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301 lines
14 KiB
301 lines
14 KiB
import math, os, time, sys, bpy, argparse |
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from tqdm import tqdm |
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from contextlib import contextmanager |
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from mathutils import Vector |
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from PIL import Image |
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import numpy as np |
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@contextmanager |
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def stdout_redirected(to=os.devnull): |
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''' |
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import os |
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with stdout_redirected(to=filename): |
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print("from Python") |
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os.system("echo non-Python applications are also supported") |
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''' |
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fd = sys.stdout.fileno() |
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##### assert that Python and C stdio write using the same file descriptor |
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####assert libc.fileno(ctypes.c_void_p.in_dll(libc, "stdout")) == fd == 1 |
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def _redirect_stdout(to): |
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sys.stdout.close() # + implicit flush() |
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os.dup2(to.fileno(), fd) # fd writes to 'to' file |
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sys.stdout = os.fdopen(fd, 'w') # Python writes to fd |
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with os.fdopen(os.dup(fd), 'w') as old_stdout: |
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with open(to, 'w') as file: |
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_redirect_stdout(to=file) |
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try: |
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yield # allow code to be run with the redirected stdout |
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finally: |
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_redirect_stdout(to=old_stdout) # restore stdout. |
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# buffering and flags such as |
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# CLOEXEC may be different |
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def check_image_rander(png_image_dir): |
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"""检查图片生成是否正常""" |
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image_list = os.listdir(png_image_dir) |
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if len(image_list)>0: |
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image_path = os.path.join(png_image_dir,image_list[0]) |
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img = Image.open(image_path) |
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# 将图像转换为 RGB 模式 |
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img = img.convert("RGB") |
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# 将图像数据转换为 numpy 数组 |
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img_data = np.array(img) |
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# 设定颜色分段数 |
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color_bins = 24 |
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# 将颜色分成 24 种，计算颜色区间 |
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# 对每个 RGB 值进行分段 |
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def get_color_bin(value, bins): |
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return (value * bins) // 256 |
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# 用于存储所有颜色的集合 |
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color_set = set() |
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# 遍历图像每一个像素 |
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for row in img_data: |
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for pixel in row: |
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# 将 RGB 每个通道的值分段 |
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r, g, b = pixel |
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r_bin = get_color_bin(r, color_bins) |
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g_bin = get_color_bin(g, color_bins) |
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b_bin = get_color_bin(b, color_bins) |
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# 将 RGB 分段后的颜色组合成一个元组 |
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color_set.add((r_bin, g_bin, b_bin)) |
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# 输出不同的颜色数量 |
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print(f"该图像中有 {len(color_set)} 种颜色。") |
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if len(color_set)>20: |
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return True |
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else: |
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return False |
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else: |
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return False |
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def rander_image_and_check(args): |
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"""渲染图片""" |
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# 列出obj列表 |
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pid = args.pid |
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pid_file_dir = os.path.join(args.input,pid) |
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if not os.path.exists(pid_file_dir): |
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return |
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obj_file_list = [aa for aa in os.listdir(pid_file_dir) if aa.endswith(".obj")] |
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if len(obj_file_list)==0: |
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print("没有obj文件",pid) |
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return |
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texture_path_list = [aa for aa in os.listdir(pid_file_dir) if aa.endswith(".jpg")] |
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if len(texture_path_list)==0: |
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print("没有贴图文件",pid) |
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return |
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texture_file = texture_path_list[0] |
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sorted_obj_file_list = sorted(obj_file_list, key=len) |
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print(sorted_obj_file_list) |
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is_rander = False |
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# 根据obj表渲染图片 |
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for obj_file in sorted_obj_file_list: |
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start = time.time() |
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# 初始化blender环境 |
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bpy.ops.wm.read_homefile() |
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bpy.ops.object.delete(use_global=False, confirm=False) |
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bpy.context.scene.unit_settings.scale_length = 1 |
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bpy.context.scene.unit_settings.length_unit = 'CENTIMETERS' |
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bpy.context.scene.unit_settings.mass_unit = 'GRAMS' |
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# bpy.context.scene.render.engine = 'CYCLES' |
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bpy.context.scene.render.engine = 'BLENDER_EEVEE' # ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES') |
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bpy.context.scene.cycles.feature_set = 'SUPPORTED' |
