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import json
import os
import requests
from urllib.parse import urlparse
from requests.exceptions import HTTPError
import sys
from pathlib import Path
import textwrap
import ast
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
pylab.rcParams['figure.figsize'] = 20, 12
import cv2
import base64
import io
def download_images_from_jsonl(jsonl_path, output_folder):
with open(jsonl_path, 'r') as jsonl_file:
for line in jsonl_file:
json_obj = json.loads(line)
url = json_obj['url']
# download_image(url, output_folder)
vis_image(json_obj, output_folder)
def download_image(url, output_folder):
try:
response = requests.get(url)
response.raise_for_status()
except HTTPError as e:
print(f"Error while downloading {url}: {e}")
return
file_name = os.path.basename(urlparse(url).path)
output_path = os.path.join(output_folder, file_name)
with open(output_path, 'wb') as file:
file.write(response.content)
def imshow(img, file_name = "tmp.jpg", caption='test'):
# Create figure and axis objects
fig, ax = plt.subplots()
# Show image on axis
ax.imshow(img[:, :, [2, 1, 0]])
ax.set_axis_off()
# Set caption text
# Add caption below image
ax.text(0.5, -0.2, '\n'.join(textwrap.wrap(caption, 120)), ha='center', transform=ax.transAxes, fontsize=18)
plt.savefig(file_name, bbox_inches='tight')
plt.close()
def vis_image(json_obj, output_folder):
url = json_obj['url']
try:
response = requests.get(url)
response.raise_for_status()
file_name = os.path.basename(urlparse(url).path)
# output_path = os.path.join(output_folder, file_name)
file_key_name = json_obj['key'] + os.path.splitext(file_name)[1]
output_path = os.path.join(output_folder, file_key_name)
except Exception as e:
print(f"Error while downloading {url}: {e}")
return
with open(output_path, 'wb') as file:
file.write(response.content)
try:
pil_img = Image.open(output_path).convert("RGB")
except:
return
image = np.array(pil_img)[:, :, [2, 1, 0]]
image_h = pil_img.height
image_w = pil_img.width
caption = json_obj['caption']
def is_overlapping(rect1, rect2):
x1, y1, x2, y2 = rect1
x3, y3, x4, y4 = rect2
return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)
grounding_list = json_obj['ref_exps']
new_image = image.copy()
previous_locations = []
previous_bboxes = []
text_offset = 10
text_offset_original = 4
text_size = max(0.07 * min(image_h, image_w) / 100, 0.5)
text_line = int(max(1 * min(image_h, image_w) / 512, 1))
box_line = int(max(2 * min(image_h, image_w) / 512, 2))
text_height = text_offset # init
# pdb.set_trace()
for (phrase_s, phrase_e, x1_norm, y1_norm, x2_norm, y2_norm, score) in grounding_list:
phrase = caption[phrase_s:phrase_e]
x1, y1, x2, y2 = int(x1_norm * image_w), int(y1_norm * image_h), int(x2_norm * image_w), int(y2_norm * image_h)
print(f"Decode results: {phrase} - {[x1, y1, x2, y2]}")
# draw bbox
# random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
new_image = cv2.rectangle(new_image, (x1, y1), (x2, y2), color, box_line)
# add phrase name
# decide the text location first
for x_prev, y_prev in previous_locations:
if abs(x1 - x_prev) < abs(text_offset) and abs(y1 - y_prev) < abs(text_offset):
y1 += text_height
if y1 < 2 * text_offset:
y1 += text_offset + text_offset_original
# add text background
(text_width, text_height), _ = cv2.getTextSize(phrase, cv2.FONT_HERSHEY_SIMPLEX, text_size, text_line)
text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - text_height - text_offset_original, x1 + text_width, y1
for prev_bbox in previous_bboxes:
while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox):
text_bg_y1 += text_offset
text_bg_y2 += text_offset
y1 += text_offset
if text_bg_y2 >= image_h:
text_bg_y1 = max(0, image_h - text_height - text_offset_original)
text_bg_y2 = image_h
y1 = max(0, image_h - text_height - text_offset_original + text_offset)
break
alpha = 0.5
for i in range(text_bg_y1, text_bg_y2):
for j in range(text_bg_x1, text_bg_x2):
if i < image_h and j < image_w:
new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(color)).astype(np.uint8)
cv2.putText(
new_image, phrase, (x1, y1 - text_offset_original), cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
)
previous_locations.append((x1, y1))
previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2))
try:
file_key_name = json_obj['key'] + '_exp' + os.path.splitext(file_name)[1]
output_path = os.path.join(output_folder, file_key_name)
imshow(new_image, file_name= output_path, caption=caption)
except:
# Out of (supported formats: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff, webp)
return
if __name__ == '__main__':
# you need to download the jsonl before run this file
jsonl_path = '/tmp/grit_coyo.jsonl'
output_folder = './output/vis_grit'
if not os.path.exists(output_folder):
os.makedirs(output_folder)
download_images_from_jsonl(jsonl_path, output_folder)
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