395 lines
16 KiB
Python
395 lines
16 KiB
Python
# 2022/10/26 by ausk
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"""
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An example that uses TensorRT's Python api to make yolop inferences.
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"""
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import ctypes
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import os
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import shutil
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import random
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import sys
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import time
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import cv2
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import numpy as np
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import pycuda.autoinit
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import pycuda.driver as cuda
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import tensorrt as trt
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CONF_THRESH = 0.5
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IOU_THRESHOLD = 0.4
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def get_img_path_batches(batch_size, img_dir):
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ret = []
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batch = []
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for root, dirs, files in os.walk(img_dir):
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for name in files:
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if len(batch) == batch_size:
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ret.append(batch)
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batch = []
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batch.append(os.path.join(root, name))
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if len(batch) > 0:
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ret.append(batch)
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return ret
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def plot_one_box(x, img, color=None, label=None, line_thickness=None):
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"""
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description: Plots one bounding box on image img,
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this function comes from YoLov5 project.
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"""
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tl = ( line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1) # line/font thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
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if label:
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tf = max(tl - 1, 1) # font thickness
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
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cv2.putText( img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
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class YolopTRT(object):
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"""
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description: Warps TensorRT ops, preprocess and postprocess ops.
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"""
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def __init__(self, engine_file_path):
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# Create a Context on this device,
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self.ctx = cuda.Device(0).make_context()
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stream = cuda.Stream()
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TRT_LOGGER = trt.Logger(trt.Logger.INFO)
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runtime = trt.Runtime(TRT_LOGGER)
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# Deserialize the engine from file
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with open(engine_file_path, "rb") as f:
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engine = runtime.deserialize_cuda_engine(f.read())
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context = engine.create_execution_context()
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host_inputs = []
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cuda_inputs = []
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host_outputs = []
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cuda_outputs = []
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bindings = []
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for binding in engine:
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print('bingding: ', binding, engine.get_binding_shape(binding))
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size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
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dtype = trt.nptype(engine.get_binding_dtype(binding))
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# Allocate host and device buffers
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host_mem = cuda.pagelocked_empty(size, dtype)
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cuda_mem = cuda.mem_alloc(host_mem.nbytes)
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# Append the device buffer to device bindings.
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bindings.append(int(cuda_mem))
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# Append to the appropriate list.
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if engine.binding_is_input(binding):
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self.input_w = engine.get_binding_shape(binding)[-1]
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self.input_h = engine.get_binding_shape(binding)[-2]
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host_inputs.append(host_mem)
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cuda_inputs.append(cuda_mem)
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else:
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host_outputs.append(host_mem)
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cuda_outputs.append(cuda_mem)
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self.input_h = 384
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self.input_w = 640
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self.img_h = 360
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self.img_w = 640
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# Store
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self.stream = stream
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self.context = context
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self.engine = engine
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self.host_inputs = host_inputs
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self.cuda_inputs = cuda_inputs
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self.host_outputs = host_outputs
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self.cuda_outputs = cuda_outputs
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self.bindings = bindings
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self.batch_size = engine.max_batch_size
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def infer(self, raw_image_generator):
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# Make self the active context, pushing it on top of the context stack.
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self.ctx.push()
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# Restore
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stream = self.stream
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context = self.context
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engine = self.engine
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host_inputs = self.host_inputs
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cuda_inputs = self.cuda_inputs
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host_outputs = self.host_outputs
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cuda_outputs = self.cuda_outputs
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bindings = self.bindings
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# Do image preprocess
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batch_image_raw = []
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batch_origin_h = []
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batch_origin_w = []
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batch_input_image = np.empty(shape=[self.batch_size, 3, self.input_h, self.input_w])
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for i, image_raw in enumerate(raw_image_generator):
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input_image, image_raw, origin_h, origin_w = self.preprocess_image(image_raw)
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batch_image_raw.append(image_raw)
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batch_origin_h.append(origin_h)
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batch_origin_w.append(origin_w)
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np.copyto(batch_input_image[i], input_image)
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batch_input_image = np.ascontiguousarray(batch_input_image)
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# Copy input image to host buffer
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np.copyto(host_inputs[0], batch_input_image.ravel())
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start = time.time()
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# Transfer input data to the GPU.
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cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
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# Run inference.
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context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle)
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# Transfer predictions back from the GPU.
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for i in range(len(host_outputs)):
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cuda.memcpy_dtoh_async(host_outputs[i], cuda_outputs[i], stream)
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# Synchronize the stream
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stream.synchronize()
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end = time.time()
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# Remove any context from the top of the context stack, deactivating it.
