import numpy as np import PIL from PIL import Image, ImageDraw, ImageFont import gradio as gr import torch import easyocr import os from pathlib import Path import cv2 import pandas as pd from transformers import TrOCRProcessor, VisionEncoderDecoderModel #torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/BeautyIsTruthTruthisBeauty.JPG', 'BeautyIsTruthTruthisBeauty.JPG') #torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/PleaseRepeatLouder.jpg', 'PleaseRepeatLouder.jpg') #torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/ProhibitedInWhiteHouse.JPG', 'ProhibitedInWhiteHouse.JPG') torch.hub.download_url_to_file('https://raw.githubusercontent.com/AaronCWacker/Yggdrasil/master/images/20-Books.jpg','20-Books.jpg') torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/english.png', 'COVID.png') torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/chinese.jpg', 'chinese.jpg') torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/japanese.jpg', 'japanese.jpg') torch.hub.download_url_to_file('https://i.imgur.com/mwQFd7G.jpeg', 'Hindi.jpeg') def draw_boxes(image, bounds, color='yellow', width=2): draw = ImageDraw.Draw(image) for bound in bounds: p0, p1, p2, p3 = bound[0] draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width) return image def box_size(box): points = box[0] if len(points) == 4: x1, y1 = points[0] x2, y2 = points[2] return abs(x1 - x2) * abs(y1 - y2) else: return 0 def box_position(box): return (box[0][0][0] + box[0][2][0]) / 2, (box[0][0][1] + box[0][2][1]) / 2 def filter_temporal_profiles(temporal_profiles, period_index): filtered_profiles = [] for profile in temporal_profiles: filtered_profile = [] for t, text in profile: # Remove all non-digit characters from text filtered_text = ''.join(filter(str.isdigit, text)) # Insert period at the specified index filtered_text = filtered_text[:period_index] + "." + filtered_text[period_index:] try: filtered_value = float(filtered_text) except ValueError: continue filtered_profile.append((t, filtered_value)) filtered_profiles.append(filtered_profile) return filtered_profiles device = 'cuda' if torch.cuda.is_available() else 'cpu' processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-printed') model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-printed').to(device) def inference(video, lang, time_step, full_scan, number_filter, use_trocr, period_index): output = 'results.mp4' reader = easyocr.Reader(lang) bounds = [] vidcap = cv2.VideoCapture(video) success, frame = vidcap.read() count = 0 frame_rate = vidcap.get(cv2.CAP_PROP_FPS) output_frames = [] temporal_profiles = [] compress_mp4 = False # Get the positions of the largest boxes in the first frame bounds = reader.readtext(frame) for i in reversed(range(len(bounds))): box = bounds[i] # Remove box if it doesn't contain a number if not any(char.isdigit() for char in box[1]): bounds.pop(i) im = PIL.Image.fromarray(frame) im_with_boxes = draw_boxes(im, bounds) largest_boxes = sorted(bounds, key=lambda x: box_size(x), reverse=True) positions = [box_position(b) for b in largest_boxes] temporal_profiles = [[] for _ in range(len(largest_boxes))] # Match bboxes to position and store the text read by OCR while success: if count % (int(frame_rate * time_step)) == 0: if full_scan: bounds = reader.readtext(frame) for box in bounds: bbox_pos = box_position(box) for i, position in enumerate(positions): distance = np.linalg.norm(np.array(bbox_pos) - np.array(position)) if distance < 50: temporal_profiles[i].append((count / frame_rate, box[1])) break else: for i, box in enumerate(largest_boxes): x1, y1 = box[0][0] x2, y2 = box[0][2] box_width = x2 - x1 box_height = y2 - y1 ratio = 0.2 x1 = max(0, int(x1 - ratio * box_width)) x2 = min(frame.shape[1], int(x2 + ratio * box_width)) y1 = max(0, int(y1 - ratio * box_height)) y2 = min(frame.shape[0], int(y2 + ratio * box_height)) cropped_frame = frame[y1:y2, x1:x2] if