Detect objects in local images

detect.py

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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])