def experiment2(annotation_file, detector, outputs, exp_name):
def getdata(transforms):
dataset = Dataset(annotation_file, transforms)
return dataset
transforms = [[], [FlipImage()], [BlurImage(1)], [RescaleImage(2.0)],
[RescaleImage(0.5)], [RotateImage(-90)], [RotateImage(45)]]
rows = 2
columns = 4
fig = plt.figure()
axes = []
for i, transform in enumerate(transforms):
dataset = getdata(transform)
img = dataset[3]["image"]
preds = detector(img)
img = img * 255
img = np.array(img.transpose(1, 2, 0), dtype=np.uint8)
print(f"Annotating Image {exp_name} {i}")
img = plot_boxes(img, preds)
img.save(f"{outputs}/{exp_name}_{i}.jpg")
axes.append(fig.add_subplot(rows, columns, i + 1))
axes[-1].set_title(f"Subplot {i+1}")
plt.imshow(img)
fig.tight_layout()
plt.savefig(f"{outputs}/{exp_name}.jpg")
def predictor(image, image_dict, detector):
image_dict['bboxes'] = []
pred_boxes, pred_class, pred_score = detector(
np.transpose(np.array(image), (2, 0, 1)) / 255)
if len(pred_score) > 5: # top 5 confident predictions
pred_score = pred_score[:5]
pred_boxes = pred_boxes[:5]
pred_class = pred_class[:5]
for k in range(len(pred_score)):
my_dict = {}
my_dict['bbox'] = list(chain.from_iterable(pred_boxes[k]))
for j in range(len(my_dict['bbox'])):
my_dict['bbox'][j] = int(my_dict['bbox'][j])
my_dict['category'] = pred_class[k]
image_dict['bboxes'].append(my_dict)
return plot_boxes(image_dict=image_dict, image=image)
def experiment(annotation_file, detector, transforms, outputs):
'''
Function to perform the desired experiments
Arguments:
annotation_file: Path to annotation file
detector: The object detector
transforms: List of transformation classes
outputs: path of the output folder to store the images
'''
# Create the instance of the dataset.
dataset = Dataset(annotation_file, transforms)
for i, data in enumerate(dataset):
img = data["image"]
preds = detector(img)
img = img * 255
img = np.array(img.transpose(1, 2, 0), dtype=np.uint8)
print(f"Annotating Image {i}")
img = plot_boxes(img, preds)
img.save(f"{outputs}/{i}.jpg")
def experiment(annotation_file, detector, transforms, outputs):
'''
Function to perform the desired experiments
Arguments:
annotation_file: Path to annotation file
detector: The object detector
transforms: List of transformation classes
outputs: path of the output folder to store the images
'''
#Create the instance of the dataset.
d = Dataset(annotation_file, transforms)
#Iterate over all data items.
for i in range(len(d)):
image, image_dict = d[i]
#Get the predictions from the detector.
pred_boxes, pred_class, pred_score = detector(
np.transpose(np.array(image), (2, 0, 1)) / 255)
if len(pred_score) > 5: # top 5 confident predictions
pred_score = pred_score[:5]
pred_boxes = pred_boxes[:5]
pred_class = pred_class[:5]
image_dict['bboxes'] = []
for k in range(len(pred_score)):
my_dict = {}
my_dict['bbox'] = list(chain.from_iterable(pred_boxes[k]))
for j in range(len(my_dict['bbox'])):
my_dict['bbox'][j] = int(my_dict['bbox'][j])
my_dict['category'] = pred_class[k]
image_dict['bboxes'].append(my_dict)
#Draw the boxes on the image and save them.
image = plot_boxes(location='./output/',
image_dict=image_dict,
image=image,
saver=1)
#Do the required analysis experiments.
my_image = Image.open('./data/imgs/9.jpg')
x, y = my_image.size
# no transformation
d = Dataset(annotation_file, transforms=[])
my_image, my_image_dict = d[9]
plt.subplot(2, 4, 1)
plt.title('Original Image')
plt.imshow(predictor(my_image, my_image_dict, detector))
# horizontal flipping
d = Dataset(annotation_file, transforms=[FlipImage()])
image, image_dict = d[9]
plt.subplot(2, 4, 2)
plt.title('Horizontally Flipped Image')
plt.imshow(predictor(image, image_dict, detector))
# blurring
d = Dataset(annotation_file, transforms=[BlurImage(radius=3)])
image, image_dict = d[9]
plt.subplot(2, 4, 3)
plt.title('Blurred Image (Radius = 3)')
plt.imshow(predictor(image, image_dict, detector))
# 2x rescaling
d = Dataset(annotation_file, transforms=[RescaleImage((2 * y, 2 * x))])
image, image_dict = d[9]
plt.subplot(2, 4, 4)
plt.title('2x Rescaled Image')
plt.imshow(predictor(image, image_dict, detector))
# 0.5x rescaling
d = Dataset(annotation_file, transforms=[RescaleImage((y // 2, x // 2))])
image, image_dict = d[9]
plt.subplot(2, 4, 5)
plt.title('0.5x Rescaled Image')
plt.imshow(predictor(image, image_dict, detector))
# 90 degree rotation to right
d = Dataset(annotation_file, transforms=[RotateImage(360 - 90)])
image, image_dict = d[9]
plt.subplot(2, 4, 6)
plt.title('90 degree Rotated Image')
plt.imshow(predictor(image, image_dict, detector))
# 45 degree rotation to left
d = Dataset(annotation_file, transforms=[RotateImage(45)])
image, image_dict = d[9]
plt.subplot(2, 4, 7)
plt.title('-45 degree Rotated Image')
plt.imshow(predictor(image, image_dict, detector))
plt.show()
def experiment(annotation_file, detector, transforms = None, outputs = None):
'''
Function to perform the desired experiments
Arguments:
annotation_file: Path to annotation file
detector: The object detector
transforms: List of transformation classes
outputs: path of the output folder to store the images
'''
#Create the instance of the dataset.