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self.ctx.pop()
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# Here we use the first row of output in that batch_size = 1
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detout = host_outputs[0]
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segout = host_outputs[1].reshape( (self.batch_size, self.img_h,self.img_w))
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laneout = host_outputs[2].reshape( (self.batch_size, self.img_h,self.img_w))
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# Do postprocess
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for i in range(self.batch_size):
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result_boxes, result_scores, result_classid = self.post_process(
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detout[i * 6001: (i + 1) * 6001], batch_origin_h[i], batch_origin_w[i]
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)
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# Draw rectangles and labels on the original image
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img = batch_image_raw[i]
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nh = img.shape[0]
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nw = img.shape[1]
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for j in range(len(result_boxes)):
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box = result_boxes[j]
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label="{}:{:.2f}".format( categories[int(result_classid[j])], result_scores[j])
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plot_one_box( box, img, label=label)
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seg = cv2.resize(segout[i], (nw, nh), interpolation=cv2.INTER_NEAREST)
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lane = cv2.resize(laneout[i], (nw, nh), interpolation=cv2.INTER_NEAREST)
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color_area = np.zeros_like(img)
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color_area[seg==1] = (0,255,0)
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color_area[lane==1] = (0,0,255)
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color_mask = np.mean(color_area, 2)
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img[color_mask != 0] = img[color_mask != 0] * 0.5 + color_area[color_mask != 0] * 0.5
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img = img.astype(np.uint8)
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return batch_image_raw, end - start
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def destroy(self):
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# Remove any context from the top of the context stack, deactivating it.
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self.ctx.pop()
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def get_raw_image(self, image_path_batch):
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for img_path in image_path_batch:
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yield cv2.imread(img_path)
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def get_raw_image_zeros(self, image_path_batch=None):
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for _ in range(self.batch_size):
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yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8)
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def preprocess_image(self, raw_bgr_image):
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image_raw = raw_bgr_image
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h, w, c = image_raw.shape
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image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)
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# Calculate widht and height and paddings
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r_w = self.input_w / w
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r_h = self.input_h / h
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if r_h > r_w:
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tw = self.input_w
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th = int(r_w * h)
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tx1 = tx2 = 0
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ty1 = int((self.input_h - th) / 2)
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ty2 = self.input_h - th - ty1
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else:
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tw = int(r_h * w)
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th = self.input_h
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tx1 = int((self.input_w - tw) / 2)
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tx2 = self.input_w - tw - tx1
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ty1 = ty2 = 0
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# Resize the image with long side while maintaining ratio
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image = cv2.resize(image, (tw, th))
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# Pad the short side with (128,128,128)
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image = cv2.copyMakeBorder(
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image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, None, (114, 114, 114)
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)
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image = image.astype(np.float32)
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# Normalize to [0,1]
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image /= 255.0
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image = (image - (0.485, 0.456, 0.406)) /(0.229, 0.224, 0.225)
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# HWC to CHW format:
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image = np.transpose(image, [2, 0, 1])
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# CHW to NCHW format
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image = np.expand_dims(image, axis=0)
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# Convert the image to row-major order, also known as "C order":
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image = np.ascontiguousarray(image)
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return image, image_raw, h, w
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def xywh2xyxy(self, origin_h, origin_w, x):
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"""
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description: Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
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param:
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origin_h: height of original image
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origin_w: width of original image
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x: A boxes numpy, each row is a box [center_x, center_y, w, h]
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return:
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y: A boxes numpy, each row is a box [x1, y1, x2, y2]
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"""
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y = np.zeros_like(x)
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r_w = self.input_w / origin_w
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r_h = self.input_h / origin_h
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if r_h > r_w:
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y[:, 0] = x[:, 0] - x[:, 2] / 2
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y[:, 2] = x[:, 0] + x[:, 2] / 2
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y[:, 1] = x[:, 1] - x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2
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y[:, 3] = x[:, 1] + x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2
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y /= r_w
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else:
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y[:, 0] = x[:, 0] - x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2
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y[:, 2] = x[:, 0] + x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2
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y[:, 1] = x[:, 1] - x[:, 3] / 2
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y[:, 3] = x[:, 1] + x[:, 3] / 2
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y /= r_h
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return y
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def post_process(self, output, origin_h, origin_w):
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# Get the num of boxes detected
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num = int(output[0])
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# Reshape to a two dimentional ndarray
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pred = np.reshape(output[1:], (-1, 6))[:num, :]
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# Do nms
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boxes = self.non_max_suppression(pred, origin_h, origin_w, conf_thres=CONF_THRESH, nms_thres=IOU_THRESHOLD)
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result_boxes = boxes[:, :4] if len(boxes) else np.array([])
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result_scores = boxes[:, 4] if len(boxes) else np.array([])
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result_classid = boxes[:, 5] if len(boxes) else np.array([])
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return result_boxes, result_scores, result_classid
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def bbox_iou(self, box1, box2, x1y1x2y2=True):
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"""
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description: compute the IoU of two bounding boxes
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param:
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box1: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))
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box2: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))
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x1y1x2y2: select the coordinate format
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return:
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iou: computed iou
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"""
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if not x1y1x2y2:
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# Transform from center and width to exact coordinates
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b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
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b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
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b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
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b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
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else:
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# Get the coordinates of bounding boxes
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b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
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b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
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# Get the coordinates of the intersection rectangle
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inter_rect_x1 = np.maximum(b1_x1, b2_x1)
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inter_rect_y1 = np.maximum(b1_y1, b2_y1)
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inter_rect_x2 = np.minimum(b1_x2, b2_x2)
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inter_rect_y2 = np.minimum(b1_y2, b2_y2)
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# Intersection area
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inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, None) * \
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np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None)
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# Union Area
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b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
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b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
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iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)
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return iou
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def non_max_suppression(self, prediction, origin_h, origin_w, conf_thres=0.5, nms_thres=0.4):
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"""
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description: Removes detections with lower object confidence score than 'conf_thres' and performs
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Non-Maximum Suppression to further filter detections.
