def save_windows(boxes, imagePath):
image_color = io.imread(imagePath, as_grey=False)
image_color = util.img_as_ubyte(image_color)
imageFilename = os.path.basename(imagePath) # Get the filename
imageBasename = os.path.splitext(imageFilename)[0] #Take out the extension
annotationsFilePath = cfg.annotationsFolderPath + 'gt.' + imageBasename + '.txt'
annotatedBoxes = utils.readINRIAAnnotations(annotationsFilePath)
signalTypes = utils.readINRIAAnnotationsDetection(annotationsFilePath)
signalTypes = list(reversed(signalTypes))
count = 0
for box in boxes:
if box[0] < 0 or box[1] < 0:
continue
if box[2] >= image_color.shape[1].__int__() or \
box[3] >= image_color.shape[0].__int__():
continue
annotated = 'NONSIGNAL'
for idx in range(0, len(annotatedBoxes)):
aBox = annotatedBoxes[idx]
currentRatio = computeOverlap(box, aBox)
currentRatio = math.ceil(currentRatio * 10) / 10
if currentRatio > 0.5:
annotated = signalTypes[idx]
break
crop = image_color[box[1]:box[3], box[0]:box[2]]
imageName = imagePath.split('/') #Working on the crop name...
fileName = imageName[len(imageName) - 1]
fileName = fileName[:len(fileName) - 4]
fileName = (fileName + '.' + str(count))
filename = (fileName + '.' + annotated + '.jpg')
crop = resize(crop, (32, 32))
io.imsave('Crops/' + filename, crop) #Save the crop
print('Crop saved')
count += 1
def save_windows(boxes, imagePath):
image_color = io.imread(imagePath, as_grey=False)
image_color = util.img_as_ubyte(image_color)
imageFilename = os.path.basename(imagePath) # Get the filename
imageBasename = os.path.splitext(imageFilename)[0] #Take out the extension
annotationsFilePath = cfg.annotationsFolderPath+'gt.'+imageBasename+'.txt'
annotatedBoxes = utils.readINRIAAnnotations(annotationsFilePath)
signalTypes = utils.readINRIAAnnotationsDetection(annotationsFilePath)
signalTypes = list(reversed(signalTypes))
count = 0
for box in boxes:
if box[0] < 0 or box[1] < 0:
continue
if box[2] >= image_color.shape[1].__int__() or \
box[3] >= image_color.shape[0].__int__():
continue
annotated = 'NONSIGNAL'
for idx in range(0, len(annotatedBoxes)):
aBox = annotatedBoxes[idx]
currentRatio = computeOverlap(box, aBox)
currentRatio = math.ceil(currentRatio*10)/10
if currentRatio > 0.5:
annotated = signalTypes[idx]
break
crop = image_color[box[1]:box[3],box[0]:box[2]]
imageName = imagePath.split('/') #Working on the crop name...
fileName = imageName[len(imageName)-1]
fileName = fileName[:len(fileName)-4]
fileName = (fileName+'.'+str(count))
filename = (fileName+'.'+annotated+'.jpg')
crop = resize(crop,(32,32))
io.imsave('Crops/'+filename, crop) #Save the crop
print('Crop saved')
count += 1
def f(resultsFile, model):
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
idx, = np.where(detectedScores > thresh)
if len(idx) > 0:
detectedBoxes = detectedBoxes[idx]
detectedScores = detectedScores[idx]
modelIndexes = modelIndexes[idx]
#Apply NMS on the selected bounding boxes
detectedBoxes, detectedScores, modelIndexes = nms_comp.non_max_suppression_fast(
detectedBoxes,
detectedScores,
modelIndexes,
overlapthresh=cfg.nmsOverlapThresh)
else:
detectedBoxes = []
detectedScores = []
modelIndexes = []
#Compute the statistics for the current detected boxes
TP, FP, FN = eval.evaluateImage(
annotatedBoxes, detectedBoxes, modelIndexes, model,
detectedScores) #For model compensation
totalTP[curThreshIDX] += TP
totalFP[curThreshIDX] += FP
totalFN[curThreshIDX] += FN
curThreshIDX += 1
return [totalTP, totalFP, totalFN]
def f(resultsFile, model):
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
idx, = np.where(detectedScores > thresh)
if len(idx) > 0:
detectedBoxes = detectedBoxes[idx]
detectedScores = detectedScores[idx]
modelIndexes = modelIndexes[idx]
#Apply NMS on the selected bounding boxes
detectedBoxes, detectedScores, modelIndexes = nms_comp.non_max_suppression_fast(detectedBoxes, detectedScores, modelIndexes, overlapthresh= cfg.nmsOverlapThresh)
else:
detectedBoxes = []
detectedScores = []
modelIndexes = []
#Compute the statistics for the current detected boxes
TP, FP, FN = eval.evaluateImage(annotatedBoxes, detectedBoxes, modelIndexes, model, detectedScores ) #For model compensation
totalTP[curThreshIDX] += TP
totalFP[curThreshIDX] += FP
totalFN[curThreshIDX] += FN
curThreshIDX += 1
return [totalTP, totalFP, totalFN]
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()