forked from TensorLayer/tensorlayer3
124 lines
5.4 KiB
Plaintext
124 lines
5.4 KiB
Plaintext
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import os
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import numpy as np
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import tensorflow as tf
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from PIL import Image
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from tqdm import tqdm
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from frcnn import FRCNN
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from nets.frcnn_training import get_new_img_size
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from utils.anchors import get_anchors
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'''
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这里设置的门限值较低是因为计算map需要用到不同门限条件下的Recall和Precision值。
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所以只有保留的框足够多，计算的map才会更精确，详情可以了解map的原理。
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计算map时输出的Recall和Precision值指的是门限为0.5时的Recall和Precision值。
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此处获得的./input/detection-results/里面的txt的框的数量会比直接predict多一些，这是因为这里的门限低，
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目的是为了计算不同门限条件下的Recall和Precision值，从而实现map的计算。
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这里的self.iou指的是非极大抑制所用到的iou，具体的可以了解非极大抑制的原理，
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如果低分框与高分框的iou大于这里设定的self.iou，那么该低分框将会被剔除。
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可能有些同学知道有0.5和0.5:0.95的mAP，这里的self.iou=0.5不代表mAP0.5。
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如果想要设定mAP0.x，比如设定mAP0.75，可以去get_map.py设定MINOVERLAP。
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'''
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gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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class mAP_FRCNN(FRCNN):
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# ---------------------------------------------------#
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# 检测图片
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# ---------------------------------------------------#
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def detect_image(self, image_id, image):
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self.confidence = 0.01
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f = open("./input/detection-results/" + image_id + ".txt", "w")
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# -------------------------------------#
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# 转换成RGB图片，可以用于灰度图预测。
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# -------------------------------------#
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image = image.convert("RGB")
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image_shape = np.array(np.shape(image)[0:2])
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old_width, old_height = image_shape[1], image_shape[0]
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# ---------------------------------------------------------#
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# 给原图像进行resize，resize到短边为600的大小上
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# ---------------------------------------------------------#
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width, height = get_new_img_size(old_width, old_height)
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image = image.resize([width, height], Image.BICUBIC)
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photo = np.array(image, dtype=np.float64)
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# -----------------------------------------------------------#
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# 图片预处理，归一化。
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# -----------------------------------------------------------#
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photo = np.expand_dims(photo, 0)
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rpn_pred = self.model_rpn_get_pred(photo)
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rpn_pred = [x.numpy() for x in rpn_pred]
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# -----------------------------------------------------------#
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# 将建议框网络的预测结果进行解码
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# -----------------------------------------------------------#
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base_feature_width, base_feature_height = self.get_img_output_length(width, height)
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anchors = get_anchors([base_feature_width, base_feature_height], width, height)
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rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
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# -------------------------------------------------------------#
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# 在获得建议框和共享特征层后，将二者传入classifier中进行预测
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# -------------------------------------------------------------#
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base_layer = rpn_pred[2]
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proposal_box = np.array(rpn_results)[:, :, 1:]
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temp_ROIs = np.zeros_like(proposal_box)
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temp_ROIs[:, :, [0, 1, 2, 3]] = proposal_box[:, :, [1, 0, 3, 2]]
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classifier_pred = self.model_classifier_get_pred([base_layer, temp_ROIs])
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classifier_pred = [x.numpy() for x in classifier_pred]
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# -------------------------------------------------------------#
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# 利用classifier的预测结果对建议框进行解码，获得预测框
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# -------------------------------------------------------------#
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results = self.bbox_util.detection_out_classifier(classifier_pred, proposal_box, self.config, self.confidence)
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if len(results[0]) == 0:
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return
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results = np.array(results[0])
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boxes = results[:, :4]
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top_conf = results[:, 4]
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top_label_indices = results[:, 5]
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boxes[:, [0, 2]] = boxes[:, [0, 2]] * old_width
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boxes[:, [1, 3]] = boxes[:, [1, 3]] * old_height
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for i, c in enumerate(top_label_indices):
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predicted_class = self.class_names[int(c)]
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score = str(top_conf[i])
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left, top, right, bottom = boxes[i]
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f.write("%s %s %s %s %s %s\n" % (
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predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)), str(int(bottom))))
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f.close()
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return
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frcnn = mAP_FRCNN()
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image_ids = open('VOCdevkit/VOC2007/ImageSets/Main/test.txt').read().strip().split()
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if not os.path.exists("./input"):
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os.makedirs("./input")
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if not os.path.exists("./input/detection-results"):
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os.makedirs("./input/detection-results")
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if not os.path.exists("./input/images-optional"):
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os.makedirs("./input/images-optional")
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for image_id in tqdm(image_ids):
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image_path = "./VOCdevkit/VOC2007/JPEGImages/" + image_id + ".jpg"
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image = Image.open(image_path)
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# image.save("./input/images-optional/"+image_id+".jpg")
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frcnn.detect_image(image_id, image)
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print("Conversion completed!")
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