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bpy.context.scene.cycles.device = 'GPU' |
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bpy.context.scene.cycles.preview_samples = args.sample |
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bpy.context.scene.cycles.samples = args.sample |
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bpy.context.scene.render.film_transparent = True |
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bpy.context.scene.cycles.use_progressive_refining = True # 启用渐进式渲染 |
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# 导入模型文件 |
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print(f'{time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())}: import pid: {pid}') |
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start_import = time.time() |
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print("===============", os.path.join(args.input, pid, obj_file)) |
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with stdout_redirected(to=os.devnull): |
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bpy.ops.import_scene.obj(filepath=os.path.join(args.input, pid, obj_file)) |
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print( |
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f'{time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())}: import pid: {pid} done in {time.time() - start_import:.2f}s') |
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texture_path = os.path.join(args.input, pid, texture_file) |
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materials = bpy.data.materials |
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for material in materials: |
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# 确保材质使用节点树 |
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if not material.use_nodes: |
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material.use_nodes = True |
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node_tree = material.node_tree |
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texture_node = node_tree.nodes.new(type='ShaderNodeTexImage') |
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texture_node.image = bpy.data.images.load(texture_path) |
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bsdf_node = node_tree.nodes.get('Principled BSDF') |
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if bsdf_node: |
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node_tree.links.new(bsdf_node.inputs['Base Color'], texture_node.outputs['Color']) |
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# 获取模型对象 |
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imported_object = bpy.context.selected_objects[0] # 假设只导入一个模型 |
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# 获取模型的边界框（bounding box） |
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bbox_corners = [imported_object.matrix_world @ Vector(corner) for corner in imported_object.bound_box] |
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# 获取最高点的 Y 值 |
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model_height = max(corner.z for corner in bbox_corners) |
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print(f"The highest Y value of the model is: {model_height}") |
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# 获取相机的位置 (假设相机位置参数已经在args中设定) |
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camera_position = args.cameras['1']['position'] |
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# 计算缩放比例：假设模型的高度是1米（或者你可以通过其他方法计算模型的实际高度） |
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# model_height =25 # 这是一个假设值，实际应用中需要根据模型大小来设定 |
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scale_factor = camera_position / model_height # 根据相机高度和模型高度计算缩放比例 |
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bpy.context.view_layer.objects.active = imported_object |
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imported_object.select_set(True) |
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bpy.ops.object.align(align_mode='OPT_1', relative_to='OPT_1', align_axis={'Z'}) |
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bpy.ops.object.transform_apply(location=True, rotation=True, scale=True) |
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# 设置模型的缩放 |
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imported_object.scale = (scale_factor, scale_factor, scale_factor) |
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bpy.context.object.rotation_euler = (0, 0, 0) |
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bpy.ops.object.origin_set(type='ORIGIN_CENTER_OF_VOLUME', center='MEDIAN') |
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bpy.context.object.location[0] = 0 # 移动到特定位置(1m, 1m)是为了让human3d可以正确识别 |
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bpy.context.object.location[1] = 0 |
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bpy.ops.object.transform_apply(location=True, rotation=True, scale=True) |
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# 设置环境光 |
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bpy.data.scenes['Scene'].world.use_nodes = True |
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enode = bpy.data.scenes['Scene'].world.node_tree.nodes.new("ShaderNodeTexEnvironment") |
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enode.image = bpy.data.images.load(args.Env_Texture_path) |
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bpy.data.scenes['Scene'].world.node_tree.links.new(enode.outputs['Color'], |
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bpy.data.scenes['Scene'].world.node_tree.nodes[ |
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'Background'].inputs['Color']) |
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# 初始化相机 |
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camera_lens = 9 # 相机焦距，调整焦距改变镜头广角角度覆盖视野范围 |
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camera_sensor_width = 25.4 # 相机传感器宽度 1英寸 = 25.4mm |
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camera_angle = 2 * math.pi / args.ps_column # 每台相机的圆心角（弧度） |
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# 初始相机参数 |
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bpy.ops.object.select_all(action='DESELECT') |
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obj_camera = bpy.data.objects['Camera'] |
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bpy.context.view_layer.objects.active = obj_camera |
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obj_camera.select_set(True) |
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bpy.context.object.data.type = args.len_type |
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bpy.context.object.data.sensor_width = camera_sensor_width |
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bpy.context.scene.render.resolution_x = args.resolution_x |
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bpy.context.scene.render.resolution_y = args.resolution_y |
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obj_camera.name = 'camera_init' |
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# 计算并输出所有相机的坐标和旋转 |
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for column in tqdm(range(args.ps_column)): |
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theta = (column + 1) * camera_angle |
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x = args.ps_radius * math.sin(theta) |
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y = args.ps_radius * math.cos(theta) |
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for camera_id, camera_info in args.cameras.items(): |
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x_ble = x |
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y_ble = y |
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z_ble = camera_info['position'] |
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camera_copy_name = obj_camera.copy() |
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camera_copy_name.data = obj_camera.data.copy() |
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bpy.context.collection.objects.link(camera_copy_name) |
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bpy.ops.object.select_all(action='DESELECT') |