use_trocr: pixel_values = processor(images=cropped_frame, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values.to(device)) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] temporal_profiles[i].append((count / frame_rate, generated_text)) else: text = reader.readtext(cropped_frame) if text: temporal_profiles[i].append((count / frame_rate, text[0][1])) im = PIL.Image.fromarray(frame) im_with_boxes = draw_boxes(im, bounds) output_frames.append(np.array(im_with_boxes)) success, frame = vidcap.read() count += 1 if number_filter: # Filter the temporal profiles by removing non-matching characters and converting to floats temporal_profiles = filter_temporal_profiles(temporal_profiles, int(period_index)) # Default resolutions of the frame are obtained. The default resolutions are system dependent. # We convert the resolutions from float to integer. width = int(vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = vidcap.get(cv2.CAP_PROP_FPS) frames_total = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT)) # Define the codec and create VideoWriter object. if compress_mp4: temp = f"{Path(output).stem}_temp{Path(output).suffix}" output_video = cv2.VideoWriter( temp, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height) ) else: output_video = cv2.VideoWriter(output, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) for frame in output_frames: output_video.write(frame) # Draw boxes with box indices in the first frame of the output video im = Image.fromarray(output_frames[0]) draw = ImageDraw.Draw(im) font_size = 30 font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf" for i, box in enumerate(largest_boxes): draw.text((box_position(box)), f"Box {i+1}", fill='red', font=ImageFont.truetype(font_path, font_size)) output_video.release() vidcap.release() if compress_mp4: # Compressing the video for smaller size and web compatibility. os.system( f"ffmpeg -y -i {temp} -c:v libx264 -b:v 5000k -minrate 1000k -maxrate 8000k -pass 1 -c:a aac -f mp4 /dev/null && ffmpeg -y -i {temp} -c:v libx264 -b:v 5000k -minrate 1000k -maxrate 8000k -pass 2 -c:a aac -movflags faststart {output}" ) os.system(f"rm -rf {temp} ffmpeg2pass-0.log ffmpeg2pass-0.log.mbtree") # Format temporal profiles as a DataFrame df_list = [] for i, profile in enumerate(temporal_profiles): for t, text in profile: df_list.append({"Box": f"Box {i+1}", "Time (s)": t, "Text": text}) df_list.append({"Box": f"", "Time (s)": "", "Text": ""}) df = pd.concat([pd.DataFrame(df_list)]) return output, im, df title = '🖼️Video to Multilingual OCR👁️Gradio' description = 'Multilingual OCR which works conveniently on all devices in multiple languages. Adjust time-step for inference and the scan mode according to your requirement. For `Full Screen Scan`, model scan the whole image if flag is ture, while scan only the box detected at the first video frame; this accelerate the inference while detecting the fixed box.' article = "

" examples = [ ['test.mp4',['en'],10,] ] css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}" choices = [ "ch_sim", "ch_tra", "de", "en", "es", "ja", "hi", "ru" ] gr.Interface( inference, [ gr.inputs.Video(label='Input Video'), gr.inputs.CheckboxGroup(choices, type="value", default=['en'], label='Language'), gr.inputs.Number(label='Time Step (in seconds)', default=1.0), gr.inputs.Checkbox(label='Full Screen Scan'), gr.inputs.Checkbox(label='Use TrOCR large (this is only available when Full Screen Scan is disable)'), gr.inputs.Checkbox(label='Number Filter (remove non-digit char and insert period)'), gr.inputs.Textbox(label="period position",default=1) ], [ gr.outputs.Video(label='Output Video'), gr.outputs.Image(label='Output Preview', type='numpy'), gr.outputs.Dataframe(headers=['Box', 'Time (s)', 'Text'], type='pandas'), ], title=title, description=description, article=article, examples=examples, css=css, enable_queue=True ).launch(debug=True)