update api test code

fencerAPI.json

@@ -0,0 +1,215 @@
+{
+  "3": {
+    "inputs": {
+      "seed": 688190695340079,
+      "steps": 3,
+      "cfg": 2,
+      "sampler_name": "dpmpp_sde",
+      "scheduler": "karras",
+      "denoise": 1,
+      "model": [
+        "32",
+        0
+      ],
+      "positive": [
+        "22",
+        0
+      ],
+      "negative": [
+        "22",
+        1
+      ],
+      "latent_image": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "KSampler",
+    "_meta": {
+      "title": "KSampler"
+    }
+  },
+  "4": {
+    "inputs": {
+      "ckpt_name": "dreamshaperXL_sfwLightningDPMSDE.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "Load Checkpoint"
+    }
+  },
+  "5": {
+    "inputs": {
+      "width": 1024,
+      "height": 1024,
+      "batch_size": 1
+    },
+    "class_type": "EmptyLatentImage",
+    "_meta": {
+      "title": "Empty Latent Image"
+    }
+  },
+  "6": {
+    "inputs": {
+      "text": "A fencer in full gear, fencing sword, 1 human, empty background, dark background, dark, empty",
+      "clip": [
+        "4",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Positive)"
+    }
+  },
+  "8": {
+    "inputs": {
+      "samples": [
+        "3",
+        0
+      ],
+      "vae": [
+        "4",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "9": {
+    "inputs": {
+      "filename_prefix": "Result",
+      "images": [
+        "8",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  },
+  "17": {
+    "inputs": {
+      "image": "dance_01.png",
+      "upload": "image"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "Load Image"
+    }
+  },
+  "19": {
+    "inputs": {
+      "control_net_name": "diffusion_pytorch_model.safetensors"
+    },
+    "class_type": "ControlNetLoader",
+    "_meta": {
+      "title": "Load ControlNet Model"
+    }
+  },
+  "22": {
+    "inputs": {
+      "strength": 0.9500000000000001,
+      "start_percent": 0,
+      "end_percent": 1,
+      "positive": [
+        "6",
+        0
+      ],
+      "negative": [
+        "40",
+        0
+      ],
+      "control_net": [
+        "19",
+        0
+      ],
+      "image": [
+        "17",
+        0
+      ],
+      "vae": [
+        "4",
+        2
+      ]
+    },
+    "class_type": "ControlNetApplyAdvanced",
+    "_meta": {
+      "title": "Apply ControlNet"
+    }
+  },
+  "28": {
+    "inputs": {
+      "ipadapter_file": "ip-adapter-plus_sdxl_vit-h.safetensors"
+    },
+    "class_type": "IPAdapterModelLoader",
+    "_meta": {
+      "title": "IPAdapter Model Loader"
+    }
+  },
+  "29": {
+    "inputs": {
+      "image": "ref_black.png",
+      "upload": "image"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "Load Image"
+    }
+  },
+  "31": {
+    "inputs": {
+      "clip_name": "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
+    },
+    "class_type": "CLIPVisionLoader",
+    "_meta": {
+      "title": "Load CLIP Vision"
+    }
+  },
+  "32": {
+    "inputs": {
+      "weight": 1,
+      "weight_type": "style and composition",
+      "combine_embeds": "add",
+      "start_at": 0,
+      "end_at": 1,
+      "embeds_scaling": "V only",
+      "model": [
+        "4",
+        0
+      ],
+      "ipadapter": [
+        "28",
+        0
+      ],
+      "image": [
+        "29",
+        0
+      ],
+      "clip_vision": [
+        "31",
+        0
+      ]
+    },
+    "class_type": "IPAdapterAdvanced",
+    "_meta": {
+      "title": "IPAdapter Advanced"
+    }
+  },
+  "40": {
+    "inputs": {
+      "text": "blurry, drawing, horror, distorted, malformed, naked, cartoon, anime, out of focus, dull, muted colors, boring pose, no action, distracting background, colorful, (face:5.0), bad hand, bad anatomy, worst quality, ai generated images, low quality, average quality, smoke, background, three arms, three hands, white light, (light:5.0), (shadow:5.0), (floor:5.0)\n\nembedding:ac_neg1, embedding:ac_neg2, embedding:badhandv4, embedding:DeepNegative_xl_v1, embedding:NEGATIVE_HANDS, embedding:negativeXL_D, embedding:unaestheticXL_cbp62 -neg, embedding:verybadimagenegative_v1.3, embedding:ziprealism_neg, ",
+      "clip": [
+        "4",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Negative)"
+    }
+  }
+}