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bpy.context.view_layer.objects.active = camera_copy_name |
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camera_copy_name.select_set(True) |
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camera_copy_name.name = f"camera_{column + 1}_{camera_id}" |
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if args.len_type == 'PERSP': |
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bpy.context.object.data.lens = camera_info['PERSP_lens'] |
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elif args.len_type == 'ORTHO': |
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bpy.context.object.data.ortho_scale = camera_info['ORTHO_scale'] |
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camera_copy_name.location = (x_ble, -y_ble, z_ble) |
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camera_copy_name.rotation_euler = (camera_info['rotation'], 0, theta) |
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# 渲染图片 |
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bpy.context.scene.camera = bpy.data.objects[camera_copy_name.name] # 切换当前相机 |
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os.makedirs(os.path.join(args.output, args.len_type, |
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f'{pid}_{args.resolution_x}x{args.resolution_y}_{args.ps_column}', camera_id), |
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exist_ok=True) |
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png_name = f"{pid}_{column + 1}.png" |
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png_out_path = os.path.join(args.output, args.len_type, |
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f'{pid}_{args.resolution_x}x{args.resolution_y}_{args.ps_column}', |
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camera_id, png_name) |
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bpy.context.scene.render.filepath = png_out_path |
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bpy.context.scene.render.image_settings.file_format = 'PNG' |
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with stdout_redirected(): |
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bpy.ops.render.render(write_still=True) |
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# delete camera_init |
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bpy.data.objects.remove(bpy.context.scene.objects['camera_init']) |
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bpy.data.objects.remove(bpy.context.scene.objects['Light']) |
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bpy.ops.wm.quit_blender() |
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print(f'{time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())}: render pid: {pid} done, time: {time.time() - start:.2f}s') |
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png_image_dir = os.path.join(args.output, args.len_type,f'{pid}_{args.resolution_x}x{args.resolution_y}_{args.ps_column}',"1",) |
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time.sleep(2) |
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if os.path.exists(png_image_dir): |
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if check_image_rander(png_image_dir): |
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break |
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def get_args(): |
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argparser = argparse.ArgumentParser(description='Render the camera in the studio') |
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argparser.add_argument('-pid', '--pid', type=str, default='', help='pid of the model') |
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argparser.add_argument('-i', '--input', type=str, default='/data/datasets_20t/obj_rander/', |
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help='input models path') |
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argparser.add_argument('-o', '--output', type=str, default='/data/datasets_20t/render_images/', |
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help='output images path') |
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argparser.add_argument('-l', '--limit', type=int, default=0, help='render limit') |
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argparser.add_argument('-ps_radius', '--ps_radius', type=float, default='18', help='photo studio radius, unit: m') |
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argparser.add_argument('-ps_column', '--ps_column', type=int, default='1', help='photo studio column number') |
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argparser.add_argument('-t', '--len_type', type=str, default='ORTHO', help='camera lens type, PERSP or.ORTHO') |
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argparser.add_argument('-r_x', '--resolution_x', type=int, default='768', help='render resolution_x') |
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argparser.add_argument('-r_y', '--resolution_y', type=int, default='1024', help='render resolution_y') |
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argparser.add_argument('-s', '--sample', type=int, default='512', help='render sample') |
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argparser.add_argument('-e', '--Env_Texture_path', type=str, default='./hdrs/studio_small_08_2k.exr', |
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help='enviroment texture path') |
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args = argparser.parse_args() |
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# 相机参数，人体按2米身高，多人拍照覆盖设置 |
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args.cameras = { |
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'1': { |
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'position': 2.5, # 1号相机的位置 0.9米高 |
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'rotation': math.radians(86), |
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'PERSP_lens': 8, |
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'ORTHO_scale': 3.0, |
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}, |
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# '2': { |
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# 'position': 1.8 , # 2号相机的位置 1.8米高 |
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# 'rotation': math.radians(58), |
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# 'PERSP_lens': 13, |
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# 'ORTHO_scale': 2.5, |
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# } |
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} |
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return args |
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def rander_image_run(args): |
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"""主流程""" |
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if not os.path.exists(args.output): |
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os.makedirs(args.output) |
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try: |
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rander_image_and_check(args) |
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except : |
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print("图片渲染错误",args.pid) |
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if __name__ == '__main__': |
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args = get_args() |
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rander_image_run(args) |
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#python image_rander_small.py -pid 234161 -i /data/datasets_20t/obj_rander/ -o /data/datasets_20t/render_images/ |
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# python image_rander_small.py -pid 5924 -i /data/datasets_20t/obj_rander/ -o /data/datasets_20t/render_images/ |
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""" |
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argparser.add_argument('-e', '--Env_Texture_path', type=str, default='/data/datasets/hdrs/studio_small_08_2k.exr', |
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替换成自己的hdrs路径 bpy 3.4.0 |
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"""
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