122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
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# Tencent is pleased to support the open source community by making ncnn available.
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#
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# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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#
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# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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# in compliance with the License. You may obtain a copy of the License at
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#
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# https://opensource.org/licenses/BSD-3-Clause
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#
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# Unless required by applicable law or agreed to in writing, software distributed
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# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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# CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations under the License.
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import sys
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import cv2
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import numpy as np
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import ncnn
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from ncnn.model_zoo import get_model
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def draw_detection_objects_seg(image, class_names, objects, mat_map):
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color = [128, 255, 128, 244, 35, 232]
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color_count = len(color)
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for obj in objects:
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print(
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"%d = %.5f at %.2f %.2f %.2f x %.2f\n"
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% (obj.label, obj.prob, obj.rect.x, obj.rect.y, obj.rect.w, obj.rect.h)
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)
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cv2.rectangle(
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image,
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(int(obj.rect.x), int(obj.rect.y)),
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(int(obj.rect.x + obj.rect.w), int(obj.rect.y + obj.rect.h)),
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(255, 0, 0),
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)
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text = "%s %.1f%%" % (class_names[int(obj.label)], obj.prob * 100)
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label_size, baseLine = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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x = obj.rect.x
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y = obj.rect.y - label_size[1] - baseLine
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if y < 0:
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y = 0
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if x + label_size[0] > image.shape[1]:
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x = image.shape[1] - label_size[0]
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cv2.rectangle(
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image,
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(int(x), int(y)),
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(int(x + label_size[0]), int(y + label_size[1] + baseLine)),
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(255, 255, 255),
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-1,
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)
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cv2.putText(
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image,
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text,
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(int(x), int(y + label_size[1])),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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(0, 0, 0),
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)
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width = mat_map.w
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height = mat_map.h
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size = mat_map.c
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img_index2 = 0
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threshold = 0.45
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ptr2 = np.array(mat_map)
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for i in range(height):
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ptr1 = image[i].flatten()
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img_index1 = 0
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for j in range(width):
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maxima = threshold
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index = -1
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for c in range(size):
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# const float* ptr3 = ptr2 + c*width*height
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ptr3 = ptr2[c].flatten()
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if ptr3[img_index2] > maxima:
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maxima = ptr3[img_index2]
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index = c
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if index > -1:
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color_index = (index) * 3
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if color_index < color_count:
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b = color[color_index]
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g = color[color_index + 1]
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r = color[color_index + 2]
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ptr1[img_index1] = b / 2 + ptr1[img_index1] / 2
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ptr1[img_index1 + 1] = g / 2 + ptr1[img_index1 + 1] / 2
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ptr1[img_index1 + 2] = r / 2 + ptr1[img_index1 + 2] / 2
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img_index1 += 3
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img_index2 += 1
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image[i] = ptr1.reshape(image[i].shape)
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cv2.imshow("image", image)
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cv2.waitKey(0)
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if __name__ == "__main__":
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if len(sys.argv) != 2:
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print("Usage: %s [imagepath]\n" % (sys.argv[0]))
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sys.exit(0)
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imagepath = sys.argv[1]
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m = cv2.imread(imagepath)
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if m is None:
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print("cv2.imread %s failed\n" % (imagepath))
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sys.exit(0)
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net = get_model("peleenet_ssd", num_threads=4, use_gpu=True)
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objects, seg_out = net(m)
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draw_detection_objects_seg(m, net.class_names, objects, seg_out)
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