vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b
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import os
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import glob
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import argparse
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import cv2 as cv
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import numpy as np
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import sys
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from common import *
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def help():
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print(
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'''
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Firstly, download required models using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.\n"\n
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To run:
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python classification.py model_name --input=path/to/your/input/image/or/video (don't give --input flag if want to use device camera)
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Sample command:
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python classification.py googlenet --input=path/to/image
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Model path can also be specified using --model argument
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'''
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)
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def get_args_parser(func_args):
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backends = ("default", "openvino", "opencv", "vkcom", "cuda")
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targets = ("cpu", "opencl", "opencl_fp16", "ncs2_vpu", "hddl_vpu", "vulkan", "cuda", "cuda_fp16")
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
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help='An optional path to file with preprocessing parameters.')
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parser.add_argument('--input',
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help='Path to input image or video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--crop', type=bool, default=False,
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help='Center crop the image.')
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parser.add_argument('--backend', default="default", type=str, choices=backends,
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help="Choose one of computation backends: "
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"default: automatically (by default), "
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"openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"opencv: OpenCV implementation, "
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"vkcom: VKCOM, "
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"cuda: CUDA, "
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"webnn: WebNN")
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parser.add_argument('--target', default="cpu", type=str, choices=targets,
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help="Choose one of target computation devices: "
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"cpu: CPU target (by default), "
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"opencl: OpenCL, "
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"opencl_fp16: OpenCL fp16 (half-float precision), "
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"ncs2_vpu: NCS2 VPU, "
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"hddl_vpu: HDDL VPU, "
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"vulkan: Vulkan, "
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"cuda: CUDA, "
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"cuda_fp16: CUDA fp16 (half-float preprocess)")
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args, _ = parser.parse_known_args()
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add_preproc_args(args.zoo, parser, 'classification')
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parser = argparse.ArgumentParser(parents=[parser],
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description='Use this script to run classification deep learning networks using OpenCV.',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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return parser.parse_args(func_args)
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def load_images(directory):
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# List all common image file extensions, feel free to add more if needed
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extensions = ['jpg', 'jpeg', 'png', 'bmp', 'tif', 'tiff']
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files = []
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for extension in extensions:
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files.extend(glob.glob(os.path.join(directory, f'*.{extension}')))
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return files
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def main(func_args=None):
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args = get_args_parser(func_args)
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if args.alias is None or hasattr(args, 'help'):
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help()
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exit(1)
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cv.utils.logging.setLogLevel(cv.utils.logging.LOG_LEVEL_INFO)
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args.model = findModel(args.model, args.sha1)
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args.labels = findFile(args.labels)
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# Load names of classes
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labels = None
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if args.labels:
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with open(args.labels, 'rt') as f:
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labels = f.read().rstrip('\n').split('\n')
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# Load a network
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engine = cv.dnn.ENGINE_AUTO
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if args.backend != "default" or args.target != "cpu":
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engine = cv.dnn.ENGINE_CLASSIC
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net = cv.dnn.readNetFromONNX(args.model, engine)
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net.setPreferableBackend(get_backend_id(args.backend))
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net.setPreferableTarget(get_target_id(args.target))
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if hasattr(cv.dnn, 'DNN_PROFILE_SUMMARY'):
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net.setProfilingMode(cv.dnn.DNN_PROFILE_SUMMARY)
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winName = 'Deep learning image classification in OpenCV'
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cv.namedWindow(winName, cv.WINDOW_NORMAL)
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isdir = False
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if args.input:
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input_path = args.input
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if os.path.isdir(input_path):
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isdir = True
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image_files = load_images(input_path)
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if not image_files:
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print("No images found in the directory.")
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exit(-1)
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current_image_index = 0
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else:
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input_path = findFile(input_path)
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cap = cv.VideoCapture(input_path)
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if not cap.isOpened():
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print("Failed to open the input video")
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exit(-1)
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else:
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cap = cv.VideoCapture(0)
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while cv.waitKey(1) < 0:
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if isdir:
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if current_image_index >= len(image_files):
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break
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frame = cv.imread(image_files[current_image_index])
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current_image_index += 1
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else:
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hasFrame, frame = cap.read()
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if not hasFrame:
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cv.waitKey()
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break
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# Create a 4D blob from a frame.
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inpWidth = args.width if args.width else frame.shape[1]
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inpHeight = args.height if args.height else frame.shape[0]
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blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=args.crop)
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if args.std:
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blob[0] /= np.asarray(args.std, dtype=np.float32).reshape(3, 1, 1)
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# Run a model
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net.setInput(blob)
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t0 = cv.getTickCount()
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out = net.forward()
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t = (cv.getTickCount() - t0) / cv.getTickFrequency()
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net.printPerfProfile()
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(h, w, _) = frame.shape
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roi_rows = min(300, h)
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roi_cols = min(1000, w)
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frame[:roi_rows,:roi_cols,:] >>= 1
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# Put efficiency information.
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label = 'Inference time: %.1f ms' % (t * 1000.0)
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cv.putText(frame, label, (15, 30), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0))
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# Print predicted classes.
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out = out.flatten()
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K = 5
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topKidx = np.argpartition(out, -K)[-K:]
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for i in range(K):
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classId = topKidx[i]
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confidence = out[classId]
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label = '%s: %.2f' % (labels[classId] if labels else 'Class #%d' % classId, confidence)
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cv.putText(frame, label, (15, 90 + i*30), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0))
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cv.imshow(winName, frame)
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key = cv.waitKey(1000 if isdir else 100)
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if key >= 0:
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key &= 255
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if key == ord(' '):
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key = cv.waitKey() & 255
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if key == ord('q') or key == 27: # Wait for 1 second on each image, press 'q' to exit
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sys.exit(0)
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cv.waitKey()
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if __name__ == "__main__":
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main()
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