractFeatures(subImage)
#Obtain prediction score for each model
for model in models:
decision_func = model.decision_function(feats)
idx = models.index(model)
decision_func += cfg.compensate[idx]
if decision_func > decisionThreshold:
# if decision_func > -0.2: #For bootstrapping
# Signal found!
h, w = window_shape
scaleMult = math.pow(cfg.downScaleFactor, scale)
x1 = int(scaleMult * (col*cfg.window_step - cfg.padding + cfg.window_margin))
y1 = int(scaleMult * (row*cfg.window_step - cfg.padding + cfg.window_margin))
x2 = int(x1 + scaleMult*(w - 2*cfg.window_margin))
y2 = int(y1 + scaleMult*(h - 2*cfg.window_margin))
if(y1 > 0) and (y2 > 0):
if y2 - y1 > 330:
continue
#bootstrapping: Save image (if positive)
# print(decision_func)
# subImages.append(subImage)
bbox = (x1, y1, x2, y2)
score = decision_func[0]
if boxes is not None:
boxes = np.vstack((bbox, boxes))
scores = np.hstack((score, scores))
indices = np.hstack((idx, indices))
else:
boxes = np.array([bbox])
scores = np.array([score])
indices = np.array([idx])
break
scale += 1
# To save backgorund crops
# numSubImages = len(subImages)
# for x in range(0,10): #Save 10 crops for each background image
# randomIndex = random.randint(1,numSubImages-1) #Get a random window index
# imageName = imagePath.split('/') #Working on the crop name...
# imageName = imageName[len(imageName)-1]
# filename = (imageName[:-4]+'-'+str(x)+'.jpg')
# io.imsave('Results/'+filename, subImages[randomIndex]) #Save the crop
#end To save backgorund crops
# To save bootstrapping windows
# numSubImages = len(subImages)
# length = min(10, len(subImages))
# if length > 0:
# for x in range(0,length) : #Save windows with detections (max 10)
# if numSubImages == 1:
# randomIndex = 0
# else:
# randomIndex = random.randint(1, numSubImages-1) #Get a random window index
# imageName = imagePath.split('/') #Working on the crop name...
# imageName = imageName[len(imageName)-1]
# filename = (imageName[:-4]+'-'+str(x)+'.jpg')
# io.imsave('Bootstrapping/'+filename, subImages[randomIndex]) #Save the crop
#end To save bootstrapping windows
if applyNMS:
#From all the bounding boxes that are overlapping, take those with maximum score.
boxes, scores = nms.non_max_suppression_fast(boxes, scores, cfg.nmsOverlapThresh)
#Color boostraping
# save_windows(boxes, imagePath)
return boxes, scores, indices
