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Detect objects in local images #14

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69 changes: 69 additions & 0 deletions detect.py
View file Open in desktop
Original file line number Diff line number Diff line change
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import glob, os

import matplotlib.pyplot as plt
import matplotlib.pylab as pylab

import requests
from io import BytesIO
from PIL import Image
import numpy as np
pylab.rcParams['figure.figsize'] = 20, 12
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.engine.predictor_glip import GLIPDemo

def load(file):
pil_image = Image.open(file).convert("RGB")
# convert to BGR format
image = np.array(pil_image)[:, :, [2, 1, 0]]
return image

def load_url(url):
"""
Given an url of an image, downloads the image and
returns a PIL image
"""
response = requests.get(url)
pil_image = Image.open(BytesIO(response.content)).convert("RGB")
# convert to BGR format
image = np.array(pil_image)[:, :, [2, 1, 0]]
return image

def imshow(img, caption):
plt.imshow(img[:, :, [2, 1, 0]])
plt.axis("off")
plt.figtext(0.5, 0.09, caption, wrap=True, horizontalalignment='center', fontsize=20)

def imsave(img, path):
plt.imsave(path, img[:, :, [2, 1, 0]])

# config_file = "configs/pretrain/glip_Swin_T_O365_GoldG.yaml"
# weight_file = "MODEL/glip_tiny_model_o365_goldg_cc_sbu.pth"

# Use this command to evaluate the GLPT-L model
# ! wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/glip_large_model.pth -O MODEL/glip_large_model.pth
config_file = "configs/pretrain/glip_Swin_L.yaml"
weight_file = "MODEL/glip_large_model.pth"

# update the config options with the config file
# manual override some options
cfg.local_rank = 0
cfg.num_gpus = 1
cfg.merge_from_file(config_file)
cfg.merge_from_list(["MODEL.WEIGHT", weight_file])
cfg.merge_from_list(["MODEL.DEVICE", "cuda"])


glip_demo = GLIPDemo(
cfg,
min_image_size=800,
confidence_threshold=0.7,
show_mask_heatmaps=False
)

caption = 'person . bicycle . car . motorcycle . airplane . bus . train . truck . boat . traffic light . fire hydrant . stop sign . parking meter . bench . bird . cat . dog . horse . sheep . cow . elephant . bear . zebra . giraffe . backpack . umbrella . handbag . tie . suitcase . frisbee . skis . snowboard . sports ball . kite . baseball bat . baseball glove . skateboard . surfboard . tennis racket . bottle . wine glass . cup . fork . knife . spoon . bowl . banana . apple . sandwich . orange . broccoli . carrot . hot dog . pizza . donut . cake . chair . couch . potted plant . bed . dining table . toilet . tv . laptop . mouse . remote . keyboard . cell phone . microwave . oven . toaster . sink . refrigerator . book . clock . vase . scissors . teddy bear . hair drier . toothbrush .'
for files in [glob.glob(e) for e in ['INPUT/*.jpg', 'INPUT/*.png', 'INPUT/*.JPG', 'INPUT/*.PNG']]:
for file in files:
image = load(file)
result, _ = glip_demo.run_on_web_image(image, caption, 0.5)
#imshow(result, caption)
imsave(result, "RESULTS/"+file.split("/")[-1])

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