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): print(image) img = rgb2gray(image) print(img) #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('demo.jpeg',line_image) # im=Image.fromarray(line_image) # im=im.convert("L") # im.save("demo.jpeg") # print("#### Image Saved #######") print(line_image) 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=["image"], outputs=gr.outputs.Textbox(),) iface.launch(enable_queue=True)