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# import gradio as gr
# def greet(name):
# return "Hello " + name + "!!"
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests
import warnings
from skimage.io import imread
from skimage.color import rgb2gray
import matplotlib.pyplot as plt
from skimage.filters import sobel
import numpy as np
from heapq import *
import gradio as gr
from skimage.filters import threshold_otsu
from skimage.util import invert
import cv2,imageio
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
plt.switch_backend('Agg')
def horizontal_projections(sobel_image):
return np.sum(sobel_image, axis=1)
def find_peak_regions(hpp, divider=4):
threshold = (np.max(hpp)-np.min(hpp))/divider
peaks = []
for i, hppv in enumerate(hpp):
if hppv < threshold:
peaks.append([i, hppv])
return peaks
def heuristic(a, b):
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
def get_hpp_walking_regions(peaks_index):
hpp_clusters = []
cluster = []
for index, value in enumerate(peaks_index):
cluster.append(value)
if index < len(peaks_index)-1 and peaks_index[index+1] - value > 1:
hpp_clusters.append(cluster)
cluster = []
#get the last cluster
if index == len(peaks_index)-1:
hpp_clusters.append(cluster)
cluster = []
return hpp_clusters
def astar(array, start, goal):
neighbors = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(1,-1),(-1,1),(-1,-1)]
close_set = set()
came_from = {}
gscore = {start:0}
fscore = {start:heuristic(start, goal)}
oheap = []
heappush(oheap, (fscore[start], start))
while oheap:
current = heappop(oheap)[1]
if current == goal:
data = []
while current in came_from:
data.append(current)
current = came_from[current]
return data
close_set.add(current)
for i, j in neighbors:
neighbor = current[0] + i, current[1] + j
tentative_g_score = gscore[current] + heuristic(current, neighbor)
if 0 <= neighbor[0] < array.shape[0]:
if 0 <= neighbor[1] < array.shape[1]:
if array[neighbor[0]][neighbor[1]] == 1:
continue
else:
# array bound y walls
continue
else:
# array bound x walls
continue
if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, 0):
continue
if tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1]for i in oheap]:
came_from[neighbor] = current
gscore[neighbor] = tentative_g_score
fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)
heappush(oheap, (fscore[neighbor], neighbor))
return []
def get_binary(img):
mean = np.mean(img)
if mean == 0.0 or mean == 1.0:
return img
thresh = threshold_otsu(img)
binary = img <= thresh
binary = binary*1
return binary
def path_exists(window_image):
#very basic check first then proceed to A* check
if 0 in horizontal_projections(window_image):
return True
padded_window = np.zeros((window_image.shape[0],1))
world_map = np.hstack((padded_window, np.hstack((window_image,padded_window)) ) )
path = np.array(astar(world_map, (int(world_map.shape[0]/2), 0), (int(world_map.shape[0]/2), world_map.shape[1])))
if len(path) > 0:
return True
return False
def get_road_block_regions(nmap):
road_blocks = []
needtobreak = False
for col in range(nmap.shape[1]):
start = col
end = col+20
if end > nmap.shape[1]-1:
end = nmap.shape[1]-1
needtobreak = True
if path_exists(nmap[:, start:end]) == False:
road_blocks.append(col)
if needtobreak == True:
break
return road_blocks
def group_the_road_blocks(road_blocks):
#group the road blocks
road_blocks_cluster_groups = []
road_blocks_cluster = []
size = len(road_blocks)
for index, value in enumerate(road_blocks):
road_blocks_cluster.append(value)
if index < size-1 and (road_blocks[index+1] - road_blocks[index]) > 1:
road_blocks_cluster_groups.append([road_blocks_cluster[0], road_blocks_cluster[len(road_blocks_cluster)-1]])
road_blocks_cluster = []
if index == size-1 and len(road_blocks_cluster) > 0:
road_blocks_cluster_groups.append([road_blocks_cluster[0], road_blocks_cluster[len(road_blocks_cluster)-1]])
road_blocks_cluster = []
return road_blocks_cluster_groups
def extract_line_from_image(image, lower_line, upper_line):
lower_boundary = np.min(lower_line[:, 0])
upper_boundary = np.min(upper_line[:, 0])
img_copy = np.copy(image)
r, c = img_copy.shape
for index in range(c-1):
img_copy[0:lower_line[index, 0], index] = 0
img_copy[upper_line[index, 0]:r, index] = 0
return img_copy[lower_boundary:upper_boundary, :]
def extract(image):
img = rgb2gray(image)
#img = rgb2gray(imread("Penwritten_2048x.jpeg"))
#img = rgb2gray(imread("test.jpg"))
#img = rgb2gray(imread(""))
sobel_image = sobel(img)
hpp = horizontal_projections(sobel_image)
warnings.filterwarnings("ignore")
#find the midway where we can make a threshold and extract the peaks regions
#divider parameter value is used to threshold the peak values from non peak values.
