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| # Copied megadetector section from https://huggingface.co/spaces/hlydecker/MegaDetector_v5 | |
| # Copied from https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels/blob/main/app.py | |
| print("before import") | |
| import gradio as gr | |
| import json | |
| import os | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from PIL import Image | |
| from dlclive import DLCLive, Processor | |
| from numpy import savetxt | |
| import PIL | |
| print("after import") | |
| # A method that allows using dlc live, but with different models, saves poses, and plots the poses onto image | |
| def dlclive_pose(model, crop_np, crop, index,dlc_proc): | |
| dlc_live = DLCLive(model, processor=dlc_proc) | |
| dlc_live.init_inference(crop_np) | |
| keypts = dlc_live.get_pose(crop_np) | |
| xpose = [] | |
| ypose = [] | |
| for key in keypts[:,2]: | |
| # if key > 0.05: # which value do we need here? | |
| i = np.where(keypts[:,2]==key) | |
| xpose.append(keypts[i,0]) | |
| ypose.append(keypts[i,1]) | |
| plt.imshow(crop) | |
| plt.scatter(xpose[:], ypose[:], 40, color='cyan') | |
| canvas = plt.gca().figure.canvas | |
| canvas.draw() | |
| image = PIL.Image.frombytes('RGB', canvas.get_width_height(), canvas.tostring_rgb()) | |
| plt.clf() | |
| return image | |
| def classify_image(img, file): | |
| primate_face_model = 'model_weights/DLC_FacialLandmarks_resnet_50_iteration-1_shuffle-1' | |
| human_model = 'model_weights/DLC_human_dancing_resnet_101_iteration-0_shuffle-1' | |
| with open(file.name, 'r') as f: | |
| detection_results = json.load(f) | |
| dlc_proc = Processor() | |
| # Assuming there is only 1 detection on the output | |
| img_data = detection_results["images"][0] | |
| output_images = [] | |
| for detections_dict in img_data["detections"]: | |
| index = img_data["detections"].index(detections_dict) | |
| if detections_dict["conf"] > 0.8: | |
| x1, y1,w_box, h_box = detections_dict["bbox"] | |
| ymin,xmin,ymax, xmax = y1, x1, y1 + h_box, x1 + w_box | |
| imageWidth=img.size[0] | |
| imageHeight= img.size[1] | |
| area = (xmin * imageWidth, ymin * imageHeight, xmax * imageWidth, | |
| ymax * imageHeight) | |
| crop = img.crop(area) | |
| crop_np = np.asarray(crop) | |
| if detections_dict["category"] == "1": | |
| selected_model = primate_face_model | |
| elif detections_dict["category"] == "2": | |
| selected_model = human_model | |
| # Until we know how to dynamically add output element to gradio, just return the first image | |
| output_images.append(dlclive_pose(selected_model, crop_np, crop, index, dlc_proc)) | |
| return output_images[0], output_images[1] # lol | |
| input_image = gr.inputs.Image(type="pil", label="Input Image") | |
| input_file = gr.inputs.File(label="output.json") | |
| # Fake it till we make it, we know our example has 2 outputs | |
| outputs = [gr.outputs.Image(type="pil", label="Output Image"), gr.outputs.Image(type="pil", label="Output Image")] | |
| gr.Interface(fn=classify_image, inputs=[input_image, input_file], outputs=outputs, theme="huggingface").launch() | |