diff --git a/.gitattributes b/.gitattributes index f6b1f326ca4ab7cf0c8798856f8fe0020ff82d58..2ac01179d2d3e28b07b3997d1306ce1a1400bc37 100644 --- a/.gitattributes +++ b/.gitattributes @@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text *.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text diff --git a/app.py b/app.py index 6a77b0d19518d4a1a4fe537740138d2c3a13d0ac..3584d2d8e82d21adebe5842d834076e9a1a74eb9 100644 --- a/app.py +++ b/app.py @@ -7,16 +7,16 @@ import random # 图像增强 ENHANCE_INPUT_DIR = "sr_input" ENHANCE_OUTPUT_DIR = "sr_output" -ENHANCE_IMG_NAMES = [f"{i}.png" for i in range(1, 30)] +ENHANCE_IMG_NAMES = [f"img_{i}.jpg" for i in range(1, 30)] # 分割 SEG_INPUT_DIR = "seg_input" SEG_OUTPUT_DIR = "seg_output" -SEG_IMG_NAMES = [f"{i}.png" for i in range(1, 30)] +SEG_IMG_NAMES = [f"img_{i}.png" for i in range(1, 30)] # 诊断 DIAG_INPUT_DIR = "diag_input" -DIAG_IMG_NAMES = [f"{i}.png" for i in range(1, 30)] +DIAG_IMG_NAMES = [f"{i}.png" for i in range(1, 32)] # Kvasir 类别 KVASIR_CLASSES = [ @@ -28,7 +28,9 @@ KVASIR_CLASSES = [ "糜烂 (dyed-lifted-polyps)", "出血 (dyed-resection-margins)", "炎症 (bbps-0-1)", - "直肠癌 (bbps-2-3)" + "直肠癌 (bbps-2-3)", + "normal-cecum", + "normal-pylorus" ] # ========== 功能函数 ========== @@ -59,9 +61,46 @@ def show_diag_input(img_name): return open_img(path) def diagnose_demo(img_name): - pred = random.choice(KVASIR_CLASSES) - conf = round(random.uniform(0.85, 0.99), 2) - return f"诊断结果：{pred}\n置信度：{conf}" + idx = int(os.path.splitext(img_name)[0]) + # 区间与类别映射（1-based index） + idx2cat = [ + (range(1, 5), "糜烂 (dyed-lifted-polyps)"), + (range(5, 9), "出血 (dyed-resection-margins)"), + (range(9, 13), "食管静脉曲张 (esophagitis)"), + (range(13, 17), "normal-cecum"), + (range(17, 21), "normal-pylorus"), + (range(21, 25), "正常粘膜 (normal-z-line)"), + (range(25, 29), "息肉 (polyps)"), + (range(29, 33), "溃疡 (ulcerative-colitis)") + ] + main_cat = None + for r, cat in idx2cat: + if idx in r: + main_cat = cat + break + # 若没落在区间，随机主类别 + if main_cat is None: + main_cat = random.choice(KVASIR_CLASSES) + # 概率分配 + n_cat = len(KVASIR_CLASSES) + probs = [0.0 for _ in range(n_cat)] + main_idx = KVASIR_CLASSES.index(main_cat) + main_prob = random.uniform(0.6, 0.8) + rest = 1 - main_prob + # 其余类别分配小随机 + other_probs_raw = [random.uniform(0.01, 1) for _ in range(n_cat-1)] + other_probs_norm = [x / sum(other_probs_raw) * rest for x in other_probs_raw] + pidx = 0 + for i in range(n_cat): + if i == main_idx: + probs[i] = main_prob + else: + probs[i] = other_probs_norm[pidx] + pidx += 1 + # 最终输出 + result_table = [[KVASIR_CLASSES[i], f"{probs[i]:.4f}"] for i in range(n_cat)] + result_text = f"诊断类别：{main_cat}（概率最大）" + return result_table, result_text # ========== Gradio 布局 ========== with gr.Blocks(title="AI内窥镜影像辅助分析系统") as demo: @@ -77,9 +116,7 @@ with gr.Blocks(title="AI内窥镜影像辅助分析系统") as demo: enhance_output_img = gr.Image(label="增强结果图片", interactive=False) enhance_btn = gr.Button("增强图片") - # 选择图片自动显示原图 enhance_select.change(fn=show_enhance_input, inputs=enhance_select, outputs=enhance_input_img) - # 按钮显示增强结果 enhance_btn.click(fn=enhance_demo, inputs=enhance_select, outputs=enhance_output_img) # 下方：左右两大模块 @@ -101,9 +138,10 @@ with gr.Blocks(title="AI内窥镜影像辅助分析系统") as demo: diag_select = gr.Dropdown(choices=DIAG_IMG_NAMES, value=DIAG_IMG_NAMES[0], label="选择样例图片", filterable=True) diag_input_img = gr.Image(label="输入图片", interactive=False) diag_btn = gr.Button("开始诊断") + diag_table = gr.Dataframe(headers=["类别", "概率"]) diag_result = gr.Textbox(label="诊断结果") diag_select.change(fn=show_diag_input, inputs=diag_select, outputs=diag_input_img) - diag_btn.click(fn=diagnose_demo, inputs=diag_select, outputs=diag_result) + diag_btn.click(fn=diagnose_demo, inputs=diag_select, outputs=[diag_table, diag_result]) demo.launch() diff --git a/diag_input/img_1.jpg b/diag_input/img_1.jpg new file 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