peaks = find_peak_regions(hpp)
peaks_index = np.array(peaks)[:,0].astype(int)
#print(peaks_index.shape)
segmented_img = np.copy(img)
r= segmented_img.shape
for ri in range(r[0]):
if ri in peaks_index:
segmented_img[ri, :] = 0
#group the peaks into walking windows
hpp_clusters = get_hpp_walking_regions(peaks_index)
#a star path planning algorithm
#Scan the paths to see if there are any blockers.
binary_image = get_binary(img)
for cluster_of_interest in hpp_clusters:
nmap = binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:]
road_blocks = get_road_block_regions(nmap)
road_blocks_cluster_groups = group_the_road_blocks(road_blocks)
#create the doorways
for index, road_blocks in enumerate(road_blocks_cluster_groups):
window_image = nmap[:, road_blocks[0]: road_blocks[1]+10]
binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:][:, road_blocks[0]: road_blocks[1]+10][int(window_image.shape[0]/2),:] *= 0
#now that everything is cleaner, its time to segment all the lines using the A* algorithm
line_segments = []
#print(len(hpp_clusters))
#print(hpp_clusters)
for i, cluster_of_interest in enumerate(hpp_clusters):
nmap = binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:]
path = np.array(astar(nmap, (int(nmap.shape[0]/2), 0), (int(nmap.shape[0]/2),nmap.shape[1]-1)))
#print(path.shape)
if path.shape[0]!=0:
#break
offset_from_top = cluster_of_interest[0]
#print(offset_from_top)
path[:,0] += offset_from_top
#print(path)
line_segments.append(path)
#print(i)
cluster_of_interest = hpp_clusters[1]
offset_from_top = cluster_of_interest[0]
nmap = binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:]
#plt.figure(figsize=(20,20))
#plt.imshow(invert(nmap), cmap="gray")
path = np.array(astar(nmap, (int(nmap.shape[0]/2), 0), (int(nmap.shape[0]/2),nmap.shape[1]-1)))
#plt.plot(path[:,1], path[:,0])
offset_from_top = cluster_of_interest[0]
## add an extra line to the line segments array which represents the last bottom row on the image
last_bottom_row = np.flip(np.column_stack(((np.ones((img.shape[1],))*img.shape[0]), np.arange(img.shape[1]))).astype(int), axis=0)
line_segments.append(last_bottom_row)
line_images = []
line_count = len(line_segments)
fig, ax = plt.subplots(figsize=(10,10), nrows=line_count-1)
output = []
for line_index in range(line_count-1):
line_image = extract_line_from_image(img, line_segments[line_index], line_segments[line_index+1])
line_images.append(line_image)
#print(line_image)
#cv2.imwrite('/Users/vatsalya/Desktop/demo.jpeg',line_image)
#im=Image.fromarray(line_image)
#im=im.convert("L")
#im.save("/Users/vatsalya/Desktop/demo.jpeg")
#print("#### Image Saved #######")
imageio.imwrite('demo.jpeg',line_image)
image = Image.open("demo.jpeg").convert("RGB")
#print("Started Processing")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
output.append(generated_text)
#ax[line_index].imshow(line_image, cmap="gray")
result=""
for o in output:
result=result+o
result=result+" "
return result
iface = gr.Interface(fn=extract,
inputs=[gr.inputs.Image(type='file', label='Ideal Answer'),gr.inputs.Image(type='file', label='Ideal Answer Diagram'),gr.inputs.Image(type='file', label='Submitted Answer'),gr.inputs.Image(type='file', label='Submitted Answer Diagram')],
outputs=gr.outputs.Textbox(),)
iface.launch(enable_queue=True) |