app.py

# 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='filepath', label='Ideal Answer'),gr.inputs.Image(type='filepath', label='Ideal Answer Diagram'),gr.inputs.Image(type='filepath', label='Submitted Answer'),gr.inputs.Image(type='filepath', label='Submitted Answer Diagram')], 
                     outputs=gr.outputs.Textbox(),)

iface.launch(enable_queue=True)