fencer_test.py

@@ -0,0 +1,49 @@
+import json
+import urllib
+from urllib import request, parse
+import random
+from requests_toolbelt.multipart.encoder import MultipartEncoder
+
+server_address = "http://127.0.0.1:8188"
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt}
+    data = json.dumps(p).encode('utf-8')
+    req =  request.Request(server_address + "/prompt", data=data)
+    request.urlopen(req)
+
+def upload_image(input_path, name, server_address, image_type="input", overwrite=False):
+  with open(input_path, 'rb') as file:
+    multipart_data = MultipartEncoder(
+      fields= {
+        'image': (name, file, 'image/png'),
+        'type': image_type,
+        'overwrite': str(overwrite).lower()
+      }
+    )
+
+    data = multipart_data
+    headers = { 'Content-Type': multipart_data.content_type }
+    request = urllib.request.Request("http://{}/upload/image".format(server_address), data=data, headers=headers)
+    with urllib.request.urlopen(request) as response:
+      return response.read()
+
+
+def gen_image(openpose_image_path):
+
+    # read fencerAPI.json into prompt_text
+    with open("fencerAPI.json", "r") as f:
+        prompt_json = f.read()
+        prompt = json.loads(prompt_json)
+
+        # upload images        
+        upload_image("ref_black.png", "ref_black.png", server_address)
+        upload_image(openpose_image_path, "openpose_image.png", server_address)
+
+        prompt["3"]["inputs"]["seed"] = random.randint(0, 10000000000)
+        prompt["29"]["inputs"]['image'] = "ref_black.png"
+        prompt["17"]["inputs"]['image'] = "openpose_image.png"
+
+        queue_prompt(prompt)
+
+

openpose_gen.py

@@ -7,6 +7,9 @@ import math
 import cv2
 import skeleton_lib as skel
 import process_json_file as pjf
+import urllib
+from urllib import request, parse
+from requests_toolbelt.multipart.encoder import MultipartEncoder
 import sys
 sys.path.append('./')
 
@@ -93,9 +96,52 @@ def save_bodypose(width: int, height: int, coordinates: list):
     canvas = draw_bodypose(canvas, keypoints, skel.coco_limbSeq, skel.coco_colors)
 
     # Save as body_pose_output0000.png, body_pose_output0001.png, ...
-    cv2.imwrite('output/body_pose_output%04d.png' % save_bodypose.counter, canvas)
+    image_name = 'output/body_pose_output%04d.png' % save_bodypose.counter
+    cv2.imwrite(image_name, canvas)
+    gen_image(image_name)
     save_bodypose.counter += 1  # Increment the counter
 
+server_address = "http://127.0.0.1:8188"
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt}
+    data = json.dumps(p).encode('utf-8')
+    req =  request.Request(server_address + "/prompt", data=data)
+    request.urlopen(req)
+
+def upload_image(input_path, name, server_address, image_type="input", overwrite=False):
+  with open(input_path, 'rb') as file:
+    multipart_data = MultipartEncoder(
+      fields= {
+        'image': (name, file, 'image/png'),
+        'type': image_type,
+        'overwrite': str(overwrite).lower()
+      }
+    )
+
+    data = multipart_data
+    headers = { 'Content-Type': multipart_data.content_type }
+    request = urllib.request.Request("http://{}/upload/image".format(server_address), data=data, headers=headers)
+    with urllib.request.urlopen(request) as response:
+      return response.read()
+
+def gen_image(openpose_image_path):
+
+    # read fencerAPI.json into prompt_text
+    with open("fencerAPI.json", "r") as f:
+        prompt_json = f.read()
+        prompt = json.loads(prompt_json)
+
+        # upload images        
+        upload_image("ref_black.png", "ref_black.png", server_address)
+        upload_image(openpose_image_path, "openpose_image.png", server_address)
+
+        prompt["3"]["inputs"]["seed"] = random.randint(0, 10000000000)
+        prompt["29"]["inputs"]['image'] = "ref_black.png"
+        prompt["17"]["inputs"]['image'] = "openpose_image.png"
+
+        queue_prompt(prompt)
+
 def main():
     directory = './fixed'
     json_files = [f for f in os.listdir(directory) if f.endswith('.json')]