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param:
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prediction: detections, (x1, y1, x2, y2, conf, cls_id)
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origin_h: original image height
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origin_w: original image width
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conf_thres: a confidence threshold to filter detections
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nms_thres: a iou threshold to filter detections
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return:
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boxes: output after nms with the shape (x1, y1, x2, y2, conf, cls_id)
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"""
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# Get the boxes that score > CONF_THRESH
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boxes = prediction[prediction[:, 4] >= conf_thres]
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# Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2]
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boxes[:, :4] = self.xywh2xyxy(origin_h, origin_w, boxes[:, :4])
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# clip the coordinates
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boxes[:, 0] = np.clip(boxes[:, 0], 0, origin_w -1)
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boxes[:, 2] = np.clip(boxes[:, 2], 0, origin_w -1)
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boxes[:, 1] = np.clip(boxes[:, 1], 0, origin_h -1)
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boxes[:, 3] = np.clip(boxes[:, 3], 0, origin_h -1)
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# Object confidence
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confs = boxes[:, 4]
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# Sort by the confs
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boxes = boxes[np.argsort(-confs)]
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# Perform non-maximum suppression
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keep_boxes = []
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while boxes.shape[0]:
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large_overlap = self.bbox_iou(np.expand_dims(boxes[0, :4], 0), boxes[:, :4]) > nms_thres
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label_match = boxes[0, -1] == boxes[:, -1]
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# Indices of boxes with lower confidence scores, large IOUs and matching labels
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invalid = large_overlap & label_match
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keep_boxes += [boxes[0]]
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boxes = boxes[~invalid]
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boxes = np.stack(keep_boxes, 0) if len(keep_boxes) else np.array([])
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return boxes
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if __name__ == "__main__":
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# load custom plugin and engine
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PLUGIN_LIBRARY = "build/libmyplugins.so"
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engine_file_path = "build/yolop.trt"
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print("usage: xxx <engine file> <plugin file> <image dir>")
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print("[WARN] preaprea you image_dir, such as: samples, or /home/user/jetson/tmp/YOLOP/inference/images")
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IMAGE_DIR = "/home/user/jetson/tmp/YOLOP/inference/images"
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if len(sys.argv) > 1:
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engine_file_path = sys.argv[1]
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if len(sys.argv) > 2:
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PLUGIN_LIBRARY = sys.argv[2]
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if len(sys.argv) > 3:
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IMAGE_DIR = sys.argv[3]
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ctypes.CDLL(PLUGIN_LIBRARY)
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categories = ["car"]
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if os.path.exists('output/'):
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shutil.rmtree('output/')
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os.makedirs('output/')
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# a YolopTRT instance
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yolop_wrapper = YolopTRT(engine_file_path)
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try:
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print('batch size is', yolop_wrapper.batch_size)
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image_dir = IMAGE_DIR
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image_path_batches = get_img_path_batches(yolop_wrapper.batch_size, image_dir)
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for i in range(1):
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batch_image_raw, use_time = yolop_wrapper.infer(yolop_wrapper.get_raw_image_zeros())
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print('warm_up->{}, time->{:.2f}ms'.format(batch_image_raw[0].shape, use_time * 1000))
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for batch in image_path_batches:
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batch_image_raw, use_time = yolop_wrapper.infer(yolop_wrapper.get_raw_image(batch))
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for i, img_path in enumerate(batch):
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parent, filename = os.path.split(img_path)
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save_name = os.path.join('output', filename)
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# Save image
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cv2.imwrite(save_name, batch_image_raw[i])
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print('input->{}, time->{:.2f}ms, saving into output/'.format(batch, use_time * 1000))
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finally:
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# destroy the instance
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yolop_wrapper.destroy()
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print("done!") |