d = Dataset(annotation_file, transforms)
#Iterate over all data items.
for i in range(len(d)):
#Get the predictions from the detector.
pred = detector(d[i]['image'])
#Draw the boxes on the image and save them.
plot_boxes(outputs+str(i)+".png",d[i]['image'],pred)
#Do the required analysis experiments.
fp = "./outputs/"
img1 = d[1]['image']
pred = detector(img1)
n_img = plot_boxes(fp + "original.png", img1, pred)
plt.subplot(2,4,1)
plt.imshow(n_img)
plt.title("Original")
d = Dataset(annotation_file,[FlipImage('horizontal')])
img2 = d[1]['image']
pred = detector(img2)
n_img = plot_boxes(fp + "flip.png", img2, pred)
plt.subplot(2,4,2)
plt.imshow(n_img)
plt.title("Horizontally flip")
d = Dataset(annotation_file,[BlurImage(3)])
img3 = d[1]['image']
pred = detector(img3)
n_img = plot_boxes(fp+"blur.png", img3, pred)
plt.subplot(2,4,3)
plt.imshow(n_img)
plt.title("Blurred")
d = Dataset(annotation_file,[RescaleImage((2*img3.shape[2],2*img3.shape[1]))])
img4 = d[1]['image']
pred = detector(img4)
n_img = plot_boxes(fp + "2x.png", img4, pred)
plt.subplot(2,4,4)
plt.imshow(n_img)
plt.title("2X")
d = Dataset(annotation_file,[RescaleImage((round(0.5*img3.shape[2]),round(0.5*img3.shape[1])))])
img5 = d[1]['image']
pred = detector(img5)
n_img = plot_boxes(fp + "halfx.png", img5, pred)
plt.subplot(2,4,5)
plt.imshow(n_img)
plt.title("0.5X")
d = Dataset(annotation_file,[RotateImage(90)])
img6 = d[1]['image']
pred = detector(img6)
n_img = plot_boxes(fp + "90right.png", img6, pred)
plt.subplot(2,4,6)
plt.imshow(n_img)
plt.title("90degree right")
d = Dataset(annotation_file,[RotateImage(-45)])
img7 = d[1]['image']
pred = detector(img7)
n_img = plot_boxes(fp + "45left.png", img7, pred)
plt.subplot(2,4,7)
plt.imshow(n_img)
plt.title("45degree left")
plt.show()
def experiment(annotation_file, detector, transforms, outputs):
'''
Function to perform the desired experiments
Arguments:
annotation_file: Path to annotation file
detector: The object detector
transforms: List of transformation classes
outputs: path of the output folder to store the images
'''
# Create the instance of the dataset.
data_obj = Dataset(annotation_file)
img_list = list()
fig = plt.figure()
titles = [
"Original Image", "Horizontally Flipped Image", "Blurred Image",
"2x Rescaled Image", "0.5x Rescaled Image",
"90 Degree Right Rotated Image", "45 Degree Left Rotated Image"
]
# Iterate over all data items.
for i, data in enumerate(data_obj):
print("Plotting boundary boxes for image " + str(i))
# Get the predictions from the detector.
image_array = data["image"]
pred_boxes, pred_class, pred_score = detector(image_array)
predictions = {
"pred_boxes": pred_boxes,
"pred_class": pred_class,
"pred_score": pred_score
}
image_array *= 255.0
image_array = np.array(image_array.transpose(1, 2, 0), dtype=np.uint8)
# Draw the boxes on the image and save them.
image = plot_boxes(image_array, predictions, outputs,
str(i) + "_pred.jpg")
if (i == 8):
img_list.append(fig.add_subplot(2, 4, 1))
img_list[-1].set_title(titles[0])
plt.imshow(image)
print()
# Do the required analysis experiments.
print("Performing analysis task (a)"
) # this is already done in the upper loop
for i, obj in enumerate(transforms):
d_obj = Dataset(annotation_file, [obj])
print("Performing analysis task (" + chr(98 + i) + ")")
image_array = d_obj[8]["image"]
pred_boxes, pred_class, pred_score = detector(image_array)
predictions = {
"pred_boxes": pred_boxes,
"pred_class": pred_class,
"pred_score": pred_score
}
image_array *= 255.0
image_array = np.array(image_array.transpose(1, 2, 0), dtype=np.uint8)
image = plot_boxes(image_array, predictions, outputs,
"8_" + chr(98 + i) + ".jpg")
img_list.append(fig.add_subplot(2, 4, i + 2))
img_list[-1].set_title(titles[i + 1])
plt.imshow(image)
print()
plt.show()